lmiranda
89f0354ccc
Add new data-platform plugin for data engineering workflows with: MCP Server (32 tools): - pandas operations (14 tools): read_csv, read_parquet, read_json, to_csv, to_parquet, describe, head, tail, filter, select, groupby, join, list_data, drop_data - PostgreSQL/PostGIS (10 tools): pg_connect, pg_query, pg_execute, pg_tables, pg_columns, pg_schemas, st_tables, st_geometry_type, st_srid, st_extent - dbt integration (8 tools): dbt_parse, dbt_run, dbt_test, dbt_build, dbt_compile, dbt_ls, dbt_docs_generate, dbt_lineage Plugin Features: - Arrow IPC data_ref system for DataFrame persistence across tool calls - Pre-execution validation for dbt with `dbt parse` - SessionStart hook for PostgreSQL connectivity check (non-blocking) - Hybrid configuration (system ~/.config/claude/postgres.env + project .env) - Memory management with 100k row limit and chunking support Commands: /initial-setup, /ingest, /profile, /schema, /explain, /lineage, /run Agents: data-ingestion, data-analysis Test suite: 71 tests covering config, data store, pandas, postgres, dbt tools Addresses data workflow issues from personal-portfolio project: - Lost data after multiple interactions (solved by Arrow IPC data_ref) - dbt 1.9+ syntax deprecation (solved by pre-execution validation) - Ungraceful PostgreSQL error handling (solved by SessionStart hook) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
796 lines
33 KiB
Python
796 lines
33 KiB
Python
"""
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MCP Server entry point for Data Platform integration.
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Provides pandas, PostgreSQL/PostGIS, and dbt tools to Claude Code via JSON-RPC 2.0 over stdio.
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"""
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import asyncio
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import logging
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import json
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from mcp.server import Server
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from mcp.server.stdio import stdio_server
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from mcp.types import Tool, TextContent
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from .config import DataPlatformConfig
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from .data_store import DataStore
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from .pandas_tools import PandasTools
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from .postgres_tools import PostgresTools
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from .dbt_tools import DbtTools
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# Suppress noisy MCP validation warnings on stderr
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logging.basicConfig(level=logging.INFO)
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logging.getLogger("root").setLevel(logging.ERROR)
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logging.getLogger("mcp").setLevel(logging.ERROR)
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logger = logging.getLogger(__name__)
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class DataPlatformMCPServer:
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"""MCP Server for data platform integration"""
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def __init__(self):
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self.server = Server("data-platform-mcp")
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self.config = None
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self.pandas_tools = None
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self.postgres_tools = None
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self.dbt_tools = None
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async def initialize(self):
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"""Initialize server and load configuration."""
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try:
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config_loader = DataPlatformConfig()
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self.config = config_loader.load()
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self.pandas_tools = PandasTools()
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self.postgres_tools = PostgresTools()
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self.dbt_tools = DbtTools()
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# Log available capabilities
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caps = []
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caps.append("pandas")
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if self.config.get('postgres_available'):
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caps.append("PostgreSQL")
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if self.config.get('dbt_available'):
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caps.append("dbt")
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logger.info(f"Data Platform MCP Server initialized with: {', '.join(caps)}")
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except Exception as e:
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logger.error(f"Failed to initialize: {e}")
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raise
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def setup_tools(self):
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"""Register all available tools with the MCP server"""
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@self.server.list_tools()
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async def list_tools() -> list[Tool]:
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"""Return list of available tools"""
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tools = [
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# pandas tools - always available
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Tool(
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name="read_csv",
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description="Load CSV file into DataFrame",
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inputSchema={
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"type": "object",
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"properties": {
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"file_path": {
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"type": "string",
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"description": "Path to CSV file"
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},
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"name": {
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"type": "string",
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"description": "Optional name for data_ref"
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},
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"chunk_size": {
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"type": "integer",
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"description": "Process in chunks of this size"
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}
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},
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"required": ["file_path"]
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}
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),
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Tool(
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name="read_parquet",
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description="Load Parquet file into DataFrame",
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inputSchema={
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"type": "object",
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"properties": {
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"file_path": {
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"type": "string",
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"description": "Path to Parquet file"
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},
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"name": {
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"type": "string",
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"description": "Optional name for data_ref"
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},
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"columns": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Optional list of columns to load"
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}
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},
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"required": ["file_path"]
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}
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),
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Tool(
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name="read_json",
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description="Load JSON/JSONL file into DataFrame",
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inputSchema={
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"type": "object",
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"properties": {
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"file_path": {
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"type": "string",
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"description": "Path to JSON file"
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},
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"name": {
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"type": "string",
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"description": "Optional name for data_ref"
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},
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"lines": {
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"type": "boolean",
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"default": False,
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"description": "Read as JSON Lines format"
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}
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},
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"required": ["file_path"]
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}
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),
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Tool(
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name="to_csv",
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description="Export DataFrame to CSV file",
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inputSchema={
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"type": "object",
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"properties": {
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"data_ref": {
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"type": "string",
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"description": "Reference to stored DataFrame"
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},
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"file_path": {
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"type": "string",
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"description": "Output file path"
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},
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"index": {
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"type": "boolean",
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"default": False,
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"description": "Include index column"
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}
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},
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"required": ["data_ref", "file_path"]
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}
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),
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Tool(
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name="to_parquet",
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description="Export DataFrame to Parquet file",
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inputSchema={
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"type": "object",
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"properties": {
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"data_ref": {
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"type": "string",
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"description": "Reference to stored DataFrame"
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},
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"file_path": {
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"type": "string",
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"description": "Output file path"
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},
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"compression": {
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"type": "string",
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"default": "snappy",
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"description": "Compression codec"
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}
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},
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"required": ["data_ref", "file_path"]
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}
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),
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Tool(
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name="describe",
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description="Get statistical summary of DataFrame",
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inputSchema={
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"type": "object",
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"properties": {
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"data_ref": {
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"type": "string",
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"description": "Reference to stored DataFrame"
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}
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},
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"required": ["data_ref"]
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}
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),
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Tool(
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name="head",
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description="Get first N rows of DataFrame",
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inputSchema={
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"type": "object",
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"properties": {
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"data_ref": {
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"type": "string",
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"description": "Reference to stored DataFrame"
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},
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"n": {
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"type": "integer",
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"default": 10,
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"description": "Number of rows"
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}
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},
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"required": ["data_ref"]
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}
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),
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Tool(
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name="tail",
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description="Get last N rows of DataFrame",
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inputSchema={
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"type": "object",
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"properties": {
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"data_ref": {
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"type": "string",
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"description": "Reference to stored DataFrame"
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},
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"n": {
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"type": "integer",
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"default": 10,
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"description": "Number of rows"
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}
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},
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"required": ["data_ref"]
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}
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),
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Tool(
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name="filter",
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description="Filter DataFrame rows by condition",
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inputSchema={
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"type": "object",
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"properties": {
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"data_ref": {
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"type": "string",
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"description": "Reference to stored DataFrame"
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},
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"condition": {
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"type": "string",
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"description": "pandas query string (e.g., 'age > 30 and city == \"NYC\"')"
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},
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"name": {
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"type": "string",
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"description": "Optional name for result data_ref"
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}
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},
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"required": ["data_ref", "condition"]
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}
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),
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Tool(
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name="select",
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description="Select specific columns from DataFrame",
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inputSchema={
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"type": "object",
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"properties": {
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"data_ref": {
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"type": "string",
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"description": "Reference to stored DataFrame"
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},
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"columns": {
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"type": "array",
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"items": {"type": "string"},
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"description": "List of column names to select"
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},
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"name": {
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"type": "string",
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"description": "Optional name for result data_ref"
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}
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},
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"required": ["data_ref", "columns"]
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}
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),
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Tool(
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name="groupby",
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description="Group DataFrame and aggregate",
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inputSchema={
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"type": "object",
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"properties": {
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"data_ref": {
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"type": "string",
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"description": "Reference to stored DataFrame"
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},
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"by": {
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"oneOf": [
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{"type": "string"},
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{"type": "array", "items": {"type": "string"}}
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],
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"description": "Column(s) to group by"
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},
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"agg": {
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"type": "object",
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"description": "Aggregation dict (e.g., {\"sales\": \"sum\", \"count\": \"mean\"})"
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},
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"name": {
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"type": "string",
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"description": "Optional name for result data_ref"
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}
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},
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"required": ["data_ref", "by", "agg"]
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}
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),
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Tool(
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name="join",
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description="Join two DataFrames",
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inputSchema={
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"type": "object",
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"properties": {
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"left_ref": {
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"type": "string",
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"description": "Reference to left DataFrame"
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},
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"right_ref": {
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"type": "string",
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"description": "Reference to right DataFrame"
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},
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"on": {
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"oneOf": [
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{"type": "string"},
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{"type": "array", "items": {"type": "string"}}
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],
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"description": "Column(s) to join on (if same name in both)"
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},
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"left_on": {
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"oneOf": [
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{"type": "string"},
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{"type": "array", "items": {"type": "string"}}
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],
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"description": "Left join column(s)"
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},
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"right_on": {
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"oneOf": [
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{"type": "string"},
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{"type": "array", "items": {"type": "string"}}
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],
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"description": "Right join column(s)"
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},
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"how": {
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"type": "string",
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"enum": ["inner", "left", "right", "outer"],
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"default": "inner",
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"description": "Join type"
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},
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"name": {
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"type": "string",
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"description": "Optional name for result data_ref"
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}
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},
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"required": ["left_ref", "right_ref"]
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}
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),
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Tool(
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name="list_data",
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description="List all stored DataFrames",
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inputSchema={
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"type": "object",
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"properties": {}
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}
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),
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Tool(
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name="drop_data",
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description="Remove a DataFrame from storage",
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inputSchema={
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"type": "object",
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"properties": {
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"data_ref": {
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"type": "string",
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"description": "Reference to drop"
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}
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},
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"required": ["data_ref"]
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}
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),
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# PostgreSQL tools
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Tool(
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name="pg_connect",
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description="Test PostgreSQL connection and return status",
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inputSchema={
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"type": "object",
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"properties": {}
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}
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),
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Tool(
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name="pg_query",
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description="Execute SELECT query and return results as data_ref",
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inputSchema={
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "SQL SELECT query"
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},
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"params": {
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"type": "array",
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"items": {},
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"description": "Query parameters (use $1, $2, etc.)"
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},
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"name": {
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"type": "string",
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"description": "Optional name for result data_ref"
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}
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},
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"required": ["query"]
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}
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),
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Tool(
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name="pg_execute",
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description="Execute INSERT/UPDATE/DELETE query",
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inputSchema={
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "SQL DML query"
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},
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"params": {
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"type": "array",
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"items": {},
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"description": "Query parameters"
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}
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},
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"required": ["query"]
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}
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),
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Tool(
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name="pg_tables",
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description="List all tables in schema",
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inputSchema={
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"type": "object",
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"properties": {
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"schema": {
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"type": "string",
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"default": "public",
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"description": "Schema name"
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}
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}
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}
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),
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Tool(
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name="pg_columns",
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description="Get column information for a table",
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inputSchema={
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"type": "object",
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"properties": {
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"table": {
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"type": "string",
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"description": "Table name"
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},
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"schema": {
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"type": "string",
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"default": "public",
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"description": "Schema name"
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}
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},
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"required": ["table"]
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}
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),
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Tool(
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name="pg_schemas",
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description="List all schemas in database",
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inputSchema={
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"type": "object",
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"properties": {}
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}
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),
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# PostGIS tools
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Tool(
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name="st_tables",
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description="List PostGIS-enabled tables",
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inputSchema={
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"type": "object",
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"properties": {
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"schema": {
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"type": "string",
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"default": "public",
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"description": "Schema name"
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}
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}
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}
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),
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Tool(
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name="st_geometry_type",
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description="Get geometry type of a column",
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inputSchema={
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"type": "object",
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"properties": {
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"table": {
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"type": "string",
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"description": "Table name"
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},
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"column": {
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"type": "string",
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"description": "Geometry column name"
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},
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"schema": {
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"type": "string",
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"default": "public",
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"description": "Schema name"
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}
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},
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"required": ["table", "column"]
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}
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),
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Tool(
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name="st_srid",
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description="Get SRID of geometry column",
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inputSchema={
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"type": "object",
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"properties": {
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"table": {
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"type": "string",
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"description": "Table name"
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},
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"column": {
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"type": "string",
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"description": "Geometry column name"
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},
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"schema": {
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"type": "string",
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"default": "public",
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"description": "Schema name"
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}
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},
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"required": ["table", "column"]
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}
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),
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Tool(
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name="st_extent",
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description="Get bounding box of all geometries",
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inputSchema={
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"type": "object",
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"properties": {
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"table": {
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"type": "string",
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"description": "Table name"
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},
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"column": {
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"type": "string",
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"description": "Geometry column name"
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},
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"schema": {
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"type": "string",
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"default": "public",
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"description": "Schema name"
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}
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},
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"required": ["table", "column"]
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}
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),
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# dbt tools
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Tool(
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name="dbt_parse",
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description="Validate dbt project (pre-flight check)",
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inputSchema={
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"type": "object",
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"properties": {}
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}
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),
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Tool(
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name="dbt_run",
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description="Run dbt models with pre-validation",
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inputSchema={
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"type": "object",
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"properties": {
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"select": {
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"type": "string",
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"description": "Model selection (e.g., 'model_name', '+model_name', 'tag:daily')"
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},
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"exclude": {
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"type": "string",
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"description": "Models to exclude"
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},
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"full_refresh": {
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"type": "boolean",
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"default": False,
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"description": "Rebuild incremental models"
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}
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}
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}
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),
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Tool(
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name="dbt_test",
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description="Run dbt tests",
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inputSchema={
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"type": "object",
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"properties": {
|
|
"select": {
|
|
"type": "string",
|
|
"description": "Test selection"
|
|
},
|
|
"exclude": {
|
|
"type": "string",
|
|
"description": "Tests to exclude"
|
|
}
|
|
}
|
|
}
|
|
),
|
|
Tool(
|
|
name="dbt_build",
|
|
description="Run dbt build (run + test) with pre-validation",
|
|
inputSchema={
|
|
"type": "object",
|
|
"properties": {
|
|
"select": {
|
|
"type": "string",
|
|
"description": "Model/test selection"
|
|
},
|
|
"exclude": {
|
|
"type": "string",
|
|
"description": "Resources to exclude"
|
|
},
|
|
"full_refresh": {
|
|
"type": "boolean",
|
|
"default": False,
|
|
"description": "Rebuild incremental models"
|
|
}
|
|
}
|
|
}
|
|
),
|
|
Tool(
|
|
name="dbt_compile",
|
|
description="Compile dbt models to SQL without executing",
|
|
inputSchema={
|
|
"type": "object",
|
|
"properties": {
|
|
"select": {
|
|
"type": "string",
|
|
"description": "Model selection"
|
|
}
|
|
}
|
|
}
|
|
),
|
|
Tool(
|
|
name="dbt_ls",
|
|
description="List dbt resources",
|
|
inputSchema={
|
|
"type": "object",
|
|
"properties": {
|
|
"select": {
|
|
"type": "string",
|
|
"description": "Resource selection"
|
|
},
|
|
"resource_type": {
|
|
"type": "string",
|
|
"enum": ["model", "test", "seed", "snapshot", "source"],
|
|
"description": "Filter by type"
|
|
},
|
|
"output": {
|
|
"type": "string",
|
|
"enum": ["name", "path", "json"],
|
|
"default": "name",
|
|
"description": "Output format"
|
|
}
|
|
}
|
|
}
|
|
),
|
|
Tool(
|
|
name="dbt_docs_generate",
|
|
description="Generate dbt documentation",
|
|
inputSchema={
|
|
"type": "object",
|
|
"properties": {}
|
|
}
|
|
),
|
|
Tool(
|
|
name="dbt_lineage",
|
|
description="Get model dependencies and lineage",
|
|
inputSchema={
|
|
"type": "object",
|
|
"properties": {
|
|
"model": {
|
|
"type": "string",
|
|
"description": "Model name to analyze"
|
|
}
|
|
},
|
|
"required": ["model"]
|
|
}
|
|
)
|
|
]
|
|
return tools
|
|
|
|
@self.server.call_tool()
|
|
async def call_tool(name: str, arguments: dict) -> list[TextContent]:
|
|
"""Handle tool invocation."""
|
|
try:
|
|
# Route to appropriate tool handler
|
|
# pandas tools
|
|
if name == "read_csv":
|
|
result = await self.pandas_tools.read_csv(**arguments)
|
|
elif name == "read_parquet":
|
|
result = await self.pandas_tools.read_parquet(**arguments)
|
|
elif name == "read_json":
|
|
result = await self.pandas_tools.read_json(**arguments)
|
|
elif name == "to_csv":
|
|
result = await self.pandas_tools.to_csv(**arguments)
|
|
elif name == "to_parquet":
|
|
result = await self.pandas_tools.to_parquet(**arguments)
|
|
elif name == "describe":
|
|
result = await self.pandas_tools.describe(**arguments)
|
|
elif name == "head":
|
|
result = await self.pandas_tools.head(**arguments)
|
|
elif name == "tail":
|
|
result = await self.pandas_tools.tail(**arguments)
|
|
elif name == "filter":
|
|
result = await self.pandas_tools.filter(**arguments)
|
|
elif name == "select":
|
|
result = await self.pandas_tools.select(**arguments)
|
|
elif name == "groupby":
|
|
result = await self.pandas_tools.groupby(**arguments)
|
|
elif name == "join":
|
|
result = await self.pandas_tools.join(**arguments)
|
|
elif name == "list_data":
|
|
result = await self.pandas_tools.list_data()
|
|
elif name == "drop_data":
|
|
result = await self.pandas_tools.drop_data(**arguments)
|
|
# PostgreSQL tools
|
|
elif name == "pg_connect":
|
|
result = await self.postgres_tools.pg_connect()
|
|
elif name == "pg_query":
|
|
result = await self.postgres_tools.pg_query(**arguments)
|
|
elif name == "pg_execute":
|
|
result = await self.postgres_tools.pg_execute(**arguments)
|
|
elif name == "pg_tables":
|
|
result = await self.postgres_tools.pg_tables(**arguments)
|
|
elif name == "pg_columns":
|
|
result = await self.postgres_tools.pg_columns(**arguments)
|
|
elif name == "pg_schemas":
|
|
result = await self.postgres_tools.pg_schemas()
|
|
# PostGIS tools
|
|
elif name == "st_tables":
|
|
result = await self.postgres_tools.st_tables(**arguments)
|
|
elif name == "st_geometry_type":
|
|
result = await self.postgres_tools.st_geometry_type(**arguments)
|
|
elif name == "st_srid":
|
|
result = await self.postgres_tools.st_srid(**arguments)
|
|
elif name == "st_extent":
|
|
result = await self.postgres_tools.st_extent(**arguments)
|
|
# dbt tools
|
|
elif name == "dbt_parse":
|
|
result = await self.dbt_tools.dbt_parse()
|
|
elif name == "dbt_run":
|
|
result = await self.dbt_tools.dbt_run(**arguments)
|
|
elif name == "dbt_test":
|
|
result = await self.dbt_tools.dbt_test(**arguments)
|
|
elif name == "dbt_build":
|
|
result = await self.dbt_tools.dbt_build(**arguments)
|
|
elif name == "dbt_compile":
|
|
result = await self.dbt_tools.dbt_compile(**arguments)
|
|
elif name == "dbt_ls":
|
|
result = await self.dbt_tools.dbt_ls(**arguments)
|
|
elif name == "dbt_docs_generate":
|
|
result = await self.dbt_tools.dbt_docs_generate()
|
|
elif name == "dbt_lineage":
|
|
result = await self.dbt_tools.dbt_lineage(**arguments)
|
|
else:
|
|
raise ValueError(f"Unknown tool: {name}")
|
|
|
|
return [TextContent(
|
|
type="text",
|
|
text=json.dumps(result, indent=2, default=str)
|
|
)]
|
|
|
|
except Exception as e:
|
|
logger.error(f"Tool {name} failed: {e}")
|
|
return [TextContent(
|
|
type="text",
|
|
text=json.dumps({"error": str(e)}, indent=2)
|
|
)]
|
|
|
|
async def run(self):
|
|
"""Run the MCP server"""
|
|
await self.initialize()
|
|
self.setup_tools()
|
|
|
|
async with stdio_server() as (read_stream, write_stream):
|
|
await self.server.run(
|
|
read_stream,
|
|
write_stream,
|
|
self.server.create_initialization_options()
|
|
)
|
|
|
|
|
|
async def main():
|
|
"""Main entry point"""
|
|
server = DataPlatformMCPServer()
|
|
await server.run()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main())
|