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+## Context
+
+When building MCP tools that manipulate pandas DataFrames, operations that subset or filter data can unexpectedly add columns to the result.
+
+**Issue:** #203 - filter tool adds unexpected `__index_level_0__` column
+
+## Problem
+
+The `filter` tool used `df.query(condition)` to filter rows. This preserves the original DataFrame's index. When the filtered result was later serialized or stored, pandas converted the preserved index into a column named `__index_level_0__`.
+
+```python
+# Before (problematic)
+filtered = df.query(condition)
+# Result has original index preserved, becomes column on storage
+```
+
+**Symptom:** Users reported filtered DataFrames having 5 columns when the source had 4.
+
+## Solution
+
+Always reset the index after filtering operations that subset rows:
+
+```python
+# After (correct)
+filtered = df.query(condition).reset_index(drop=True)
+```
+
+The `drop=True` parameter discards the old index entirely rather than converting it to a column.
+
+## Prevention
+
+When implementing pandas operations in MCP tools:
+
+1. **Filter/query operations** - Always add `.reset_index(drop=True)`
+2. **Groupby operations** - Use `.reset_index()` (already correct in our impl)
+3. **Merge/join operations** - `pd.merge()` handles this automatically
+4. **Slicing operations** (head, tail) - Consider if index reset is needed
+
+**Rule of thumb:** If an operation changes which rows are in the DataFrame, reset the index.
+
+## Related
+
+- File: `mcp-servers/data-platform/mcp_server/pandas_tools.py`
+- Fix commit: `4ed3ed7`
+
+
+---
+**Tags:** pandas, data-platform, mcp-server, python, dataframe
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