adding mcp tools

.claude/tools/agent_cache_wrapper.py

@@ -0,0 +1,115 @@
+# .claude/tools/agent_cache_wrapper.py
+"""
+Cache wrapper for AI agents
+Use this in your agent workflows to add caching
+"""
+
+import os
+import sys
+from pathlib import Path
+
+# Add the tools directory to Python path
+tools_dir = Path(__file__).parent
+sys.path.insert(0, str(tools_dir))
+
+from local_cache_client import (
+    get_cache,
+    cached_ai_query,
+    store_ai_response,
+    print_cache_stats,
+)
+
+
+class AgentCacheWrapper:
+    """Wrapper for agent AI calls with caching support."""
+
+    def __init__(self, agent_type: str, project: str = None):  # type: ignore
+        self.agent_type = agent_type
+        self.project = project or os.getenv("AI_CACHE_PROJECT", "job_forge")
+        self.cache = get_cache()
+
+        print(f"🤖 {agent_type.title()} Agent initialized with caching")
+
+    def query_with_cache(self, prompt: str, make_ai_call_func=None) -> str:
+        """
+        Query with cache support.
+
+        Args:
+            prompt: The prompt to send
+            make_ai_call_func: Function to call if cache miss (should return AI response)
+
+        Returns:
+            AI response (from cache or fresh API call)
+        """
+        # Try cache first
+        cached_response, was_hit = cached_ai_query(
+            prompt, self.agent_type, self.project
+        )
+
+        if was_hit:
+            return cached_response  # type: ignore
+
+        # Cache miss - make AI call
+        if make_ai_call_func:
+            print(f"🤖 Making fresh AI call for {self.agent_type}...")
+            ai_response = make_ai_call_func(prompt)
+
+            # Store in cache for next time
+            if ai_response:
+                store_ai_response(prompt, ai_response, self.agent_type, self.project)
+
+            return ai_response
+        else:
+            print(f"⚠️ No AI call function provided for cache miss")
+            return None  # type: ignore
+
+    def store_response(self, prompt: str, response: str):
+        """Manually store a response in cache."""
+        store_ai_response(prompt, response, self.agent_type, self.project)
+
+    def get_stats(self):
+        """Get cache statistics for this session."""
+        return self.cache.get_stats()
+
+
+# Convenience functions for each agent type
+def technical_lead_query(prompt: str, ai_call_func=None) -> str:
+    """Technical Lead agent with caching."""
+    wrapper = AgentCacheWrapper("technical_lead")
+    return wrapper.query_with_cache(prompt, ai_call_func)
+
+
+def qa_engineer_query(prompt: str, ai_call_func=None) -> str:
+    """QA Engineer agent with caching."""
+    wrapper = AgentCacheWrapper("qa_engineer")
+    return wrapper.query_with_cache(prompt, ai_call_func)
+
+
+def devops_engineer_query(prompt: str, ai_call_func=None) -> str:
+    """DevOps Engineer agent with caching."""
+    wrapper = AgentCacheWrapper("devops_engineer")
+    return wrapper.query_with_cache(prompt, ai_call_func)
+
+
+def fullstack_developer_query(prompt: str, ai_call_func=None) -> str:
+    """Full-Stack Developer agent with caching."""
+    wrapper = AgentCacheWrapper("fullstack_developer")
+    return wrapper.query_with_cache(prompt, ai_call_func)
+
+
+# Example usage and testing
+if __name__ == "__main__":
+    # Example AI call function (replace with your actual Claude Code integration)
+    def example_ai_call(prompt):
+        # This is where you'd call Claude Code or your AI service
+        # For testing, return a mock response
+        return f"Mock AI response for: {prompt[:50]}..."
+
+    # Test with Technical Lead
+    response = technical_lead_query(
+        "What is the current FastAPI project structure?", example_ai_call
+    )
+    print(f"Response: {response}")
+
+    # Print stats
+    print_cache_stats()