feat: add LLM-based query router for RAG, MCP search, and MCP analysis

[GovindhKishore]

Summary

Adds LLM-based query routing to ReactToMeGraphBuilder so questions are directed to the correct retrieval path before any tool calls are made.

Closes #141

Context

PR #127 and #137 introduced MCP tools into the agent. Without routing, every question goes through the tool calling loop even when the vector database already has the answer. This PR adds a lightweight classification step that runs before retrieval and directs each question to the right path.

What Changed

src/mcp/query_router.py (new)

• create_query_router(llm) returns an async route() function
• Classifies questions into rag, mcp_search, or mcp_analysis
• Uses a structured prompt with explicit criteria for each category
• Falls back to rag on unexpected output
• Intended to be called with a lightweight model (gpt-4o-mini)

src/agent/profiles/react_to_me.py (modified)

• Two route-specific LLM instances built at init time - llm_with_search_tools
  and llm_with_analysis_tools - each bound to the relevant tool subset only
• call_model now calls the router first and branches accordingly:
  • rag - existing RAG path, no MCP involved
  • mcp_search - LLM with search/lookup tools only
  • mcp_analysis - LLM with analyze_identifiers only
• When MCP_SERVER_PATH is not set, routing is skipped entirely and existing
  RAG behaviour is unchanged

Routing Logic

rag - general knowledge questions answerable from static embeddings
mcp_search - live lookup by identifier, name, species list, or db metadata
mcp_analysis - pathway enrichment on an explicit list of gene/protein identifiers
               (requires both analysis intent AND a list - a list alone routes to rag)

Testing

Manual testing against a running MCP server is pending.

The routing logic has been reviewed against the prompt criteria for the following question types:

• General knowledge questions → rag
• Lookup by identifier or name → mcp_search
• Enrichment analysis on a gene list → mcp_analysis
• Ambiguous or unclear questions → rag fallback

Notes

• Router uses gpt-4o-mini separately from the main generation model to keep
  classification cost low
• Tool subset filtering happens at init time via bind_tools - no rebinding
  per message
• Routing is only active when MCP_SERVER_PATH is set - zero impact on
  deployments without MCP

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AI assistance was used in drafting and implementation. All changes reviewed and verified
by me.