feat(mcp): connect Reactome MCP server to LangGraph agent with LangChain tool wrappers

[GovindhKishore]

Summary

Integrates the Reactome MCP server into the LangGraph agent by wrapping MCP tools as LangChain StructuredTool objects and connecting them to the React-to-Me chat profile. Builds directly on the MCP client foundation from #127.

Changes

New - src/mcp/mcp_tools.py

• create_mcp_tools() async factory that starts the MCP server and returns LangChain tool wrappers
• Five tools covering the most impactful MCP capabilities:
  • search_reactome - search pathways, proteins, genes by keyword
  • get_pathway - retrieve pathway details by stable ID (e.g. R-HSA-109582)
  • analyze_identifiers - run pathway enrichment analysis on a gene/protein list
  • get_database_info - get current Reactome release version
  • get_species - list all species available in Reactome
• Returns ([], None) when no server path provided - chatbot works unchanged without MCP

Modified - src/agent/graph.py

• MCP server started in initialize() controlled by MCP_SERVER_PATH environment variable
• Graphs built individually per profile - React-to-Me receives mcp_tools, Cross-Database unaffected
• MCP server stopped cleanly in close_pool() on app shutdown
• llm, embedding, profiles stored as instance variables so initialize() can access them

Modified - src/agent/profiles/react_to_me.py

• create_reactome_graph() and ReactToMeGraphBuilder accept optional mcp_tools parameter
• Tools bound to LLM once at init time via bind_tools() - not re-bound on every message
• call_model implements tool-calling loop with 15 iteration guard against infinite loops
• Falls back gracefully to existing reactome_rag behaviour when mcp_tools is empty

How It Works

User message arrives
-> LLM receives message + tool schemas via bind_tools()
-> LLM decides: answer directly or call an MCP tool
-> if tool call requested -> LangChain executes matching @tool function
-> MCPClient sends JSON-RPC to Node.js reactome-mcp server
-> Node.js calls Reactome REST API and returns result
-> result sent back to LLM as ToolMessage
-> LLM generates final answer using tool result
-> loop repeats if LLM requests another tool, up to 15 iterations max
-> if no tools available -> existing reactome_rag behaviour unchanged

Usage

Set MCP_SERVER_PATH to the compiled MCP server entry point:

MCP_SERVER_PATH=/path/to/reactome-mcp/dist/index.js

Leave unset to run in existing RAG-only mode with zero behaviour change.

Relation to Previous Work

• Builds on feat(mcp): add minimal Python MCP client for reactome-mcp integration #127 (MCP client + process manager)

AI Transparency

AI Tools (Claude) were used to assist with drafting code and documentation. All changes were reviewed and verified by me.

closes #136