MCP Resume Matching — Milestone 2

Converts the Milestone 1 filesystem tools into a proper MCP (Model Context Protocol) server, and refactors the LangGraph matching agent to talk to that server as an MCP client instead of calling the filesystem directly.

Files

Related MCP server: Filesystem MCP Server

API keys — where they come from

Neither key is ever hardcoded in the source. Both are read from environment variables via python-dotenv, loaded from a local .env file that you create yourself and should not commit to git.

If you'd rather use a different LLM provider, swap ChatOpenAI(...) in matching_agent.py for another LangChain chat model (e.g. ChatAnthropic) and set the matching key instead — the MCP plumbing doesn't change.

Setup in VS Code

1. Open the folder File → Open Folder… → select this project directory.

2. Create and select a virtual environment (Terminal in VS Code, Ctrl+`):

   python -m venv venv
   # Windows:
   venv\Scripts\activate
   # macOS/Linux:
   source venv/bin/activate

   In VS Code, run Python: Select Interpreter (Ctrl+Shift+P) and pick ./venv.

3. Install dependencies

   pip install -r requirements.txt

4. Add your keys

   cp .env.example .env

   Open .env and paste in your real OPENAI_API_KEY (and optionally TAVILY_API_KEY).

5. Add sample resumes

   mkdir -p resumes
   # drop a few .pdf or .txt resumes into resumes/

Running it

You do not run filesystem_mcp_server.py by itself in normal use — matching_agent.py launches it automatically as a subprocess and talks to it over stdio via JSON-RPC 2.0.

# Full agent run (ranks resumes/ against a hardcoded job description)
python matching_agent.py

# All test scenarios (tool discovery, batch_process, watch_directory,
# error handling, end-to-end agent run)
python test_scenarios.py

To manually test the server in isolation, launch it with the MCP inspector (installed via the mcp[cli] extra):

mcp dev filesystem_mcp_server.py

This opens a browser UI where you can call each tool and see the raw JSON-RPC 2.0 requests/responses — useful for the demo video.

Why this counts as JSON-RPC 2.0 / resource discovery

• The mcp SDK's FastMCP class implements the full MCP spec, which is itself built on JSON-RPC 2.0 — every tools/list, tools/call, and resources/list exchange over stdio is a JSON-RPC 2.0 request/response.

• Errors raised inside any @mcp.tool() function (e.g. FileNotFoundError for a bad path) are automatically converted into JSON-RPC 2.0 error responses by the SDK, with the message preserved — that's the "proper error handling and status codes" requirement.

• @mcp.resource(...) decorators (resumes://list, config://server) are what a client calls resources/list / resources/read against — that's the resource discovery endpoint requirement.

Multi-MCP bonus (Part B)

If TAVILY_API_KEY is set in .env, matching_agent.py automatically adds a second entry to its MCP config (web_search, launched via npx -y tavily-mcp), and the LangGraph agent gets tools from both servers in the same run — demonstrating multi-MCP integration without any code changes on your end.

Demo video checklist (5–6 min)

1. Show filesystem_mcp_server.py running via mcp dev — call batch_process and watch_directory live, point at the JSON-RPC traffic in the inspector.

2. Show matching_agent.py connecting as a client (log line: tools/list).

3. Run python test_scenarios.py end to end.

4. Walk through workflow_diagram.md.

5. (Bonus) Add TAVILY_API_KEY and show the second MCP server joining.