Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@filesystem-mcp-server List all files in the resumes directory"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
┌─────────────────────────┐ JSON-RPC 2.0 (TCP) ┌─────────────────────────────┐
│ matching_agent.py │ ◄──────────────────────── │ filesystem_mcp_server.py │
│ (LangGraph + GPT-4) │ ──────────────────────── ►│ (MCP Server, port 8765) │
│ │ │ │
│ Nodes: │ tools/call → result │ Tools exposed: │
│ 1. load_job_desc │ │ • read_file │
│ 2. load_resumes │ │ • write_file │
│ 3. watch_new_resumes │ │ • list_directory │
│ 4. match_candidates────┼──► GPT-4o (OpenAI API) │ • search_files │
│ 5. save_report │ │ • get_file_info │
└─────────────────────────┘ │ • delete_file │
│ • watch_directory ★ │
│ • batch_process ★ │
└─────────────────────────────┘Related MCP server: File System MCP Server
pip install -r requirements.txt
export OPENAI_API_KEY="sk-..."python filesystem_mcp_server.py --transport tcp --port 8765python matching_agent.py --jd job_descriptions/senior_engineer.txt --resumes resumes/python -m pytest tests/ -vpython demo_runner.pyFile | Purpose |
| MCP server — JSON-RPC 2.0, 8 tools, stdio + TCP transport |
| Async MCP client used by the agent |
| LangGraph agent with 5 nodes, all I/O via MCP |
| 29 unit tests (JSON-RPC, tools, batch, watch) |
| End-to-end demo script |
| Sample resume files (alice_chen.txt, bob_martinez.txt, priya_nair.txt) |
| Sample JD (senior_engineer.txt) |
| Server logs + generated match reports |
The server implements MCP 2024-11-05 over JSON-RPC 2.0.
Client → initialize (protocolVersion, capabilities)
Server → {protocolVersion, capabilities, serverInfo}
Client → initialized (notification, no response)→ {"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"batch_process","arguments":{...}}}
← {"jsonrpc":"2.0","id":3,"result":{"content":[{"type":"text","text":"{...}"}],"isError":false}}Code | Meaning |
-32700 | Parse error (invalid JSON) |
-32601 | Method not found |
-32602 | Invalid params (missing required field) |
-32001 | File not found |
-32002 | Permission denied / path traversal |
Polls a directory every N seconds for new *.txt files. Returns file_created events:
{"event": "file_created", "path": "resumes/new_candidate.txt", "timestamp": "...", "size_bytes": 1234}Processes multiple files in one RPC call. Operations:
read_all — return full content of each file
word_count — words, lines, chars per file
extract_emails — regex-extracted emails per file
summarize_stats — compact metadata for matching pipeline
This server cannot be deployed
Browse and manage files in your Moxt AI workspace from any MCP client.
MCP Server for an Agent Task Marketplace
Persistent file storage for AI agents via MCP and curl. Upload, download, and version files.
Generate tailored, ATS-optimized resume PDFs and cover letters from a job description, over MCP.
