Portfolio MCP Server

An MCP server that turns my AI project portfolio into something you can query, not just read.

Point any MCP client (Claude Desktop, Cursor, custom agents) at it and ask "What has Ayush built with LangGraph?" or "What's his flagship project?" — it answers from live structured data, not a static PDF.

Watch the full walkthrough ▶

Table of contents

• Why this exists

• Demo

• Tools exposed

• Quickstart

• Connect to Claude Desktop

• Stack

• Testing & CI

• Project structure

• Roadmap

• License

• Author

Related MCP server: Axon.MCP.Server

Why this exists

Most AI-developer portfolios are a list of links. This is a working MCP server — the same protocol agentic products use to connect to tools — built around my own portfolio. It's both a real implementation of the spec and an answer to "show me you've actually built with MCP," not just talked about it.

Demo

Full video walkthrough (setup → tool calls → live answers): watch here ▶

Tools exposed

Quickstart

git clone https://github.com/ayush-s-tomar/portfolio-mcp-server.git
cd portfolio-mcp-server

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

pip install -r requirements.txt

Test it interactively with the MCP Inspector before wiring it into a client:

mcp dev server.py

This opens a browser UI where you can call each tool manually and inspect raw request/response payloads.

Connect to Claude Desktop

Open your Claude Desktop config file:

If the file already has an mcpServers key with other servers in it, add the "portfolio" entry inside the existing object rather than overwriting the file. Use the absolute path to server.py on your machine:

{
  "mcpServers": {
    "portfolio": {
      "command": "python",
      "args": ["/absolute/path/to/portfolio-mcp-server/server.py"]
    }
  }
}

Restart Claude Desktop, then ask it something like:

"What projects has Ayush built with FastAPI?"

Claude will call search_projects_by_stack and answer from the live data.

Stack

• Python 3.10+

• MCP Python SDK (FastMCP)

• stdio transport

Testing & CI

Every push and pull request runs through GitHub Actions:

• Lint — ruff check .

• Type check — mypy server.py

• Smoke test — spins up the server and calls each of the 5 tools over stdio to confirm they return valid, schema-matching JSON

See .github/workflows/ci.yml. Run the same checks locally before opening a PR:

pip install -r requirements-dev.txt
ruff check .
mypy server.py
pytest

Project structure

portfolio-mcp-server/ ├── server.py # FastMCP server + tool definitions ├── data/ │ └── projects.json # Project data the tools read from ├── tests/ │ └── test_tools.py # Smoke tests for each tool ├── requirements.txt ├── requirements-dev.txt └── .github/workflows/ci.yml

Roadmap

• search_projects_by_stack — support matching on multiple technologies at once

• Add an HTTP/SSE transport option alongside stdio for remote clients

• Publish to the MCP server registry

• Cache resume/project data with a lightweight refresh endpoint instead of static JSON

License

Released under the MIT License.

Author

Ayush Tomar — GitHub

If this was useful as a reference for building your own MCP server, a ⭐ on the repo is appreciated.

mcp-name: io.github.ayush-s-tomar/portfolio-mcp-server