The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the Portfolio MCP Server listing page.
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.
TL;DR
pip install portfolio-mcp-server gets it running in any MCP client in under a minute, no repo clone required.
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.
| MCP Inspector — tool discovery | Live chat demo |
|---|---|
![]() | ![]() |
https://github.com/user-attachments/assets/4c1b844a-087f-48a6-b156-bdef27282acc
Setup → tool calls → live answers, end to end.
| Tool | Description |
|---|---|
list_projects | Short summary of all 9 projects |
get_project_details(project_name) | Full details for one project |
search_projects_by_stack(technology) | Find projects using a given technology |
get_flagship_project | The single best project to look at first |
get_resume_summary | Background, target role, and core stack |
Option A — install from PyPI (fastest):
Option B — clone and run from source (for local edits/testing):
Test it interactively with the MCP Inspector before wiring it into a client:
This opens a browser UI where you can call each tool manually and inspect raw request/response payloads.
Open your Claude Desktop config file:
| OS | Path |
|---|---|
| macOS | ~/Library/Application Support/Claude/claude_desktop_config.json |
| Windows | %APPDATA%\Claude\claude_desktop_config.json |
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.
If you installed via PyPI (Option A above):
If you're running from a cloned source checkout (Option B above): use the absolute path to server.py on your machine:
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.
FastMCP)io.github.ayush-s-tomar/portfolio-mcp-server)Every push and pull request runs through GitHub Actions:
ruff check .mypy server.pySee .github/workflows/ci.yml. Run the same
checks locally before opening a PR:
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 ├── pyproject.toml # PyPI packaging config ├── server.json # MCP registry manifest └── .github/workflows/ci.yml
search_projects_by_stack — support matching on multiple technologies at onceReleased under the MIT License.
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