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Pavan Madduri — Personal Knowledge MCP Server

by pmady
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Python
Hybrid

Pavan Madduri — Personal Knowledge MCP Server

MCP Python License

A Model Context Protocol (MCP) server that exposes my professional profile — certifications, industry articles, open source contributions, and live GitHub activity — as a queryable API for AI agents.

Why? Instead of a static resume, this is a Personal Knowledge API. Any AI agent (Claude, Gemini, Copilot) can query my career data in real-time. This is AI infrastructure, not just AI usage.

What's Inside

Resources (Static Data)

Resource URI

Description

profile://about

Bio, links, expertise areas

profile://certifications

CNCF Golden Kubestronaut (all 15 certs + LFCS)

profile://articles

9 industry articles (CNCF Blog, IEEE ComSoc, CloudNativeNow, PlatformEngineering.com, d7y.io)

profile://open-source-summary

26 PRs across 15 projects

profile://contributions/cncf

Detailed CNCF project PRs

profile://contributions/aswf

Detailed ASWF project PRs

Tools (Dynamic Functions)

Tool

Description

search_contributions(project)

Search contributions by project name

search_articles(keyword)

Search industry articles by keyword, category, or publication

get_expertise(domain)

Check expertise in a technical domain

get_eb1a_evidence(criterion)

Retrieve EB-1A extraordinary ability evidence

get_github_activity(repo, limit)

Live GitHub PR data via API

get_github_stats()

Live GitHub profile statistics

get_profile_summary()

One-page comprehensive summary

Related MCP server: GitHub MCP Server

Quick Start

Prerequisites

  • Python 3.11+

  • uv (recommended) or pip

Install & Run

# Clone
git clone https://github.com/pmady/pavan-profile-mcp.git
cd pavan-profile-mcp

# Option A: uv (recommended)
uv sync
uv run server.py

# Option B: pip
pip install -e .
python server.py

Environment Variables (Optional)

# For higher GitHub API rate limits (optional — works without it)
export GITHUB_TOKEN="ghp_your_token_here"

Connect to Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "pavan_profile": {
      "command": "uv",
      "args": ["--directory", "/path/to/pavan-profile-mcp", "run", "server.py"]
    }
  }
}

Restart Claude Desktop. You'll see the tools appear in the MCP panel.

Example Prompts

  • "What are Pavan's contributions to Dragonfly?"

  • "Show me his published articles on AI infrastructure"

  • "What EB-1A evidence does Pavan have for original contributions?"

  • "Get his latest GitHub activity"

  • "Does he have expertise in GPU scheduling?"

Connect to Other Clients

Cursor / Windsurf

Add to your MCP config:

{
  "pavan_profile": {
    "command": "uv",
    "args": ["--directory", "/path/to/pavan-profile-mcp", "run", "server.py"]
  }
}

Render (Public Hosting)

One-Click Deploy: Deploy to Render

This server deploys on Render with HTTP transport for remote access.

Live Production Server: https://pavan-profile-mcp.onrender.com/mcp

Connect any MCP client to the remote endpoint:

{
  "mcpServers": {
    "pavan_profile": {
      "url": "https://pavan-profile-mcp.onrender.com/mcp"
    }
  }
}

Manual deployment:

  1. Fork this repo

  2. Go to Render Dashboard

  3. Click "New" → "Blueprint"

  4. Connect your forked repo

  5. Render auto-detects render.yaml and deploys

  6. Your MCP endpoint will be at https://<your-service-name>.onrender.com/mcp

Architecture

AI Agent (Claude / Gemini / Copilot)
        │
        ▼
┌─────────────────────────────┐
│   MCP Protocol (stdio/SSE)   │
├─────────────────────────────┤
│   FastMCP Server            │
│                             │
│   Resources:                │
│   ├── profile://about       │
│   ├── profile://certs       │
│   ├── profile://articles    │
│   └── profile://oss-summary │
│                             │
│   Tools:                    │
│   ├── search_contributions  │
│   ├── search_articles       │
│   ├── get_expertise         │
│   ├── get_eb1a_evidence     │
│   ├── get_github_activity ──┼──► GitHub API (live)
│   ├── get_github_stats    ──┼──► GitHub API (live)
│   └── get_profile_summary   │
│                             │
│   Data: data/profile.json   │
└─────────────────────────────┘

Project Structure

pavan-profile-mcp/
├── server.py              # MCP server — all resources and tools
├── data/
│   └── profile.json       # Structured profile data (certs, articles, PRs)
├── Dockerfile             # Railway/Render deployment
├── pyproject.toml         # Python project config
├── SKILL.md               # Smithery skill definition
├── smithery.yaml          # Smithery.ai config
├── claude_desktop_config.example.json
├── README.md
└── LICENSE

About the Author

Pavan Madduri — Senior DevOps/Platform Engineer

  • CNCF Golden Kubestronaut (all 15 CNCF certifications + LFCS)

  • Published author on CNCF Blog, IEEE ComSoc, CloudNativeNow, PlatformEngineering.com

  • 26 PRs across 15 CNCF & ASWF projects (Dragonfly, Volcano, KEDA, Kubernetes, TiKV, OpenColorIO, and more)

  • Dragonfly Community Member (CNCF Incubating)

GitHub · LinkedIn · Blog

License

MIT

Install Server
A
license - permissive license
A
quality
D
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Tools

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