Pavan Madduri — Personal Knowledge MCP Server
A Model Context Protocol (MCP) server that exposes a professional profile — certifications, industry articles, open source contributions, and live GitHub activity — as a queryable API for AI agents.
README
Pavan Madduri — Personal Knowledge MCP Server
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 |
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)
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:
- Fork this repo
- Go to Render Dashboard
- Click "New" → "Blueprint"
- Connect your forked repo
- Render auto-detects
render.yamland deploys - 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)
License
MIT
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.