personal-context
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
personal-context
Turn your digital footprint into an AI-readable profile. GitHub + Blogs + Resume + Notes -> one context source for every AI agent.
Work in progress — something crazy on the way. ✦
Documentation: https://ayush7614.github.io/personal-context/
Current AI tools forget who you are. You keep re-explaining your work, skills,
projects, blogs, and open-source contributions. personal-context aggregates
all of it from your real sources and outputs it as a single profile you can
print, share, or feed to any AI tool (Cursor, Claude Code, Codex, Gemini CLI).
$ npx personal-context all
Ayush Kumar
DevRel · AI Agents · Open Source · AI Security
Current Role:
Lead Developer Relations Engineer @ NodeShift AI Cloud
Contact:
Email: ayushknj3@gmail.com
GitHub: https://github.com/Ayush7614
Recent Blogs:
- AI Agents Masterclass — Full Visual Guide
- Harness Engineering — Full Visual Guide
...
GitHub:
Contributions: 1250
Repositories: 35
...Quick start
# 1. Scaffold a config in the current folder
npx personal-context init
# 2. Edit personal.yaml with your details + source handles
# 3. See your full profile
npx personal-context all
# 4. Generate AI-readable files (context.md, profile.json, llms.txt)
npx personal-context buildRelated MCP server: CacheTank MCP Server
Commands
Command | Description |
| Create a starter |
| Animated "something crazy on the way" splash |
| Full profile overview (the single-command output) |
| GitHub stats + latest contributions |
| Recent posts across all RSS feeds |
| Role + experience timeline |
| Open-source contributions |
| Write |
| One-paragraph AI brief |
Every read command supports --json for piping into other tools.
Configuration
Everything is driven by a single personal.yaml (see personal.yaml
for a full reference). Static facts (name, role, skills, projects) live in the
file; live data is pulled from the sources you configure:
Information | Source |
GitHub contributions, PRs, repos | GitHub REST + GraphQL API |
Latest blogs | RSS feeds (Medium, Hashnode, Dev.to) |
Experience timeline |
|
Resume skills / summary | Resume JSON or PDF |
Set GITHUB_TOKEN (see .env.example) to unlock contribution
counts and higher rate limits.
MCP server
personal-context also ships an MCP server so
any AI agent (Cursor, Claude Code, Codex, etc.) can query your context live.
Add it to your MCP client config (e.g. Cursor's ~/.cursor/mcp.json):
{
"mcpServers": {
"personal-context": {
"command": "npx",
"args": ["-y", "personal-context-mcp"],
"env": {
"PERSONAL_CONTEXT_CONFIG": "/absolute/path/to/personal.yaml",
"GITHUB_TOKEN": "optional-token"
}
}
}
}Then your agent can call:
Tool | Returns |
| The full profile object |
| One-paragraph AI brief |
| Recent posts across feeds |
| Contributions, PRs, repos, top repos |
| Projects you build/ship |
| Open-source contributions |
| Email, website, socials |
| What you're learning + skills |
PERSONAL_CONTEXT_CONFIG points the server at your personal.yaml regardless of
the directory the client launches it from.
Architecture
A pnpm monorepo with a shared core so the CLI, the MCP server, and your portfolio all consume the same profile data.
personal-context/
├── packages/
│ ├── core/ # sources + aggregation + renderers (Profile, context.md, llms.txt)
│ ├── cli/ # the `personal-context` command
│ └── mcp/ # the `personal-context-mcp` MCP server
└── personal.yamlRoadmap
v1 — Core + CLI (GitHub, blogs, LinkedIn, resume, generated profiles) ✅
v2 — MCP server (
get_profile,get_recent_blogs,get_github_stats, ...) ✅ + portfoliocontext.json/llms.txtendpoints (in progress)v3 — "Ask My Portfolio" chat + auto-learning timeline
v4 — AI personal brief + scheduled refresh
License
MIT © Ayush Kumar
Tool Schema Changelog
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This server cannot be deployed
Maintenance
Related MCP Connectors
One profile — skills, credentials, and memory — synced to every agent tool via one MCP URL.
Your memory, everywhere AI goes. Build knowledge once, access it via MCP anywhere.
Cross-AI personal memory. Save once in ChatGPT, recall in Claude, Mistral, Grok, or any MCP client.
Persistent memory, knowledge graph, task management and more for AI agents via MCP.
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