mcp-me
This server provides a personal identity layer for AI assistants, exposing a structured personal profile so AI tools can learn about you automatically.
Ask free-form questions about a person (
ask_about_me) — Answers questions about bio, career history, skills, projects, interests, personality, goals, and FAQs (e.g., "What programming languages do they know?" or "What are their long-term goals?").Search the entire profile by keyword (
search_profile) — Searches across names, bios, job titles, companies, skills, project names, article/book titles, social links, and FAQ answers, returning matched fields with their location in the profile.Static profile resources — Exposes resources like
me://identity,me://career,me://skills, etc., for direct reading by AI assistants.Ready-to-use summaries — Prompts like
introduce_meandsummarize_careergenerate introduction and career summaries, whilecollaboration_fitassesses how well the person matches a project or team.Real-time data via plugins — When enabled, plugins provide live information (e.g., current GitHub repos, coding stats, now-playing music).
Both core tools are read-only and designed to give AI assistants instant, structured access to personal identity data across every conversation.
Generates profile data from Bluesky social media, such as posts and profile information.
Generates profile data from dev.to, including articles and activity.
Generates profile data from GitHub, including repositories, contributions, and open-source projects.
Provides real-time music scrobbles plugin, integrating Last.fm listening history.
Provides real-time now playing plugin, integrating Spotify current track information.
mcp-me
Your AI assistants don't know who you are. Every time you start a conversation with Claude, Copilot, Cursor, or Windsurf, it's a blank slate — no context about your skills, your projects, your career, or what you care about.
mcp-me fixes that. It creates a structured personal profile that any AI assistant can read via the Model Context Protocol (MCP). Think of it as a digital identity layer for AI — your bio, career, skills, interests, projects, and more, always available to every AI tool you use.
You: "Write me a cover letter for this job"
AI: (reads your me://career, me://skills, me://projects)
"Based on your 5 years at Acme Corp, your TypeScript expertise,
and your open-source work on mcp-me..."Why mcp-me?
AI that knows you — Your assistants remember your skills, career, projects, and personality across every conversation
Auto-generated — Pull data from 329 registered generators (implemented across 44 generator source files) with one command
Privacy-first — All data stays local in YAML files on your machine. Nothing is sent to any cloud.
Real-time plugins — 13 live integrations (Spotify now playing, GitHub repos, Last.fm scrobbles) that AI queries on demand
Extensible — Community-driven generators and plugins. Add a new data source in ~10 lines of code.
Works everywhere — Claude Desktop, Cursor, Windsurf, Copilot, and any MCP-compatible AI assistant
Related MCP server: mundigital
Installation
Prerequisite: Node.js 20 or later. Verify with
node -v.
One-Click Install
Add mcp-me to your AI assistant in one click — no path configuration needed (profile defaults to ~/.mcp-me):
Claude Desktop: Download the latest .mcpb release and double-click to install, or drag it into Claude Desktop → Settings → Extensions.
After installing, initialize your profile:
mcp-me init
mcp-me generate --github your-usernamenpm (recommended)
npm install -g mcp-meThis makes the mcp-me command available everywhere on your system. No need to clone any repository — npm downloads the package for you.
mcp-me --help
mcp-me init
mcp-me generate --github your-username
mcp-me serveAlternative — run without installing (via npx):
npx mcp-me --helpnpx downloads the package temporarily and runs it. Useful for trying mcp-me once, but slower on repeated use since it re-downloads each time.
Profile location: Commands default to
~/.mcp-me. Override withMCP_ME_PROFILE_DIRor pass an explicit path:mcp-me serve ~/my-profile.
Quick Start
# 1. Initialize your profile (creates YAML templates + .mcp-me.yaml in ~/.mcp-me)
mcp-me init
# 2. Edit the config file — uncomment your sources
code ~/.mcp-me/.mcp-me.yamlYour .mcp-me.yaml config file:
generators:
github: your-username
devto: your-username
bluesky: handle.bsky.social
zodiac: aquarius
plugins:
github:
enabled: true
username: your-username# 3. Generate! Reads sources from .mcp-me.yaml automatically
mcp-me generate
# 4. Start the MCP server
mcp-me serveCLI flags also work:
mcp-me generate --github octocat --devto myuser
All commands work with npx (zero install) or with mcp-me directly if installed globally. The generate command pulls your data from public APIs and auto-populates profile YAML files — no API keys needed for most sources.
Profile directory structure
~/.mcp-me/ → Default profile location
.mcp-me.yaml → Configuration (generators + plugins)
identity.yaml → Your data (name, bio, contact)
skills.yaml → Your data (languages, tools)
projects.yaml → Your data (portfolio)
career.yaml → Your data (experience)
interests.yaml → Your data (hobbies, topics)
personality.yaml → Your data (traits, values)
goals.yaml → Your data (short/long-term)
faq.yaml → Your data (Q&A pairs)Configure Your AI Assistant
One-Click (Cursor & VS Code)
Use the one-click install badges above. After installing, run mcp-me init to create your profile.
Cursor Plugin (Open Plugins)
mcp-me ships as a full Open Plugins plugin for Cursor. Install from cursor.directory or clone the repo — the plugin auto-detects all components:
Component | Location | Purpose |
MCP Server | Zero-config | |
Rules | Always consult profile before answering | |
Skills | Profile usage and setup guides | |
Agents | Profile assistant and intro writer | |
Commands |
| |
Hooks | Session-start profile reminder | |
LSP | YAML language server for profile files |
Validate the plugin structure: npm run validate:open-plugin
Windsurf
Add to ~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": {
"me": {
"command": "npx",
"args": ["-y", "mcp-me", "serve"]
}
}
}Cursor (manual)
Add to .cursor/mcp.json in your project root:
{
"mcpServers": {
"me": {
"command": "npx",
"args": ["-y", "mcp-me", "serve"]
}
}
}VS Code (GitHub Copilot)
Add to .vscode/mcp.json in your project root (or in your User Settings for global access):
{
"servers": {
"me": {
"command": "npx",
"args": ["-y", "mcp-me", "serve"]
}
}
}Tip: To enable it globally (all workspaces), open VS Code Settings (
Ctrl+Shift+P→ "Preferences: Open User Settings (JSON)") and add themcpkey there instead.
Claude Desktop
Option A — Desktop Extension (.mcpb, recommended):
Download
mcp-me.mcpbfrom GitHub ReleasesDouble-click the file, or drag it into Claude Desktop → Settings → Extensions
Set your profile directory when prompted (default:
~/.mcp-me)Run
mcp-me initif you haven't created a profile yet
Option B — Manual config:
Add to your Claude Desktop config:
{
"mcpServers": {
"me": {
"command": "npx",
"args": ["-y", "mcp-me", "serve"]
}
}
}Agent Instruction Files
AI agents read special Markdown files in your project to understand how they should behave. Pair them with mcp-me so that every AI session automatically consults your profile — no more repeating your stack, career, or preferences in every conversation.
File | Works with |
| Windsurf (Cascade), OpenAI Codex, Cline, and others |
| GitHub Copilot (VS Code + GitHub.com) |
Quick setup:
# AGENTS.md — for Windsurf, Cline, Codex, etc.
cp "$(npm root -g)/mcp-me/templates/AGENTS.md" ./AGENTS.md
# GitHub Copilot
mkdir -p .github
cp "$(npm root -g)/mcp-me/templates/.github/copilot-instructions.md" .github/copilot-instructions.mdBoth templates are included with mcp-me and tell the AI to call ask_about_me whenever context about you would improve the response.
See the Agent Instructions Guide for examples, global setup, and tips on combining with project-specific rules.
Profile Schema
Your profile is a collection of YAML files:
File | Description |
| Name, bio, location, languages, contact info |
| Work experience, education, certifications |
| Technical and soft skills with proficiency levels |
| Hobbies, music, books, movies, food preferences |
| Values, traits, MBTI, strengths |
| Short, medium, and long-term goals |
| Personal and open-source projects |
| Custom Q&A pairs about yourself |
See Schema Reference for full documentation.
MCP Interface
Resources
Static profile data exposed as MCP resources:
me://identity— Personal identity and contactme://career— Professional historyme://skills— Skills and proficienciesme://interests— Hobbies and preferencesme://personality— Personality traits and valuesme://goals— Personal and professional goalsme://projects— Portfolio and projectsme://faq— Frequently asked questions
Tools
ask_about_me— Free-form question about the usersearch_profile— Keyword search across all profile data
Prompts
introduce_me— Generate a 2-paragraph introductionsummarize_career— Summarize career trajectorytechnical_profile— Describe technical skills and stackcollaboration_fit— Evaluate fit for a project
Generators (selected examples)
Generators run during mcp-me generate to auto-populate your profile from public APIs or user-provided exports. No API keys needed for most sources.
Current counts:
Registered generators: 329
Generator source files: 44 single-generator files + 15 batch files (
src/generators/)
The table below is a curated list of commonly used generators. The complete source of truth is
src/generators/index.ts.
Category | Flag | Source | Data |
Code |
| GitHub API | Repos, languages, stars, profile |
Code |
| GitLab API | Projects, topics, profile |
Code |
| Bitbucket API | Repos, languages |
Code |
| Hugging Face API | Models, datasets, spaces |
Code |
| Kaggle API | Competitions, datasets, medals |
Code |
| Gitea API | Repos, languages |
Writing |
| DEV.to API | Articles, tags, reactions |
Writing |
| Medium RSS | Articles, categories, article text |
Writing |
| Blogger XML export | Imported posts from a local backup, filtered by author if needed |
Writing |
| Hashnode GraphQL | Blog posts, tags |
Writing |
| Substack RSS | Newsletter posts, article text |
Writing |
| WordPress API | Blog posts, categories, tags |
Writing |
| Open Library API | Books authored |
Writing |
| ORCID API | Academic publications |
Writing |
| S2 API | Research papers, citations |
Writing |
| YouTube RSS | Videos, channel info |
Community |
| Stack Exchange API | Top tags, reputation, badges |
Community |
| HN Firebase API | Karma, submissions |
Community |
| Mastodon API | Posts, hashtags, bio |
Community |
| AT Protocol API | Posts, followers |
Community |
| Reddit JSON API | Karma, bio |
Community |
| ProductHunt GraphQL | Launched products, upvotes |
Community |
| Threads API | Bio, follower stats |
Packages |
| npm Registry | Published packages |
Packages |
| PyPI JSON API | Package metadata |
Packages |
| Crates.io API | Rust crates |
Packages |
| Docker Hub API | Container images |
Activity |
| WakaTime API | Coding time, languages, editors |
Blogger XML Backup
--blogger-backup imports written content from a Blogger XML export file on disk. It is designed for archived or multi-author blogs where you want to recover your posts even if the site is old, partially offline, or not easily queryable through a public API.
By default it imports all post entries from the file. If the backup contains multiple authors, append ::author1,author2,... after the file path to keep only posts whose author name or email matches one of those values.
Examples:
# Import all posts from a Blogger export
mcp-me generate --blogger-backup ~/Downloads/blog-2026-03-24.xml
# Import only posts written by specific authors/emails
mcp-me generate --blogger-backup "~/Downloads/blog.xml::fernandopalad@gmail.com,fnpaladini@gmail.com,Fernando Paladini"How to get the export file:
Open Blogger and choose your blog.
Go to
Settings.In
Manage blog, clickBack up content.Download the XML file.
Pass that file path to
--blogger-backup.
The generator imports matching posts into projects.yaml with category: article, preserves post labels as tags, and adds summary FAQ entries about the archive.
| Activity | --letterboxd <user> | Letterboxd RSS | Films watched, ratings |
| Activity | --goodreads <user> | Goodreads RSS + author page | Read books, shelves, reviews, published books |
| Activity | --chess <user> | Chess.com API | Rating, stats |
| Activity | --lichess <user> | Lichess API | Rating, games |
| Activity | --codewars <user> | Codewars API | Rank, honor, languages |
| Activity | --leetcode <user> | LeetCode GraphQL | Problems solved, contests |
| Activity | --lastfm <user> | Last.fm API | Listening history, top artists |
| Activity | --steam <id> | Steam API | Games, playtime |
| Activity | --twitch <user> | Twitch API | Stream info |
| Activity | --dribbble <user> | Dribbble | Design shots, portfolio |
| Activity | --unsplash <user> | Unsplash API | Photos, downloads, collections |
| Activity | --exercism <user> | Exercism API | Language tracks, exercises |
| Activity | --hackerrank <user> | HackerRank API | Badges, challenges solved |
| Activity | --anilist <user> | AniList GraphQL | Anime/manga stats, genres |
| Identity | --gravatar <email> | Gravatar API | Bio, linked accounts, photo |
| Identity | --keybase <user> | Keybase API | Verified identity proofs |
Want to add a new data source? See the Generator Creation Guide.
Plugins (13 live integrations)
Plugins run during mcp-me serve and provide real-time data to AI assistants on every query.
Plugin | Description | Auth |
GitHub | Live repos, activity, languages | Optional token |
Spotify | Now playing, top artists, playlists | OAuth required |
Professional history from export | Local JSON file | |
WakaTime | Live coding stats, languages | Optional API key |
DEV.to | Live articles, reactions | Optional API key |
Bluesky | Live posts, profile, followers | None |
Hacker News | Live stories, karma | None |
Live karma, posts | None | |
GitLab | Live projects, activity, MRs | Optional token |
Mastodon | Live toots, profile, engagement | None |
YouTube | Live videos, channel stats | Optional API key |
Last.fm | Now playing, top artists, scrobbles | Optional API key |
Steam | Currently playing, game library | Optional API key |
Enable plugins in .mcp-me.yaml:
plugins:
github:
enabled: true
username: "your-username"
spotify:
enabled: true
client_id_env: "SPOTIFY_CLIENT_ID"
client_secret_env: "SPOTIFY_CLIENT_SECRET"
refresh_token_env: "SPOTIFY_REFRESH_TOKEN"Community plugins are installed from npm (mcp-me-plugin-*) and auto-discovered. See the Plugin Creation Guide.
Generators vs Plugins
Generators | Plugins | |
Run when |
|
|
Output | Static YAML files | Live MCP resources/tools |
Auth | Almost never needed | Sometimes (OAuth) |
Example | "Repos I had in March" | "Repos I have right now" |
Extend | Add | Add |
CLI Reference
# Auto-generate profile from multiple data sources
mcp-me generate [--directory] --github <user> [--devto <user>] [--stackoverflow <id>] ...
# Initialize with blank YAML templates (defaults to ~/.mcp-me)
mcp-me init [directory]
# Validate profile YAML files
mcp-me validate [directory]
# Start the MCP server (defaults to ~/.mcp-me)
mcp-me serve [directory]
# Scaffold a new generator or plugin (for contributors)
mcp-me create generator <name> [--category <category>]
mcp-me create plugin <name>Profile location: Defaults to
~/.mcp-me. Override withMCP_ME_PROFILE_DIRor pass an explicit[directory]argument.
Development
# Clone the repo
git clone https://github.com/paladini/mcp-me.git
cd mcp-me
# Install dependencies
npm install
# Run tests
npm test
# Build
npm run build
# Run in dev mode
npm run devFAQ
What is MCP (Model Context Protocol)? MCP is an open standard by Anthropic that connects AI assistants to external data sources. mcp-me uses MCP to let AI assistants read your personal profile data.
Do I need API keys?
Most generators use public APIs with no auth needed. Some (Strava, Spotify) require tokens — see the .mcp-me.yaml comments for details.
Can I use this with Claude Desktop / VS Code / Cursor / Windsurf? Yes! Any MCP-compatible AI assistant works. See the Configure Your AI Assistant section.
How do I make AI agents use my profile automatically?
Drop an AGENTS.md (for Windsurf, Cline, Codex) or .github/copilot-instructions.md (for GitHub Copilot) into your project. Templates are included — see the Agent Instructions Guide.
How do I add a new data source?
Run mcp-me create generator myservice to scaffold a new generator, or see the Generator Creation Guide.
Is my data stored anywhere? No. All data stays local in your YAML files. The MCP server reads from disk — nothing is sent to any cloud.
How many generators are there? Currently 329 registered generators, implemented across 44 generator source files plus 15 batch files.
Contributing
We welcome contributions! Whether it's a new plugin, a bug fix, or documentation improvements — see CONTRIBUTING.md for guidelines.
Maintainers: see Publishing Guide for release and distribution instructions.
License
Available Tools
2 toolsask_about_meAsk About Me — Personal Q&AARead-only
Ask any question about this person and get an answer based on their complete profile. Covers: bio, career history, skills, projects, interests, personality, goals, and FAQ. Examples: 'What programming languages do they know?', 'Where do they work?', 'What books have they written?'
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | Any question about this person (e.g. 'What are their top skills?', 'Do they have open-source projects?') |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint: true, and the description confirms read-only behavior. It adds value by detailing the scope of answers (bio, career, etc.), but no additional behavioral traits like rate limits or authentication needs are disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with examples, front-loading the core action and then elaborating with scope and examples. Every sentence is informative and no word is wasted.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description is complete. It covers what the tool does, the range of questions it can answer, and provides examples. No missing critical information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a well-described parameter. The description reinforces the parameter's meaning with examples, but adds little beyond the schema's own description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Ask any question about this person and get an answer based on their complete profile.' It lists specific coverage areas and provides examples, effectively distinguishing it from the sibling tool 'search_profile' which likely searches for profiles.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (for personal Q&A) and provides examples, but does not explicitly state when not to use it or mention alternatives beyond the sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_profileSearch Profile — Find Skills, Projects, ExperienceARead-only
Search across the entire personal profile for a keyword or phrase. Searches through: name, bio, job titles, companies, skill names, project names, article titles, book titles, social links, FAQ answers, and more. Returns matching fields with their location in the profile.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Keyword to search for (e.g. 'TypeScript', 'open-source', 'running') |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true. The description adds context on search scope (searches multiple fields) but does not disclose any additional behavioral traits beyond safety. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with the core action. Every sentence adds value without redundancy or excess.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (single param, no output schema, annotations present), the description covers what is searched, the scope, and what is returned (matching fields with location). No gaps identified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a single parameter. The schema description includes examples, and the tool description does not add further semantic meaning. Baseline score applies as schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches the entire personal profile for a keyword, listing specific fields searched (name, bio, job titles, etc.). This differentiates it from the sibling tool 'ask_about_me' which is likely conversational.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for keyword searches but does not explicitly state when to use search_profile over ask_about_me or provide any exclusions. Basic guidance is present but no comparative context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
The two tools have clearly distinct purposes: 'ask_about_me' answers natural language questions, while 'search_profile' performs keyword search. There is no overlap in functionality.
Both tools follow a consistent verb_noun pattern using snake_case (ask_about_me, search_profile), making the naming predictable and clear.
With only 2 tools, the server is slightly thin but still well-scoped for querying a personal profile. The tools are comprehensive enough to cover the intended use cases.
The tools cover both Q&A and keyword search adequately for a read-only profile assistant. Minor gaps exist (e.g., no explicit list tool), but they are not critical.
Maintenance
Resources
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Looking for Admin?
If you are the server author, to access and configure the admin panel.
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