About MCP Server Dataset Builder
MCP Server Dataset Builder collects MCP server listings from a curated repository and GitHub, then merges and maintains them as a CSV dataset. It is intended for people tracking the MCP server ecosystem and includes categorization and technology detection.
Key features
- 01Collects curated and GitHub listings
- 02Merges and deduplicates entries
- 03Categorizes repositories automatically
- 04Detects languages and frameworks
- 05Maintains a daily CSV dataset
Use cases
- 01Building a directory of MCP servers
- 02Tracking changes in server listings
- 03Filtering repositories by category or stack
MCP support
It collects and organizes information about MCP servers; it does not describe tools or resources exposed to MCP clients.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEMCP Server Dataset Builder
A comprehensive tool for building and maintaining a dataset of Model Context Protocol (MCP) servers. This tool automatically collects, categorizes, and updates information about MCP servers from multiple sources.
Overview
The MCP Server Dataset Builder is designed to:
- Extract MCP server information from the awesome-mcp-servers repository
- Search GitHub for additional MCP server repositories
- Merge and deduplicate data from both sources
- Generate a daily CSV file with comprehensive information about each server
Features
- Dual Data Sources: Combines data from curated lists and GitHub search
- Automatic Categorization: Assigns categories based on repository content
- Tech Stack Detection: Identifies programming languages and frameworks
- Emoji Tagging: Adds visual indicators for quick identification
- Daily Updates: Automatically runs to keep the dataset current
- Data Persistence: Maintains historical data while adding new entries
Dataset Structure
The generated CSV files contain the following fields:
| Field | Description |
|---|---|
| name | Repository name |
| description | Repository description |
| html_url | URL to the repository |
| stars | Number of GitHub stars |
| forks | Number of GitHub forks |
| keywords | Comma-separated list of keywords |
| category | Primary category (e.g., framework, utility, client) |
| techstack | Comma-separated list of technologies used |
| emojis | Visual indicators for quick identification |
Usage
Automatic Daily Updates
The dataset is automatically updated daily via GitHub Actions. No manual intervention is required.
Manual Trigger
You can manually trigger the workflow from the GitHub Actions tab:
- Go to the "Actions" tab in the repository
- Select "Unified MCP Servers Extraction"
- Click "Run workflow"
- Optionally customize:
- Keywords for GitHub search
- Minimum stars and forks thresholds
- Which extraction methods to run
Local Development
To run the scripts locally:
# Install dependencies
pip install -r requirements.txt
# Run README extraction
python extract_mcp_servers.py
# Run GitHub search
python daily.py
Environment Variables
The following environment variables can be used to customize the behavior:
| Variable | Description | Default |
|---|---|---|
| GITHUB_TOKEN | GitHub API token for authentication | - |
| KEYWORDS_ENV | Comma-separated list of search keywords | MCP-related keywords |
| MIN_STARS | Minimum number of stars for repositories | 10 |
| MIN_FORKS | Minimum number of forks for repositories | 5 |
Data Sources
1. Awesome MCP Servers Repository
The tool extracts data from the awesome-mcp-servers repository, which contains a curated list of MCP servers organized by category.
2. GitHub Search
The tool searches GitHub for repositories matching MCP-related keywords, ensuring comprehensive coverage of the ecosystem.
Categorization System
Repositories are categorized based on their content and purpose:
- Framework: Core MCP server implementations
- Utility: Helper tools and utilities
- Client: Client libraries and applications
- Tutorial: Learning resources and examples
- Database: Database integrations
- API: API implementations
- Storage: Storage solutions
- AI: AI and LLM integrations
- Chat: Chat and messaging features
- Search: Search functionality
Tech Stack Detection
The tool identifies the following technologies:
- Languages: Python, TypeScript, Go, Rust, Java, C#
- Frameworks: FastAPI, Langchain, Spring
- Protocols: SSE, WebSocket, HTTP
- Deployment: Cloud, Local, Docker
- Platforms: iOS, Windows, Linux
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Comments
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An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.