MCPCorpus
MCPCorpus is a comprehensive dataset for analyzing the Model Context Protocol (MCP) ecosystem, containing ~14K MCP servers and 300 MCP clients with 20+ normalized metadata attributes.
What is MCPCorpus?
MCPCorpus is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to mcpcorpus is a comprehensive dataset for analyzing the model context protocol (mcp) ecosystem, containing ~14k mcp servers and 300 mcp clients with 20+ normalized metadata attributes.
MCPCorpus is a comprehensive dataset for analyzing the Model Context Protocol (MCP) ecosystem, containing ~14K MCP servers and 300 MCP clients with 20+ normalized metadata attributes.
This server falls under the Search & Data Extraction and Analytics categories on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- MCPCorpus is a comprehensive dataset for analyzing the Model
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx mcpcorpusConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use MCPCorpus
MCPCorpus is a research dataset and toolset for analyzing the Model Context Protocol ecosystem, containing approximately 14,000 MCP servers and 300 MCP clients with over 20 normalized metadata attributes per entry. It aggregates data from GitHub repositories, community hubs, and package managers, then normalizes it across sources — covering categories, languages, installation methods, stars, license, and extracted tool definitions. Researchers, product managers, and ecosystem analysts can use it to study MCP adoption trends, compare server capabilities, and understand the distribution of use cases across the growing MCP landscape.
Prerequisites
- Python 3.9 or later with pip
- pandas library installed (`pip install pandas`)
- Optional: a GitHub personal access token for running the metadata enrichment collector
- Git to clone the repository
Clone the repository
Clone MCPCorpus to your local machine to get access to the dataset files and analysis scripts.
git clone https://github.com/Snakinya/MCPCorpus.git
cd MCPCorpusInstall Python dependencies
Install the required Python libraries for data analysis and the web interface server.
pip install pandas requestsExplore the dataset programmatically
Load the pre-collected server and client datasets into pandas DataFrames for analysis. The JSON files are located under Crawler/Servers/ and Crawler/Clients/.
import json
import pandas as pd
with open('Crawler/Servers/mcpso_servers_cleaned.json') as f:
servers = json.load(f)
with open('Crawler/Clients/mcpso_clients_cleaned.json') as f:
clients = json.load(f)
servers_df = pd.DataFrame(servers)
clients_df = pd.DataFrame(clients)
print(f"Servers: {len(servers_df)}, Clients: {len(clients_df)}")Launch the web browser interface
Start the built-in HTTP server to explore the dataset through a browser-based interface, which provides filtering and search over the corpus.
cd Website
python server.py
# Open http://localhost:8000 in your browserRefresh the dataset with latest GitHub metadata
To update the corpus with fresh GitHub stars, descriptions, and tool counts, run the collector scripts with your GitHub personal access token.
export YOUR_GITHUB_TOKEN=ghp_yourtoken
cd Crawler/Servers && python Server_request.py
cd ../Clients && python Client_request.py
cd .. && python github_info_collector.py --token $YOUR_GITHUB_TOKENMCPCorpus Examples
Programmatic analysis example
Sample Python code to query the dataset for servers by language and category.
import json, pandas as pd
with open('Crawler/Servers/mcpso_servers_cleaned.json') as f:
df = pd.DataFrame(json.load(f))
# Top languages
print(df['language'].value_counts().head(10))
# Filter TypeScript servers in the Developer Tools category
ts_dev = df[df['language'].str.lower() == 'typescript']
print(ts_dev[['name', 'stars', 'categories']].sort_values('stars', ascending=False).head(20))Dataset exploration queries
Example analytical questions you can answer with the MCPCorpus dataset.
- "Which programming language is most common among MCP servers in the corpus?"
- "How many MCP servers expose tools for database access vs. web search?"
- "What is the distribution of MCP server stars on GitHub?"
- "Which MCP clients support the most server integrations?"
- "Find all MCP servers tagged with 'security' and compare their installation methods"Troubleshooting MCPCorpus
JSON parsing error when loading dataset files
Some dataset files use UTF-8 encoding with special characters. Open them with explicit encoding: `open('file.json', encoding='utf-8')`. If the file is malformed, run `python -m json.tool Crawler/Servers/mcpso_servers_cleaned.json > /dev/null` to find the offending line.
GitHub rate limit errors when running github_info_collector.py
The GitHub API has a rate limit of 5,000 requests per hour for authenticated tokens. Pass your token via `--token` to use the authenticated rate limit. If you still hit limits, add a sleep between batches or run the collector in chunks by modifying the script's pagination parameters.
Web interface at localhost:8000 shows no data
Ensure you are running server.py from inside the Website/ subdirectory, not the project root. The server expects the JSON data files to be at relative paths defined in the script. Verify the Crawler/Servers/ and Crawler/Clients/ directories are populated before starting the web server.
Frequently Asked Questions about MCPCorpus
What is MCPCorpus?
MCPCorpus is a Model Context Protocol (MCP) server that mcpcorpus is a comprehensive dataset for analyzing the model context protocol (mcp) ecosystem, containing ~14k mcp servers and 300 mcp clients with 20+ normalized metadata attributes. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install MCPCorpus?
Follow the installation instructions on the MCPCorpus GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with MCPCorpus?
MCPCorpus works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is MCPCorpus free to use?
Yes, MCPCorpus is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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