About Mcpcorpus
Mcpcorpus is an MCP server published by Snakinya in the AI category: 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.
The repository has 35 stars and 7 forks, with the last commit a year ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues before depending on it in production.
Install
git clone https://github.com/Snakinya/MCPCorpusThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
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Static checks derived from the repository 5 days ago. Heuristics on the source, not an audit.
- ✓README presentThe repository ships documentation.
- !License declaredNo license file. Legally you have no right to use it in a product.
- !Tests presentNo tests found. Behaviour changes may go unnoticed between versions.
- !Dependencies pinnedNo lockfile. Installs can pull different code over time.
- ✓No dynamic code executionSource scan found no eval, dynamic Function, or shell execution.
- !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.
README
MCPCorpus: A Large-Scale Evolvable Dataset for Model Context Protocol Ecosystem and Security Analysis
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.
📊 Dataset Overview
- Scale: ~14K MCP servers + 300 MCP clients
- Attributes: 20+ normalized metadata per artifact
- Sources: GitHub, community hubs, package managers
- Applications: Ecosystem analysis, security research, interoperability studies
📁 Structure
MCPCorpus/
├── Crawler/ # Data collection tools
│ ├── Servers/ # Server data
│ ├── Clients/ # Client data
│ ├── github_info_collector.py # GitHub metadata collector
| ├── data_cleaner.py # Data normalization
│ └── tool_extractor.py # mcp tool extract
└── Website/ # Web search interface
├── server.py # Local web server
└── index.html # Search interface
└── mcpso_servers_cleaned.json
└── mcpso_clients_cleaned.json
🚀 Quick Start
Explore Dataset
cd Website
python server.py
# Open http://localhost:8000
Access Data Programmatically
import json
import pandas as pd
# Load datasets
with open('Crawler/Servers/mcpso_servers_cleaned.json', 'r') as f:
servers = json.load(f)
with open('Crawler/Clients/mcpso_clients_cleaned.json', 'r') as f:
clients = json.load(f)
# Convert to DataFrame
servers_df = pd.DataFrame(servers)
clients_df = pd.DataFrame(clients)
Update Dataset (Optional)
# Collect new data
cd Crawler/Servers && python Server_request.py
cd ../Clients && python Client_request.py
# Add GitHub metadata
cd .. && python github_info_collector.py --token YOUR_GITHUB_TOKEN
📚 Citation
If you use MCPCorpus in your research, please cite it as:
@misc{lin2025largescaleevolvabledatasetmodel,
title={A Large-Scale Evolvable Dataset for Model Context Protocol Ecosystem and Security Analysis},
author={Zhiwei Lin and Bonan Ruan and Jiahao Liu and Weibo Zhao},
year={2025},
eprint={2506.23474},
archivePrefix={arXiv},
primaryClass={cs.CR},
url={https://arxiv.org/abs/2506.23474},
}
README fetched from the repository 5 days ago. The original is authoritative.