A Large-Scale Dataset of MCP Implementations on GitHub

2026-07-11 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, quick

Summary

This study introduces the first large-scale, evidence-based dataset of real-world Model Context Protocol (MCP) implementations, collected directly from GitHub. A hybrid pipeline, integrating GitHub REST and GraphQL APIs with custom Python scripts, identified 3,238 candidate repositories. After multi-stage evidence checks and filtering out non-operational projects, a final dataset of 2,297 validated MCP projects was established, achieving an 83% precision at a 95% confidence level. Each project is classified by operational role (client, server, gateway) and exported in a reproducible JSONL schema. Analysis reveals Python and TypeScript dominate MCP development, with hybrid architectures being the most common design pattern, providing a foundational benchmark for future research.

Key takeaway

For AI Engineers or Software Engineers developing with Model Context Protocol (MCP), this dataset offers a critical resource for understanding real-world implementation patterns. You should leverage this benchmark to inform your design choices, ensure compatibility, and identify best practices, particularly when working with Python and TypeScript-based hybrid architectures. This can streamline development and improve the robustness of your MCP integrations.

Key insights

The first large-scale dataset of real-world Model Context Protocol (MCP) implementations offers a foundational benchmark.

Principles

• Transparent verification strategies enhance dataset quality.
• Structured evidence tagging improves data utility.
• Reproducible data organization supports future research.

Method

A hybrid pipeline integrates GitHub REST/GraphQL APIs with Python scripts for multi-stage filtering and validation of MCP repositories, classifying them by operational role (client, server, gateway).

In practice

• Utilize GitHub APIs for large-scale code analysis.
• Classify projects by operational role.
• Filter educational samples from operational codebases.

Topics

• Model Context Protocol
• Large Language Models
• GitHub API
• Software Engineering
• Dataset Curation
• Python
• TypeScript

Best for: Research Scientist, AI Architect, AI Scientist, AI Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.