A Large-Scale Dataset of MCP Implementations on GitHub

2026-07-14 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, long

Summary

A new study introduces the first large-scale, evidence-based dataset of Model Context Protocol (MCP) implementations, collected directly from GitHub. Utilizing a hybrid pipeline combining GitHub REST and GraphQL APIs with custom Python scripts, the research identified 3,238 candidate repositories. After multi-stage evidence checks and filtering, including a manual review confirming 83% precision at a 95% confidence level, a final dataset of 2,297 validated MCP projects was established. Each project is classified by operational role (client, server, gateway) and exported in a reproducible JSONL schema. Analysis reveals Python and TypeScript as dominant languages in MCP development, with hybrid architectures being the most common design pattern. This dataset provides a foundational benchmark for studying real-world MCP ecosystems and supports future research on integration and compatibility.

Key takeaway

For AI Engineers building or integrating with Model Context Protocol (MCP) systems, this dataset offers a critical, verifiable resource. You can use its structured evidence and role classifications to understand real-world MCP design patterns, identify common language choices like Python and TypeScript, and inform your architectural decisions. Utilize this data to benchmark your own MCP implementations, anticipate integration challenges, and contribute to more robust, compatible agentic workflows.

Key insights

A new dataset provides verifiable, code-grounded insights into real-world Model Context Protocol implementations on GitHub.

Principles

• Evidence-based verification improves dataset reliability.
• Hybrid architectures are common in MCP development.
• Python and TypeScript dominate MCP implementations.

Method

A hybrid pipeline used GitHub APIs and Python scripts for discovery, followed by multi-stage evidence checks (manifests, directory structure, CI/CD) and manual validation to classify 2,297 MCP projects.

In practice

• Use the dataset to analyze MCP security vulnerabilities.
• Study MCP evolution through commit history.
• Inform future MCP integration and compatibility efforts.

Topics

• Model Context Protocol
• GitHub Data
• LLM Agents
• Protocol Implementation
• Software Engineering
• Dataset Curation

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.