# A Large-Scale Dataset of MCP Implementations on GitHub

arXiv: https://arxiv.org/abs/2607.10123 · HTML: https://mlsanity.com/paper/2607.10123/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 11 Jul 2026.

## Builds on (most cited first, 5 of 5)

- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases](https://mlsanity.com/paper/2306.05301/) (2023)
- [From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review](https://mlsanity.com/paper/2504.19678/) (2025)
- [Model Context Protocol (MCP) at First Glance: Studying the Security and Maintainability of MCP Servers](https://mlsanity.com/paper/2506.13538/) (2025)
- [Traceability and Accountability in Role-Specialized Multi-Agent LLM Pipelines](https://mlsanity.com/paper/2510.07614/) (2025)

## Built on by (most cited first, 3 of 3)

- [An Empirical Study of Model Context Protocol Applications](https://mlsanity.com/paper/2607.25635/) (2026)
- [A Two-Dimensional Study of the Model Context Protocol: Publication and Adoption](https://mlsanity.com/paper/2609.14721/) (2026)
- [When Tool Calls Succeed but Workflows Fail: Anomalies at the Agent-Tool Boundary](https://mlsanity.com/paper/2609.15397/) (2026)

## Abstract (arXiv)

The rapid emergence of the Model Context Protocol (MCP) has introduced a new standard for connecting large language models to external tools and services. Despite its rapid adoption in open-source development, systematic understanding of how MCP is implemented, structured, and maintained remains limited. This study presents the first large-scale, evidence-based dataset of real-world MCP implementation collected directly from GitHub. Using a hybrid pipeline that integrates the GitHub REST and GraphQL APIs with custom Python verification scripts, 3,238 candidate repositories were discovered, filtered, and validated through multi-stage evidence checks. Each verified project was classified by operational role (e.g., client, server, gateway) and exported in a reproducible JSONL schema. A manual review of a representative subset confirmed an overall precision of 83% at a 95% confidence level, and additionally revealed a set of repositories functioning primarily as educational samples, tutorials, or demonstration templates. A targeted exclusion rule was then applied to remove these non-operational repositories, resulting in a final dataset of 2,297 validated MCP projects. The analysis shows that Python and TypeScript dominate MCP development, with hybrid architectures emerging as the most common design pattern. By emphasizing transparent verification strategies, structured evidence tagging, and reproducible data organization, this work establishes a foundational benchmark for studying real-world MCP ecosystems and supports future research on integration, connectivity, and compatibility across the broader developer community.