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octocode-mcp

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Basic Information

Octocode MCP is an open source AI-powered code analysis and research platform designed to give AI assistants and developers deep, actionable knowledge about code across GitHub and package registries. Built on the Model Context Protocol (MCP), the repository implements a full MCP server and related packages that let tools perform semantic code search, structural repository analysis, commit and pull request examination, and package discovery for NPM and Python. The project targets organizations and individual developers who need to extract institutional knowledge from private and public repositories, understand complex multi-service architectures, and discover proven implementations across millions of repositories. The codebase includes a main engine package with eight specialized tools and a utilities package for content processing and AI optimization. The README documents installation options, authentication fallbacks for GitHub access, use cases for enterprise teams, security researchers, and developers, and links to detailed tool schemas and usage guides.

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App URL
https://github.com/bgauryy/octocode-mcp
Github Repository
https://github.com/bgauryy/octocode-mcp/blob/main/README.md

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App Details

Features
Octocode MCP provides semantic code search across many repositories and deep repository intelligence that maps code structure and relationships. It offers commit and pull request analysis to understand code evolution and development patterns and package discovery linking code to NPM and Python packages. The main engine exposes eight specialized MCP tools for AI assistants, while octocode-utils supplies AI-optimized content processing, multi-strategy minification for many file types, JSON-to-natural-language conversion, and robust error handling. The project includes enterprise features for private repository access, an authentication fallback chain supporting environment tokens and GitHub CLI, token-optimized responses, intelligent caching and bulk operations for performance, documented tool schemas and a quick start with multiple installation methods.
Use Cases
The repository helps teams and AI assistants turn raw code into structured, searchable, and context-rich knowledge so developers can discover patterns, reuse battle-tested implementations, and generate documentation from real code. For enterprises it enables institutional knowledge mining, cross-repository architectural analysis, and security and compliance scanning at scale. For individual developers it accelerates learning and development by surfacing proven patterns, producing contextual code examples, and enabling fast semantic searches. Researchers can perform large-scale pattern analysis and ecosystem studies. Integration via MCP lets assistants query live data and receive AI-optimized, structured results for downstream automation, code generation, auditing, and research workflows.

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