octocode-mcp and codegraph
These are complements: octocode-mcp provides natural language semantic search across codebases while codegraph provides structural dependency analysis and complexity metrics, so they address different aspects of code understanding that could be combined in a comprehensive code intelligence workflow.
About octocode-mcp
bgauryy/octocode-mcp
MCP server for semantic code research and context generation on real-time using LLM patterns | Search naturally across public & private repos based on your permissions | Transform any accessible codebase/s into AI-optimized knowledge on simple and complex flows | Find real implementations and live docs from anywhere
This project helps software developers enhance their AI assistants by providing a comprehensive understanding of codebases. It takes code from GitHub, GitLab, and local repositories and processes it to allow AI assistants to perform tasks like code search, understanding implementations, and reviewing pull requests with deep context. This tool is for software engineers, tech leads, or engineering managers who want their AI assistants to operate with the expertise of a senior staff engineer.
About codegraph
optave/codegraph
Code intelligence CLI — function-level dependency graph across 11 languages, 30-tool MCP server for AI agents, complexity metrics, architecture boundary enforcement, CI quality gates, git diff impact with co-change analysis, hybrid semantic search. Fully local, zero API keys required.
This tool helps software developers and architects maintain code quality and understand complex codebases, especially when working with AI agents. It analyzes your entire codebase to create a detailed map of how functions connect and depend on each other. This map helps catch structural problems early, leading to more robust code and fewer review cycles.
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