This is amazing work - we’ve recently integrated Octocode MCP as part of an internal AI assistant, built for Product Managers to explore product logic directly from the codebase.
It’s been incredibly useful for bridging the gap between product and engineering - especially with the semantic code search and multi-repo context handling.
Excited to see how this evolves!
I built Octocode MCP, an MCP server that enables advanced research on GitHub for any purpose: searching code, exploring repository structures, reading targeted file ranges, analyzing PRs, and synthesizing findings to support code generation, complex flow understanding, and feature exploration (for both public and private organizations).
Motivation:
To fill knowledge gaps in large organizations and the open-source world, and to create smart context for AI agents tackling any task!
Highlights
- Understand unfamiliar code and features quickly: follow a feature from API to UI—even across repositories and services.
- Answer concrete questions with code evidence, not just documentation: "Where is X done?" "How does Y work?"—always with cited references.
- Close the context loop for PMs, developers, analysts, and managers with shared, traceable findings.
- Research like a senior engineer: discover → verify → explain. Designed by an architect who used to do this manually in production and in code for many years.
- Works with your existing GitHub access: supports public and private organization repositories via GitHub CLI or PAT.
- Efficient and secure by default: targeted reads, token‑efficient summaries, and secret‑aware sanitization.
Currently, it is used by several organizations, has ~4.5k downloads per week (according to npm), and is an official Model Context Protocol community MCP server - https://github.com/modelcontextprotocol/servers
Today, it helps developers, product managers, analysts, and engineering managers better understand code and features, closing the context and knowledge loop in large organizations—and even for indie developers.
I'm looking for feedback from real-world use cases, reports of rough edges, ideas for additional research workflows, and further collaboration or sponsorship to take this project to the next level (this is just an initial phase, and there are many plans for the future).
Quick start
1. Install and authenticate to the GitHub CLI – https://cli.github.com/
2. Add Octocode MCP via your client:
`npx octocode-mcp@latest`
3. Start asking anything about any code!
This is amazing work - we’ve recently integrated Octocode MCP as part of an internal AI assistant, built for Product Managers to explore product logic directly from the codebase.
It’s been incredibly useful for bridging the gap between product and engineering - especially with the semantic code search and multi-repo context handling. Excited to see how this evolves!
I built Octocode MCP, an MCP server that enables advanced research on GitHub for any purpose: searching code, exploring repository structures, reading targeted file ranges, analyzing PRs, and synthesizing findings to support code generation, complex flow understanding, and feature exploration (for both public and private organizations).
Motivation: To fill knowledge gaps in large organizations and the open-source world, and to create smart context for AI agents tackling any task!
Highlights - Understand unfamiliar code and features quickly: follow a feature from API to UI—even across repositories and services. - Answer concrete questions with code evidence, not just documentation: "Where is X done?" "How does Y work?"—always with cited references. - Close the context loop for PMs, developers, analysts, and managers with shared, traceable findings. - Research like a senior engineer: discover → verify → explain. Designed by an architect who used to do this manually in production and in code for many years. - Works with your existing GitHub access: supports public and private organization repositories via GitHub CLI or PAT. - Efficient and secure by default: targeted reads, token‑efficient summaries, and secret‑aware sanitization.
Currently, it is used by several organizations, has ~4.5k downloads per week (according to npm), and is an official Model Context Protocol community MCP server - https://github.com/modelcontextprotocol/servers
Today, it helps developers, product managers, analysts, and engineering managers better understand code and features, closing the context and knowledge loop in large organizations—and even for indie developers.
I'm looking for feedback from real-world use cases, reports of rough edges, ideas for additional research workflows, and further collaboration or sponsorship to take this project to the next level (this is just an initial phase, and there are many plans for the future).
Links - GitHub: https://github.com/bgauryy/octocode-mcp - NPM: https://www.npmjs.com/package/octocode-mcp
Quick start 1. Install and authenticate to the GitHub CLI – https://cli.github.com/ 2. Add Octocode MCP via your client: `npx octocode-mcp@latest` 3. Start asking anything about any code!