GitHub
GitHub’s official server lets AI read and manage code, issues and pull requests in your account. Use GitHub’s hosted version or run it locally with Docker.
Description from the maker (original English)
AI code research platform. Search, analyze, and extract insights from any GitHub repository.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
GITHUB_TOKENValues marked secret are keys to your account. Never share them or paste them into a chat.
You need
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"octocode": {
"command": "npx",
"args": [
"-y",
"octocode-mcp"
],
"env": {
"GITHUB_TOKEN": "your-value"
}
}
}
}Run this in your terminal
claude mcp add octocode --env GITHUB_TOKEN=your-value -- npx -y octocode-mcpFill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"octocode": {
"command": "npx",
"args": [
"-y",
"octocode-mcp"
],
"env": {
"GITHUB_TOKEN": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"octocode": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"octocode-mcp"
],
"env": {
"GITHUB_TOKEN": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026
The registry name was verified with the GitHub account bgauryy, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
You need to fill in: GITHUB_TOKEN
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (7.0.9) reached the registry 12 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: GitHub, Desktop Commander, Cloudflare MCP
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
GitHub’s official server lets AI read and manage code, issues and pull requests in your account. Use GitHub’s hosted version or run it locally with Docker.
MCP server for terminal commands, file operations, and process management
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
Evidence-first code research for AI agents and developers.
Octocode researches your local code and external code alike (GitHub repositories, PRs, npm) with one toolset: ripgrep + AST search, trees, precise reads, and LSP. Use it as a CLI or MCP server, backed by a Rust engine for fast, token-efficient results across single files or mega-repos.
Prerequisites: Node.js 20.12+
1. Run the Octocode CLI with `npx`
npx octocode --help
2. Authenticate with GitHub - optional, but unlocks private repositories and higher API rate limits:
npx octocode auth login
npx octocode status # verify the active token source
3. Choose your interface. Same tools and Rust engine on both. (Clone is on by default in the CLI, opt-in for MCP.)
🖥️ CLI - research straight from your terminal:
npx octocode
🤖 MCP - one-click install:
Show more install options (Windsurf, Kiro, Goose, LM Studio, Claude Code)
Claude Code:
claude mcp add-json octocode --scope user '{"command":"npx","type":"stdio","args":["octocode-mcp@latest"]}'
Any other client: npx octocode install
Add to your MCP client config (or use a one-click install above):
{
"octocode": { "command": "npx", "type": "stdio", "args": ["octocode-mcp@latest"] } }
Put a GitHub token and options under env (see Configuration).
Run npx octocode and agents figure out the rest. The bare command prints built-in usage and the full tool catalog, so any coding agent knows how to drive it out of the box, no MCP client or extra wiring required.
npx octocode # self-describing usage for agents
npx octocode tools # list every tool npx octocode tools localSearchCode --scheme # inspect a tool's schema
Every MCP tool is also a plain command: JSON in, token-efficient YAML out. Local paths route to local tools; owner/repo[/path] routes to GitHub.
npx octocode tools localSearchCode \
--queries '{"path":".","searchText":"authenticate","maxFiles":20}'
```yaml
results:
Learn more at [octocode.ai](https://octocode.ai).
Agents code better from evidence than from guesses. Octocode researches two worlds with one flow, your local code and external code on GitHub and npm, and hands back compact, citable context before an agent changes, reviews, or explains code. *Code is truth; context is the map.*
Most tools do one slice (web search, or grep your repository) and hand back a fixed blob. Octocode covers the whole loop and lets the agent decide what data it needs next:
What you can do (whenever the next step needs proven context, not a guess):
| Need | Use Octocode to |
|---|---|
| Codebase questions | Search local or GitHub code, read exact regions, browse trees, and carry file/line anchors into the answer. |
| Implementation research | Compare patterns across repositories, npm packages, pull requests, commits, and local files before changing code. |
| Semantic navigation | Resolve definitions, references, callers/callees, call hierarchy, hovers, symbols, diagnostics, and type relationships through LSP. |
| Structural matching | Run AST-shaped searches with patterns or YAML rules so comments and strings do not become false positives. |
| Large-file context | Minify, skeletonize, or paginate code so agents spend tokens on relevant structure instead of boilerplate. |
| Agent workflows | Same engine through MCP, CLI, and Agent Skills. |
A blind, head-to-head test on research-oriented flows rather than plain lookups (multi-hop traces, dependency/call-graph chains, commit ranges, blast-radius, PR reviews across repositories).
How it works: 30 GitHub questions × 3 passes; Octocode vs gh, gh+Headroom, and gh+RTK on identical questions (only the CLI differs). A blind judge (gpt-5.5) grades correctness; the metric is characters through the model, counted from instrumented logs (characters, not tokens). Result: at near-parity correctness, Octocode answers with ~2.0× fewer characters than plain `gh`, ~2.6× fewer than `gh`+Headroom, and ~3.2× fewer than `gh`+RTK in the local-build headline runs.
▶ [Open the interactive report](https://raw.githack.com/bgauryy/octocode/main/packages/octocode-benchmark/results/index.html) · [run it / method](https://github.com/bgauryy/octocode/tree/main/packages/octocode-benchmark/skills/octocode-benchmark) · questions · all reports
17 tools in the full catalog. How many register depends on the surface and the flags you set:
| Surface | Registers | What that set is |
|---|---|---|
| MCP, no flags | 8 | GitHub search and read, plus npmSearch |
MCP, ENABLE_LOCAL=true | 14 | Adds the five local tools and lspGetSemantics |
MCP, + ENABLE_CLONE=true | 15 | Adds ghCloneRepo |
MCP, + ENABLE_TOOLS allowlist | 17 | Adds ghListReleases and ghSearchDiscussions |
| CLI, no flags | 15 | Local tools and clone are on by default |
CLI, + ENABLE_RELEASES=1 ENABLE_DISCUSSIONS=1 | 17 | Adds the same two GitHub tools |
ghListReleases and ghSearchDiscussions need two settings on MCP, not one: ENABLE_RELEASES=1 or ENABLE_DISCUSSIONS=1 puts the tool in the catalog, and ENABLE_TOOLS="ghListReleases,ghSearchDiscussions" registers it. Either setting alone leaves the tool unregistered, because both carry isDefault: false and the MCP registration filter admits only default or explicitly allowlisted tools. The CLI needs only the ENABLE_RELEASES and ENABLE_DISCUSSIONS flags. ENABLE_LOCAL and ENABLE_CLONE accept true or 1; ENABLE_RELEASES and ENABLE_DISCUSSIONS also accept yes and on. Flags: Configuration.
Token knobs. concise:true returns path/title-only lists. minify controls file read density: symbols = skeleton with line numbers, standard = comments/blanks stripped (default), none = exact bytes.
| Tool | What it does | Knob |
|---|---|---|
ghSearchCode | Code and path search across GitHub by owner, repository, path, filename, extension, and match filters. Accepts 1 to 5 parallel queries. | concise |
ghGetFileContent | Read a GitHub file or region: full file, line range, match slice, or paginated chars. |
Only the beginning is shown; read the rest in the repo