instagit
Instagit
Let Your Agents Instantly Understand Any GitHub Repo
An MCP server that gives coding agents instant insight into any Git repository — no guessing, no hallucination.
Quick Start
Agentic Install
Share the following with your agent to get started:
curl -s https://instagit.com/install.mdManual Install
Add to your MCP client configuration:
{
"mcpServers": {
"instagit": {
"command": "npx",
"args": ["-y", "instagit@latest"]
}
}
}Works with Claude Code, Claude Desktop, Cursor, OpenClaw, and any MCP-compatible client. The @latest tag ensures you always get the most recent version.
Related MCP server: my-local-mcp
Why
Agents that integrate with external libraries are flying blind. They read docs (if they exist), guess at APIs, and hallucinate patterns that don't match the actual code. The result: broken integrations, wrong function signatures, outdated usage patterns, hours of debugging.
When an agent can actually analyze the source code of a library or service it's integrating with, everything changes. It sees the real function signatures, the actual data flow, the patterns the maintainers intended. Integration becomes dramatically easier and less error-prone because the agent is working from ground truth, not guesses.
What Agents Can Do With This
Integrate with any library correctly the first time — "How do I set up authentication with this SDK?" gets answered from the actual code, not outdated docs or training data. Your agent sees the real constructors, the real config options, the real error types.
Migrate between versions without the guesswork — Point your agent at both the old and new version of a library. It can diff the actual implementations and generate a migration plan that accounts for every breaking change.
Debug issues across repository boundaries — When a bug spans your code and a dependency, your agent can read both codebases and trace the issue to its root cause — even into libraries you've never opened.
Generate integration code that actually works — Instead of producing plausible-looking code that fails at runtime, your agent writes integration code based on the real API surface: actual method names, actual parameter types, actual return values.
Evaluate libraries before committing — "Should we use library A or B?" Your agent can analyze both implementations, compare their approaches to error handling, test coverage, and architectural quality, and give you a grounded recommendation.
Onboard to unfamiliar codebases in minutes — Point your agent at any repo and ask how things work. It answers from the code itself, with file paths and line numbers, not from memory that may be months out of date.
Features
Agent-native context — Purpose-built for coding agents. Returns the exact context an AI needs to understand, modify, and reason about code.
Architectural truth — Goes beyond keyword search. Understands how components connect, why decisions were made, and where the real complexity lives.
Any repo, any scale — From weekend projects to massive monorepos. Public and private repositories, any Git host.
Exact source citations — Every claim traced back to specific files and line numbers. No hallucination, no hand-waving.
Configuration
Environment Variables
Variable | Description | Default |
| API key from instagit.com | Auto-registers anonymous token |
| Custom API endpoint | Production API |
Authenticated Usage
Sign up at instagit.com for higher rate limits and faster analysis:
{
"mcpServers": {
"instagit": {
"command": "npx",
"args": ["-y", "instagit@latest"],
"env": {
"INSTAGIT_API_KEY": "ig_your_api_key_here"
}
}
}
}Anonymous Usage
No API key required — the server automatically registers an anonymous token on first use. Anonymous tokens are stored in ~/.instagit/token.json and shared with the Python MCP server.
Tool: ask_repo
Analyze any Git repository with AI.
Parameters:
Parameter | Type | Required | Description |
| string | yes | Repository URL, shorthand ( |
| string | yes | What to analyze or ask about the codebase |
| string | no | Branch, commit SHA, or tag (default: repository's default branch) |
Example prompts:
"Explain the architecture and main components"
"Review the authentication implementation for security issues"
"How would I add a new API endpoint following existing patterns?"
"What would it take to upgrade from React 17 to 18?"
Requirements
Node.js 18+
License
MIT — Copyright (c) 2026 Instalabs, LLC
Learn more at instagit.com
Available Tools
1 toolask_repoA
Analyze any Git repository with AI. Point it at a repo and ask questions about the codebase.
Example prompts by use case:
Understanding Architecture:
repo: "nginx/nginx", prompt: "How does nginx handle concurrent connections? Walk through the event loop, worker process model, and connection state transitions."
Integration and API Usage:
repo: "hashicorp/terraform", prompt: "How do I implement a custom provider? What interfaces does the SDK expose, how are CRUD operations mapped to the resource lifecycle?"
Debugging and Troubleshooting:
repo: "docker/compose", prompt: "How does Compose resolve service dependencies and startup order? What happens with depends_on and health check conditions?"
Security Review:
repo: "redis/redis", prompt: "Review the ACL security model. How are per-user command permissions enforced, and how does AUTH prevent privilege escalation?"
Code Quality and Evaluation:
repo: "vitejs/vite", prompt: "How does Vite's plugin system compare to Rollup's? What are the Vite-specific hooks and tradeoffs?"
Deep Technical Analysis:
repo: "ggml-org/llama.cpp", prompt: "How does the KV cache work during autoregressive generation? How are past key-value pairs stored, reused, and evicted?"
Migration Planning (with ref — Pro/Max plans only):
repo: "mui/material-ui", ref: "v4.12.0", prompt: "Document the Button component's full API surface — every prop, its type, default value, and behavior."
repo: "mui/material-ui", ref: "v5.0.0", prompt: "Document the Button component's full API surface — every prop, its type, default value, and behavior." (Compare both results to build a migration guide between v4 and v5)
repo: "kubernetes/kubernetes", ref: "release-1.29", prompt: "How does the scheduler's scoring and filtering pipeline work for pod placement?"
Ask detailed, specific questions — the tool returns real function signatures, parameter types, return values, and source citations with exact file paths and line numbers.
If you hit a rate limit (429), the user's monthly token credits are exhausted — they can wait for the reset or upgrade their plan. Free-tier repos larger than 2 GB will be rejected with a 413 error; suggest upgrading to Pro or Max for unlimited repo size.
| Name | Required | Description | Default |
|---|---|---|---|
| repo | Yes | Repository to analyze. Accepts GitHub URLs (https://github.com/owner/repo), shorthand (owner/repo), GitLab/Bitbucket URLs, or any public Git URL | |
| prompt | Yes | What to analyze or ask about the codebase | |
| ref | No | Branch, commit SHA, or tag to analyze (default: repository's default branch) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing important behavioral traits: rate limits (429 errors), token credit exhaustion, free-tier size limitations (2GB repos rejected with 413), upgrade options, and detailed information about the nature of returned results (function signatures, parameter types, return values, source citations). It doesn't mention authentication requirements or data retention policies.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately front-loaded with the core purpose, but becomes quite lengthy with extensive examples and operational details. While all content is valuable, it could be more concise by summarizing example categories rather than listing numerous specific prompts.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of an AI-powered code analysis tool with no annotations and no output schema, the description does well to cover purpose, usage examples, behavioral constraints, and operational limits. However, it doesn't describe the output format or structure beyond mentioning it includes 'function signatures, parameter types, return values, and source citations'.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description doesn't add significant parameter semantics beyond what's already in the schema, though it provides extensive examples showing how parameters are used together in realistic scenarios.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Analyze any Git repository with AI' with specific examples showing it answers questions about codebases. It provides a clear verb ('analyze') and resource ('Git repository') with no siblings to distinguish from.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool through extensive example prompts categorized by use case (Understanding Architecture, Integration and API Usage, etc.). However, it doesn't explicitly state when NOT to use it or mention alternatives since there are no sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
v0.1.7- First observed
ask_repo
TDQS
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'ask_repo' has a clearly defined and distinct purpose: analyzing Git repositories with AI-driven questions.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'ask_repo' follows a clear verb_noun pattern, and there are no other tools to create inconsistency.
A single tool is too few for a server named 'instagit' that implies Git-related functionality. While the tool is powerful, the scope feels thin; typical Git servers would include tools for operations like cloning, branching, or committing, not just AI analysis.
The tool surface is severely incomplete for a Git-focused server. It only provides AI-based repository analysis, missing essential Git operations such as repository management, file operations, or version control actions, which are core to the inferred domain.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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