MaiMap – MCP Server for Codebase Understanding

TL;DR

TL;DR: MaiMap is an MCP server that parses your distributed codebase using AST analysis and LLM inference, then serves a queryable architecture map to any compatible LLM — so you can ask questions like “which services depend on the payment module?” in plain English.

What Is MaiMap?

LLMs hit context-window limits on large, multi-repo codebases. MaiMap solves this by continuously parsing your codebase — extracting services, API call graphs, data flows, and bounded contexts — and exposing that map through the Model Context Protocol. You connect your LLM (Claude, GPT-4o, etc.) once, then ask architecture questions that would normally take days of code archaeology.

The vendor describes it as: “Make your LLM understand your entire distributed system.”

How It Works

Step 1: Analyze Your Repositories

Connect your Git organization so MaiMap can import and parse your repos. The parser reads source files using language-specific AST analysis rather than simple grep or regex, so it understands actual call graphs, not just string matches.

Step 2: View Interactive Architecture Graphs

Results appear as an interactive service-map — nodes represent services or modules, edges represent API calls and async events. You can trace the data path from a user sign-up event all the way through downstream side-effects.

Step 3: Connect via MCP

An MCP server exposes the architecture map to any LLM that speaks the Model Context Protocol. This means you do not change your existing AI tooling — you point it at MaiMap’s MCP endpoint and it gains access to the full context.

Step 4: Ask Your AI

Now you can query the codebase in natural language:

• “Which services depend on the payment processing module?”
• “Show me the data flow between auth-service and user-management.”
• “Which endpoints in auth-service query the user_permissions table in Postgres?”
• “Trace the series of events when a user signs up.”

Supported Languages

At launch, MaiMap supports AST-based analysis for:

• Java
• TypeScript / NodeJS
• Python
• Go

More languages are planned. The architecture is designed to be language-extensible, with a separate analysis module per language.

Practical Evaluation Checklist

• MCP protocol support — yes, first-class MCP server integration
• Multi-repo / distributed systems — designed for this from day one
• Static analysis + LLM — combines AST parsing with LLM inference for richer context than grep-based tools
• Interactive graph output — yes, interactive service-map visualization
• Deployment options — on-premise option planned for enterprise security requirements
• Pricing — hosted and on-premise options planned; exact pricing TBD

Security Notes

• On-premise deployment is planned for teams that cannot send code to a hosted service
• No public documentation yet on authentication, data retention, or audit logging — check the docs at maimap.dev/docs before evaluating for regulated environments

FAQ

Q: Does MaiMap work on a monolith, or only microservices? A: It works on both. The docs state it handles “microservices and large monolithic codebases.”

Q: What LLM models are supported? A: Any LLM that can consume the Model Context Protocol. MaiMap acts as a context provider, not a model host.

Q: Is there a self-hosted option? A: Yes — an On-Premise plan is listed with custom pricing, including unlimited repositories and priority support.

Q: How does this compare to asking an LLM to read the codebase directly? A: A raw codebase exceeds context limits on large projects. MaiMap pre-digests the architecture into a structured, queryable map, so the LLM sees only relevant context — reducing hallucinations and token waste.

Source and Accuracy Notes

• Project page: maimap.dev
• Live demo: demo.maimap.dev
• HN launch thread: news.ycombinator.com/item?id=46943437
• License: not yet publicly stated — verify before use in commercial projects
• Source last checked: 2026-08-07