"Maps" matching MCP servers:

On-demand micro-mutation sandbox for AI test verification that maps weaknesses in unit tests by running isolated mutation testing via the Model Context Protocol.

A local Git intelligence MCP server that provides deep repository analytics including hotspots, temporal coupling, knowledge maps, churn analysis, and risk scoring for AI agents.

Enables Claude and Claude Code to scan local repositories, generate interactive architecture maps, and trace transitive callers to assess change impact.

Deterministic navigation maps over code AND markdown for AI agents: one MCP server, two lenses (tree-sitter TS/JS/Python + markdown). Map a whole project, read one function or one doc-section, ~99% less context. Merges codelens + docslens.

An MCP server that maps any repository into architecture diagrams, providing tools for scanning code facts, building import graphs, and generating Mermaid/DOT diagrams, plus architecture rule enforcement for CI.

Provides role-aware repository maps for LLM agents by analyzing codebases and ranking files within a token budget.

A local Git intelligence MCP server that provides deep repository analytics including hotspots, churn, knowledge maps, and risk scoring, all computed from commit history without data leaving your machine.

Extracts deterministic architecture maps from codebases for AI agents, enabling queries about blast radius, routes, security findings, and production readiness without sending code anywhere.

AI reads your codebase like a book - one page at a time. Pharaoh gives it the index and a map of how every chapter connects.

Analyzes codebases from local directories, GitHub, and Azure DevOps, providing intelligent context to AI coding assistants through repository structure, critical files, and semantic maps.

Maps function and component dependencies in Haskell and ReScript codebases, providing LLMs with a real dependency graph for accurate test generation and change-impact analysis.

Provides AI coding agents with durable architecture memory for repositories by generating structured project maps of responsibilities, relationships, and risks.

An MCP server that generates ranked, token-budgeted code structure maps using Tree-sitter AST analysis and PageRank, enabling AI agents to quickly understand unfamiliar codebases.

Enables decoding minified production stack traces to original TypeScript source locations using source maps, with tools for retracing, code context retrieval, and sourcemap auditing.

Local-first code context CLI + MCP server: discover, index, and search repos; generate task-aware context packs, code maps, agent harnesses, and PR-review/merge-readiness gates for coding agents and reviewers.

Enables scanning repositories for hardcoded service URLs and clients, and building service-to-service API dependency maps.

Enables LLMs to access AST-based code structure maps, token-efficient skeletons, and analyze diff impact and blast radius for regression testing.

AI-assisted COBOL to modern stack bridge that parses COBOL programs, maps to Python/Java/Go, generates test harnesses, and plans migration for legacy mainframe modernization.

Enables AI coding agents to build and query local repository memory, returning ranked files, context packs, relationship maps, semantic recall, and feedback-tuned search without uploading code.

An MCP server for code intelligence that maps source code into a knowledge graph, enabling AI tools to understand architecture and structure rather than just individual files.

An MCP server for inspecting and editing StarCraft II maps and mods directly, enabling file-level manipulation of map components, triggers, scripts, and more without the Galaxy Editor UI.

Enables proof-backed code audit via MCP tools for AST-based code maps, symbol lookup, callers, audit commands, and persistent verdict memory.

Enables users to read code with selection-based floating explanations, file-purpose summaries, interactive code mind maps, and source navigation, available as an independent web app or Codex plugin. It supports local-only reading workflows, model selection, caching, and project context without sending chat messages.

Enables agent harnesses to explore a codebase in read-only mode, create line-anchored hierarchical execution maps, retrieve them as context, and verify map drift against real code or CI.

Enables coding agents to perform deterministic, model-free repository analysis and patch operations—tree-sitter repo maps, symbol/reference lookup, and SEARCH/REPLACE parsing/validation—via a CLI or MCP stdio server, without ever calling a language model.

Enables local LLM tools and agentic IDEs to inspect repository token maps, retrieve clean token-bounded codebase snapshots, and audit code for hardcoded secrets via MCP over stdio.

Generates optimized repository maps and enables identifier search with graph-ranking, designed for efficient LLM context caching and codebase navigation.

Provides Claude with persistent, semantic knowledge of a codebase across sessions by maintaining a local knowledge base of structural maps, compressed file summaries, and insights.

AI code impact analysis MCP server that monitors file changes, maps dependency graphs, detects cascading breakage, and gates builds before damage spreads.

An MCP server that maps code symbol relationships and provides tools for impact analysis, caller lookup, and duplicate detection to help coding agents understand codebases efficiently.