"Organic 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.

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.

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.

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.

Enables agents to search a local, license-aware library of tested code assets, verified bug fixes, package guidance, rules, and lessons, while providing repo maps, secret scanning, and licence checks through MCP tools over stdio.

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

Local-first code retrieval MCP server: hybrid semantic + keyword search over an AST-aware index of your codebase, with symbol definition and reference lookup, repo maps, and file tools. Runs fully offline; serves Claude Code, Cursor, and any MCP client over stdio or HTTP.

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.

Deterministic, local-first repository context for coding agents. Maps an issue, prompt, or git diff to ranked files to read first, likely test commands, and review-risk notes—no API key required.

Enables coding agents to generate question-scoped repository maps, inspect symbols and SQL lineage, and audit working-tree diffs for ripple candidates, all verified against the current Git commit.

MCP server for code comprehension indices and test maps. It answers what must be known to change a function and which tests pin its behavior, using compiler type information.

Compiles codebases into a layered knowledge graph, providing an MCP server with 16 tools for AI to navigate, understand, and analyze code before making changes.

Enables agents to generate a JSON sitemap of how a path through a TypeScript/JavaScript codebase runs, capturing the summary, areas, every step with its file, lines, description, calls and data, and the links between services. The resulting document can be opened in a viewer that draws the same map as a single page, and rebuilt in CI to keep system documentation current.

Enables AI coding agents to retrieve repository context through stdio-based MCP tools, including ranked repo maps, context packs, and live symbol search.

A navigable symbol map of your codebase for coding agents, enabling efficient code navigation and reading only relevant symbols instead of entire files.

This server provides verifiable code facts and entrypoint maps for a repository. It enables AI agents to understand the deterministic impact of code changes by providing evidence-based analysis of dependencies and usage.