"A search for maps or map-related resources" matching MCP servers:

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

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.

An experimental MCP server that explores neuroscience-inspired approaches to software development analysis, featuring associative memory, context-aware caching, and cross-language API detection.

Enables code analysis of JavaScript, TypeScript, TSX, Python, and Rust files using Tree-sitter, providing symbol listing, definition/reference lookup, AST queries, LSP-style features, and SQLite indexing for efficient cross-file searches.

An MCP server that reduces AI coding agent token usage by 80-99% through a queryable knowledge graph of code, change tracking, and persistent memory across sessions.

Enables mapping JavaScript error stack traces back to original source code, extracting context information to help developers locate and fix issues.

A Model Context Protocol server that helps programmers understand code by providing explanations, tech stack analysis, and best practice suggestions through prompt templates.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

Enables semantic code search across indexed code folders using vector embeddings, with support for multiple embedding providers and automatic file watching. Provides an admin UI and integrates with MCP clients for natural language code queries.

Semantic + lexical code search as an MCP server. Agents query in natural language and get back ranked file:line ranges to read precisely.

Enables blazingly fast file and content searching in large codebases using ripgrep, with intelligent filtering, fuzzy finding, and directory tree visualization while respecting .gitignore and avoiding common bloat directories.

Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.

Clones a Git repo into a temp directory so MCP clients can search and read files from another repo for context.

Enables AI agents to perform semantic code search across entire codebases using natural language queries. Provides fast indexing and ranked search results with line numbers and file paths through the Seroost search engine.

Enables AI coding assistants to semantically search codebases by meaning rather than exact text, with zero external daemons and fully local embeddings. Works out-of-the-box with Claude Code, Gemini CLI, Antigravity, and Cursor.

High-performance code understanding toolkit that enables batch reading of multiple files with dependency context, structural outline extraction with Java annotation awareness, and precise location of classes/methods across large codebases.

Maintains a self-updating JSON index of repository files, functions, and descriptions so agents can query relevant files instead of grepping the tree.

Provides semantic code search over codebases using local embeddings with natural language queries. Supports hybrid search, file watching, and respects .gitignore.