Best Codebase AI Skills & MCP Servers

Your AI agent has read your codebase but still can't safely change it. unerr is a local guardrail that hands the agent the call graph and your rules at the moment it edits.

Reduce Claude Code token usage by 70-90% with cross-session memory and four-layer optimization: semantic codebase indexing, PostToolUse tool compression, turn summarization, and session consolidation. MCP server powered by Ollama — zero API calls, runs en

MCP Server for persistent code indexing. Gives AI assistants (Claude, Gemini, Copilot, Cursor) instant access to your codebase. 50x less context than grep.

AI-powered MCP server for codebase navigation and LLM prompt optimization

Persistent intelligence infrastructure for agentic development

MCP server that gives AI agents (Claude, Cursor, Copilot) deep understanding of your codebase patterns, conventions, and architecture. Query patterns, security boundaries, call graphs in real-time.

Lint your AI agent context files, MCP server configs, and session data against your actual codebase

Bounded conventions map and local-pattern discovery for AI coding agents. Local-first MCP server with AST-backed hybrid search.

MCP server for semantic code search and codebase analysis — Treesitter parsing, AST extraction, and embedding-based indexing for any LLM that supports MCP

MCP server & CLI: codebase index + cache for Claude Code, Cursor, OpenAI Codex, Google Antigravity/Gemini. Model Context Protocol tools (codetree_read, codetree_search, …), hooks on Claude, rules on other IDEs. Token savings, SQLite index, symbol search,

MCP server that gives Claude, Cursor, and Gemini long-term memory across sessions. Your AI remembers your codebase, decisions, and preferences — across tools and machines.

Local-first MCP server giving AI coding agents fast, structured, and semantic context over any codebase.

Zero-config codebase context for AI agents. One command generates a ~250-token navigation map and auto-configures Claude Code, Cursor, and Aider.

Local-first AI-powered codebase analysis tool for stdio or HTTP transports using environment-supplied provider credentials.

MCP server for AI-powered codebase indexing with auto-save checkpoints, incremental updates, real-time progress tracking, and index verification tools

Codebase graph analysis for AI agents — AST + call graph + type graph + hybrid semantic search via MCP

Your codebase never forgets. Persistent, structured memory for AI coding agents.

Graph-powered code intelligence for AI agents. Index any codebase, query via MCP or CLI.

Repository scanner that builds a structural map of a codebase (functions, imports, exports, dependencies) and adds file descriptions of what the file does,, so anagentic AI can just read the map and know the project context

Model Context Protocol (MCP) server for advanced GitHub repository analysis and code discovery. Provides AI assistants with powerful tools to search, analyze, and understand codebases across GitHub.

Mneme — the memory layer for your codebase. Knows the WHY, the WHAT, the WHERE-IT-BREAKS.

Complete codebase intelligence — loads treesitter, symbol, outline, callgraph, depgraph, refs.

Run 400B+ open coding models on your codebase without the hardware bill. Ollama Cloud first — OpenAI, Anthropic, and Gemini when you need them.

Code-aware browser testing agent — reads your codebase, understands functionality, tests every element, reports with screenshots. Works as MCP server for Cursor/Claude Code or standalone CLI.