"A tool for searching through my codebase" matching MCP servers:

A Docker-based MCP server providing codebase memory and search tools. It enables storing/retrieving memories, regex search of code, and file summarization for AI assistants.

MCP server for local codebase memory: semantic indexing, hybrid search, and clean code analysis (static + LLM) with retrievable project notes, designed to run fully local on modest hardware.

Provides persistent memory for AI coding agents through the Model Context Protocol, enabling them to store and retrieve project knowledge across sessions.

An MCP server that indexes codebases into a local knowledge graph, providing 52 AI tools for semantic search, call-graph traversal, git archaeology, and impact analysis to give AI agents deep understanding of code.

Enables AI agents to index, search, and retrieve architectural documentation and store self-learning notes from codebases.

High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 159 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Provides a 'reflect' tool that creates cognitive checkpoints for AI assistants, forcing structured step-by-step reasoning through complex problems to improve accuracy and maintain context during task execution.

Serves a git-backed folder of Markdown notes over MCP so AI agents can search, read, write, move and delete them, with every write recorded as a git commit attributed to the calling client. Supports full-text section search with snippets, wikilinks and typed relations, LFS-backed searchable attachments, optimistic concurrency for multiple agents, and per-client token or OAuth 2.1 access.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

A MCP server that provides persistent memory for AI assistants, storing personal information, relationships, and observations to enable personalized and contextual conversations.

Central registry for AI tools with vector search and knowledge graph, enabling intelligent tool discovery, chaining, and execution via MCP.

Implements Anthropic's 'think' tool for Claude, providing a dedicated space for structured reasoning during complex problem-solving tasks that improves performance in reasoning chains and policy adherence.

Provides an MCP interface to the ROBOT command-line tool for OWL ontology editing, enabling operations like merging, reasoning, and conversion via natural language.

A simple MCP server that recalls and saves memories from and to Apple Notes.

MCP server that wraps codebase-memory-mcp to provide code indexing, search, tracing, and architecture analysis tools with automatic reindexing after source changes.

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 structural knowledge-graph code search, AST snippet retrieval, architecture analysis, call-path tracing, and textual code search across indexed repositories, plus an auto-started graph visualization UI.

MCP server that enables persistent, hybrid, local memory for LLM agents, with vector + BM25 search, knowledge graph, and policy-driven retention, providing token-budgeted context injection for AI assistants.

Persists Claude Code sessions to Notion as team memory, enabling queryable recall of decisions, work state, and project architecture.