"A tool or assistant to explore and debug my codebase" matching MCP servers:

Enables read-only searching, reading, and status inspection of a local Git-backed Markdown wiki via a minimal MCP toolset, without any network or write operations.

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.

Record debug sessions, commands, failed attempts, and successful fixes in local SQLite storage.

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

Local-first debug memory for MCP clients. Record incidents, commands, failed attempts, successful fixes, diagnostics, and searchable debugging history in SQLite.

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.

Structures debugging processes into a persistent knowledge graph, enabling problem decomposition, hypothesis testing, and reusable solution discovery.

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.

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

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.

Enables Claude to act as a personal assistant in Telegram by providing tools to read new messages, send replies, and manage tasks/reminders/notes in a Supabase-backed memory.

MCP server that connects Claude to a Telegram personal assistant, enabling message processing, task management, and responses via scheduled workflows.

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.