"Debugging and Navigating Code in a Project" matching MCP servers:

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Controls a running Chrome/Chromium browser via the Chrome DevTools Protocol, enabling navigation, JavaScript evaluation, tab management, and raw CDP commands through MCP tools.

Persistent memory and pattern learning for AI coding assistants via MCP.

Provides tools for managing project knowledge graphs, enabling structured representation of projects, tasks, milestones, resources, and team members.

Open-source AI code review MCP server for local git diff auditing with deterministic security rules and AI-powered analysis using any OpenAI-compatible model.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Provides AI-driven project memory management through structured prompts that help Claude parse tasks from specs, review code changes, sync with commit history, and maintain project documentation without directly accessing files.

Enables task management (create, list, update tasks with priority and status) using SQLite storage via MCP tools.

Enables Claude to directly manage the Savage in a Suit website through 31 tools for pages, events, members, testimonials, podcast episodes, and site content, with all changes logged.

Turns AI assistants into full-stack software engineers with 36 tools for cognitive reasoning, code validation, project scaffolding, and AI/IDE configuration generation across 130+ programming languages, databases, and frameworks.

Enables geospatial analysis by chaining MCP tools that resolve areas, select datasets, pull statistics, and generate insights, with session state passing data between tools.

Allows agents to lookup code snippets in your project. Uses a remote MCP server with a collection of code snippets, with support for automatic project language detection.

Enables AI assistants to surgically extract and analyze code, reducing token usage and costs by up to 200x compared to reading entire files.

Provides over 1,000 creative ways to decline requests across four categories (polite, humorous, professional, and creative). The MCP server wraps a REST API to help users craft professional rejections through natural language interactions.

Enables MCP clients to connect to a privacy-first, self-hostable workout planning and training log, allowing coaching agents to preview and apply program changes while accessing training data through OAuth-protected endpoints.

MCP server providing 29 A-share analysis skills including real-time data, capital flow, limit-up tracking, technical/fundamental analysis, backtesting, risk control, and Xueqiu portfolio tracking, enabling AI agents to execute market research and strategy tasks.

Enables Claude AI to run terminal commands, perform file operations, and manage git in a project directory via the Model Context Protocol.

An MCP server that provides CLI coding agents with persistent decision memory, codebase dependency-graph awareness, plan validation against architectural constraints, and a self-bootstrapping constraints.md file.

Aggregates multiple MCP services into a single unified interface with self-configuration capabilities, enabling dynamic addition and removal of tools via conversation.

Enables task management through natural language: create tasks, list and filter them, and update their status. It uses SQLite for persistence and supports both local stdio and remote SSE transports.