"A tool for inspecting and debugging Model Context Protocol (MCP) servers" matching MCP servers:

A Python-based server for managing and analyzing code context for AI-assisted development. Provides real-time code analysis, project structure analysis, dependency tracking, and secure API endpoints.

Provides intelligent error detection and debugging capabilities across multiple programming languages with real-time monitoring of build, lint, runtime, console, and test errors. Offers AI-enhanced error analysis with automated resolution suggestions and context-aware debugging.

Enables querying Odoo's model registry (fields, inheritance, methods) without a database connection by loading the registry in-memory.

A tool discovery MCP server that integrates with Azure DevOps wikis and code-graph-rag to match natural language goals to tools, enabling tool composition and proxy testing.

Integrates the Atlassian Rovo Dev CLI with the Model Context Protocol, allowing AI assistants to perform deep code analysis using Rovo Dev's large context capabilities. It enables features like repository-wide queries, file-specific analysis, and specialized coding modes through a standard MCP interface.

A task-aware context compression layer for Agent workflows, RAG pipelines, and AI Coding assistants, reducing noisy logs, retrieval chunks, and code context into high-signal LLM inputs via CLI, Python SDK, and MCP.

Enables AI coding agents to automatically compile the most relevant slice of a repository into context for a given task.

Enables agents to rank and retrieve source files most relevant to a coding task, exposing a pack_context tool that supplies ranked context.

An intelligent MCP server that acts as an AI tech lead for coding agents, providing expert validation, impact analysis, and strategic guidance before code changes are made.

A local MCP server that indexes a repository into line-range chunks and serves compact evidence packets to cut token usage when exploring or debugging code.

MCP server that helps AI coding agents understand a repository by providing lightweight tree/map, code search, and token-budgeted context packing tools without dumping the entire monorepo into the prompt.

Enables AI-driven semantic code search using Windsurf's reverse-engineered SWE-grep protocol to query local codebases with natural language. It executes local search tools like ripgrep and tree-node-cli to return relevant file paths and line ranges to MCP-compatible clients.

An MCP server that provides dynamic codebase context to Claude Code through tools like hybrid search, recent changes, and symbol definitions, enhancing AI-assisted coding with local RAG.

A self-contained, in-memory graph database for AI Agents. Provides semantic code understanding through the Model Context Protocol (MCP).

Provides AI assistants with real-time visibility into your codebase's internal libraries, team patterns, naming conventions, and usage frequencies to generate code that matches your team's actual practices.

MCP server that inspects local codebases and generates source-grounded context packs for coding agents, with tools for repository search, symbols, dependencies, Git analysis, and context generation.

A local semantic memory and code-indexer that uses AST parsing for structural understanding and persists architectural decisions to help AI assistants bypass context window limits.

A Model Context Protocol (MCP) server designed to easily dump your codebase context into Large Language Models (LLMs).

Provides codebase indexing and retrieval tools that give AI agents token-efficient, query-relevant context packages (symbols, imports, and dependencies) instead of scanning entire repositories.

Provides persistent memory and a codebase knowledge graph for AI coding assistants, enabling shared context across multiple tools like Claude, Cursor, and ChatGPT, with significant token reduction.