"Multi MCP (Model Context Protocol) implementation or management" 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.

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

Provides AI agents with secure, read-only file system access to analyze and understand project codebases, enabling multi-repository context aggregation and cross-project code tracing.

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.

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).

Enables AI-driven semantic code search by leveraging Windsurf's reverse-engineered protocol to perform multi-round local searches using natural language. It automatically executes bundled ripgrep and file operations to return relevant code snippets and file paths to MCP-compatible clients.

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.

An MCP server that acts as a context-aware code retrieval broker for AI coding agents, enforcing backpressure and token budgeting to prevent context window overflow.

Enables LLMs to efficiently read, write, and refactor code using precise AST-based operations, reducing token usage and context window waste.

A local-first, agent-agnostic MCP server that provides semantic search, persistent memory, and automated code review capabilities for development workflows. It leverages the Auggie SDK to offer advanced tools for codebase indexing, implementation planning, and deterministic static analysis.

Enables coding agents to retrieve token-budgeted, source-hashed code context from explicitly registered local repositories via read-only MCP tools, reducing broad repository crawling while providing structure, symbols, and impact slices.

Local code context engine for Codex App/CLI that indexes code with SQLite/sqlite-vec for hybrid search and context packing to provide relevant files, line numbers, snippets, and paths.

Exposes CodeGraph capabilities for multiple local repositories to MCP-compatible AI coding assistants, enabling multi-repo code exploration and cross-repo symbol tracing.

Provides AI coding agents with structured Git repository context including project state, code structure, activity, and risk analysis without modifying or uploading code.