"Serena" matching MCP servers:

A coding agent toolkit that provides IDE-like semantic code retrieval and editing tools, enabling LLMs to efficiently navigate and modify codebases at the symbol level rather than working with entire files.

Provides IDE-like coding capabilities for AI agents via MCP, enabling semantic code retrieval, symbol-level editing, refactoring, and navigation across many programming languages.

Enables multiple coding-agent sessions (Claude Code, Codex, and other MCP clients) in the same repository to share a single Serena server and its language servers, instead of each session starting its own. This reduces memory and CPU use, speeds up session startup, and prevents separate instances from overwriting each other's cache.

Token-optimized Serena MCP server for AI assistants, reduces context usage by 50% with zero functionality loss.

Bridges stdio MCP clients to shared, warm per-project backend instances over streamable HTTP, so repeated client sessions skip cold starts and transparently survive backend restarts. Heartbeat leases, idle-TTL reaping, and per-request deadlines keep per-project instances alive and shared across multiple clients without hanging the caller.

Provides IDE-like semantic code retrieval and editing tools for LLMs, enabling precise code understanding and manipulation in large codebases via the Model Context Protocol.

Enables AI IDEs and coding agents to navigate code at the AST symbol level, perform surgical, collision-free refactoring, and resolve live library documentation. It also supports project onboarding memory, giving agents semantic precision without regex guesswork or heavy dependencies.

Enables MCP clients to run persistent REPL sessions for semantic code navigation and to drive the Mago language server for PHP analysis, linting, safe fixes, and scoped formatting in a disposable Symfony workspace.

Provides a single code_intel MCP tool that autonomously manages zvec-grep, CodeGraph, and Serena LSP backends to enable semantic search, architecture analysis, impact assessment, symbol references, implementations, and diagnostics for local codebases.

A project-aware AI design and code review server that uses dual reviewers via OpenRouter to catch context-blind errors in AI-generated code, optionally integrating with Serena for long-term project memory.