"The Movie Database" matching MCP servers:

Workspace-aware MCP server for the yudao/ruoyi-vue-pro ecosystem that helps AI coding tools identify backend, frontend, and database configurations so generated code lands in the right project structure.

A production-packaged Model Context Protocol server for coding agents that routes large file, git, web, database, and other tasks through token-budgeted tools and workflows.

MCP server for the AIMFP paradigm, enabling AI assistants to manage functional programming projects with persistent database state, directive compliance, and task tracking via JSON-RPC tools.

A local vector database system that provides LLM coding agents with fast, efficient semantic search capabilities for software projects via the Message Control Protocol.

Enables Puck (or any MCP client) to start, inspect, list, cancel, and continue durable Factory Droid tasks on the same host, with idempotent request keys, workspace locking, autonomy/reasoning control, and live authenticated model discovery. It runs without a UI, database, or daemon and inherits the service user's existing Droid CLI login instead of collecting credentials.

Connect an AI assistant to aCelery, describe the app you want in plain words, and it writes the app, creates its database, runs it on your phone, checks for errors and fixes them — while you watch.

Indexes GitHub repositories and provides living documentation context to coding agents, enabling them to ground work in actual repository conventions and propose doc updates.

MCP server that lets multiple coding-agent sessions on the same machine discover each other and collaborate through a shared SQLite database.

Enables autonomous software-engineering tasks in a repository by coordinating filesystem, GitHub, database, and CI/CD tools through MCP, with a deterministic safety boundary for permissions, validation, timeouts, duplicate protection, and test verification.

Enables AI coding agents to access and update a shared local database for tracking pull requests and work items across repositories.

Indexes local Python code into a Neo4j graph database to provide AI assistants with deep code understanding and relationship analysis. Enables querying code structure, dependencies, and impact analysis through natural language interactions.

An interactive coding agent and MCP server that provides access to various AI models via the Tsinghua University lab proxy API. It enables users to inspect files and execute shell commands within their local directory using models like DeepSeek, GLM, and Qwen.

A code understanding and generation system designed for Galacean Engine that helps LLMs comprehend the engine architecture and generate high-quality engine code based on user intent.

Plori runs AI agents in Workspaces. A Workspace is a project folder in the cloud. Several agents can work in it at the same time, each on its own copy of the files, and Plori merges their work back into the folder. Every version of the files is kept.

Enables structured, step-by-step problem-solving through dynamic thinking processes that can be revised, branched, and adjusted as understanding deepens. Supports breaking down complex problems into manageable steps with the ability to revise previous thoughts and explore alternative reasoning paths.

Provide a human vision statement. The AI autonomously researches, audits your codebase, generates specs and plans, implements, and validates — with bi-directional verification at every junction to ensure nothing is missed or invented.

An MCP server that allows coding agents to search the Swift Package Index and retrieve GitHub READMEs without requiring an API key. It features comprehensive search filters for stars, platforms, and licenses, providing token-efficient responses optimized for LLM comprehension.

Exposes the four Ejentum cognitive harnesses (reasoning, code, anti-deception, memory) as MCP tools any agentic client can call. Drop-in scaffolding that catches LLM failure modes like sycophancy, hallucination, and reasoning shortcuts.

This server lets MCP clients delegate coding tasks to a locally installed Qwen Code CLI, enabling second opinions, parallel work, and clean separation from the main context.

AI code review against your team's own rules from your repository, before the pull request: two tools, review_changes and get_team_rules. Closed-source binary distributed via GitHub Releases; published in the official MCP Registry as dev.reviewgate/reviewgate.