"Firebase Firestore Database Information" matching MCP servers:

Enables coding agents to search and retrieve persistent repository memory created by Cliper, providing context, gaps, architecture, and timeline information via MCP tools.

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.

Enables coding agents to navigate and query source code by providing context, symbols, and call graph information through a graph index.

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 users to run shell commands with compressed output, extract information from large files, drive local opencode and Codex agents, perform browser-automation tasks, and monitor runs via a live dashboard.

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.

Enables coding agents like Cursor to access Figma design data, fetch layout and styling information, and implement designs in any framework.

https://gopls-mcp.org/ MCP server for golang projects development: Expand AI Code Agent ability boundary to have a semantic understanding and determinisic information for golang projects.

Enables MCP-compatible AI coding tools to store and recall persistent memories across sessions and projects, with keyword and semantic search backed by a local SQLite database.

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.

MCP server to provide Jira Tickets information to AI coding agents like Cursor

Bridges Large Language Models with Language Server Protocol interfaces, allowing LLMs to access LSP's hover information, completions, diagnostics, and code actions for improved code suggestions.

Enables AI coding agents to access real-time VSCode LSP diagnostics, type information, and code navigation without slow tsc/eslint checks, plus symbol renaming and file operations.

Runs as an MCP server providing file system operations, shell execution, and system information tools, allowing Claude or any MCP client to edit files and control the machine.

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.

Enables AI assistants to scaffold, edit, live-preview, and deploy full-stack apps with built-in database, email, and push notification capabilities through natural language.

A specialized toolchain that guides AI agents through a structured 'Atomic Development' workflow for building Python FastAPI and Supabase backends. It manages project scaffolding and enforces dependency-ordered generation of database models, API routes, and tests.

Enables bounded, local coding tasks to be delegated to OpenAI-compatible models with explicit working directory and read/write paths, pre-application unified-diff validation, and optional verification commands. It routes tasks by complexity and risk, while blocking sensitive changes such as auth, payment, secrets, migrations, database, deployment, or production environments.

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.

Enables reasoning agents to turn PRDs into production-ready applications by discovering and reusing existing features, UI components, database models, and templates, then planning, scaffolding, validating, and preparing Git changes for review.

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.

Provides AI coding agents with access to Figma design data through the Figma API. Enables accurate one-shot implementation of designs in any framework by translating Figma files into simplified layout and styling information.

Enables managing Linux hosts and local coding agents from an MCP client through shell commands, files, background jobs, system information, instruction and skill discovery, and optional Claude Code, Codex, or Cursor CLI conversations. It provides privileged remote access with token authentication and can run longer work in the background.