"Vector Database" matching MCP servers:

A marketplace of 13 specialist AI agents for coding assistants, offering tools to hire agents, run scripts, audit code, and manage vector stores with async job execution.

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.

A server component of the Model Context Protocol that provides intelligent analysis of codebases using vector search and machine learning to understand code patterns, architectural decisions, and documentation.

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

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 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.

Zettelkasten-based persistent memory for AI coding agents. Auto-saves atomic knowledge cards with \[\[bidirectional links]] after tasks and auto-recalls before new ones. No vector DB — plain markdown files with git sync. Works as Claude Code plugin or MCP server for Cursor, VS Code Copilot, Codex, and Windsurf.

A graph-based MCP server that provides AI coding agents with persistent memory to store patterns, track complex relationships, and retrieve knowledge across sessions. It leverages graph structures to handle temporal queries and relational paths that traditional vector stores often miss.

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 for AI coding agents that builds a complete code structure graph and semantic vector index, enabling fast querying of code entities, relationships, and impact analysis.

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.