# Tools

> Every listed resource, editors’ picks first. The filters below can be combined.

- Search: append `?q=<keywords>` to the URL. Separate several keywords with spaces; all of them must match. Matches name, repository, tags, professions, description and README.
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- Filter by profession: append `?profession=<profession id>` to the URL. Separate several ids with commas; a resource matches when it suits any of them. Ids are listed at https://hebbmind.com/api/v1/professions
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Resources for Full-stack / Indie or ML / AI, Tagged `vector-database`: 7 items.

> - Full-stack / Indie: General software work: coding-agent workflows, code review, debugging and Git collaboration.
> - ML / AI: Model training, evaluation and LLM application development.

- [LLM Application Dev](https://hebbmind.com/es/plugin/llm-application-dev.md): [Plugin] Plugin for building LLM apps: LangGraph agents with memory and tool use, hybrid-search RAG, vector index tuning, prompt optimization and LLM evaluation. Includes 3 subagents and 8 skills.
- [Claude Context](https://hebbmind.com/es/mcp/claude-context.md): [MCP] From Zilliz: indexes your codebase as vectors in Milvus or Zilliz Cloud so coding agents find relevant code by semantic search, cutting context costs in large repos. Needs an embedding model (OpenAI, VoyageAI, Ollama and others) and a vector database.
- [Qdrant MCP Server](https://hebbmind.com/es/mcp/mcp-server-qdrant.md): [MCP] Uses the Qdrant vector database as a semantic memory layer for agents: store information and retrieve it by meaning, connecting to a remote instance or a local path, with FastEmbed generating embeddings by default. Maintained by Qdrant.
- [Redis MCP Server](https://hebbmind.com/es/mcp/mcp-redis.md): [MCP] Redis's official MCP server: read and write strings, hashes, lists, streams, JSON and other data structures in natural language, with vector indexing and search and pub/sub. Bring your own Redis instance; runs via uvx.
- [Chroma MCP Server](https://hebbmind.com/es/mcp/chroma-mcp.md): [MCP] Chroma's official MCP: lets agents create and manage collections, add documents, and retrieve them with vector search, full-text search and metadata filters. Connects to in-memory or local persistent stores, self-hosted instances or Chroma Cloud.
- [MongoDB Agent Skills](https://hebbmind.com/es/skill/mongodb-agent-skills.md): [Skill] MongoDB's official skill library covering document model design, driver connection setup, query optimization, natural-language query generation, Atlas full-text and vector search, and stream processing. Works alongside the MongoDB MCP Server.
- [Pinecone Plugin for Claude Code](https://hebbmind.com/es/plugin/pinecone-claude-code-plugin.md): [Plugin] Pinecone's official plugin: bundles the Pinecone MCP server and /pinecone skill commands to create vector indexes, upsert data, search in plain language, or build a document Q&A assistant with citations from Claude Code. Needs a Pinecone API key (free sign-up); Assistant features also need uv.