Servidores MCP

Connects AI agents to ChromaDB vector databases for semantic search and retrieval augmented generation (RAG) workflows. Supports creating collections, upserting documents with embeddings, querying by semantic similarity, and managing metadata filters. Ideal for knowledge base and document retrieval applications.

Official dbt Labs MCP server for analytics engineering workflows. Lets AI agents run dbt commands (build, run, test), inspect models and lineage via the dbt project, and query the Semantic Layer and Discovery API in dbt Cloud. Useful for transforming data, validating models, and answering metric questions grounded in governed definitions.

MCP server for the Databricks Data Intelligence Platform. Enables AI agents to run SQL against the Unity Catalog, inspect schemas and tables, and manage and monitor jobs, bringing lakehouse data and workflows into AI-powered development tools.

Apache Solr's MCP server connects agents to search collections and query workflows for schema discovery, relevance investigation, and search application development.

Google's MCP Toolbox provides secure, ready-made and custom tools for database queries, schema operations, and semantic search in agent workflows.

Pinecone's official MCP server connects AI assistants to Pinecone vector databases for retrieval-augmented generation (RAG) workflows. Lets agents create and configure indexes, upsert and embed documents, and run semantic searches over vector data — all from natural language, without leaving the editor or chat.

Prisma's official MCP server that lets AI tools manage Prisma ORM projects and Prisma Postgres databases through natural language. Provides both a local server for working with a project's Prisma schema and migrations, and a remote server for provisioning and managing Prisma Postgres databases. Useful for scaffolding models, generating and applying migrations, and running database workflows from an AI coding assistant.

MCP server for interacting with a Tinybird Workspace from any MCP client. Lets AI agents explore data sources, call published API endpoints, run SQL queries over real-time data, and push datafiles, making Tinybird's analytics readily available to LLM-driven workflows.