Chromadb FastAPI MCP Server

Overview

The Chromadb FastAPI MCP server bridges a vector database—Chroma—with the Model Context Protocol, enabling AI assistants to query and manage embeddings directly from their native toolchains. By exposing Chroma’s rich collection, document, and search APIs through a lightweight FastAPI interface, the server solves the common pain point of integrating vector search into conversational AI workflows without custom SDKs or complex data pipelines. Developers can therefore focus on building higher‑level interactions while the MCP server handles embedding generation, persistence, and retrieval behind a standard HTTP/SSE contract.

At its core, the server offers CRUD operations for collections and documents, mirroring Chroma’s own API surface. Collections can be created, inspected, or deleted; documents are added, queried with similarity search, retrieved by ID, updated, or removed. These operations are automatically discoverable by any MCP‑compatible client that supports Server‑Sent Events (SSE), such as Cursor or Claude Desktop via the bridge. The integration is seamless: once the server runs, a client simply points to , and all available tools—“create collection”, “add documents”, “search” and more—are exposed as first‑class actions that can be invoked within a conversation.

Key capabilities include:

• Embedding Generation: The server uses the OpenAI API (configured via ) to transform text into vector embeddings on demand, ensuring that the most up‑to‑date models power similarity queries.
• Persistent vs. Ephemeral Storage: By toggling , developers can choose between an in‑memory, transient store for rapid prototyping or a disk‑backed persistent client that retains data across restarts.
• Rich Metadata Support: Documents can carry arbitrary key/value pairs, enabling advanced filtering or context‑aware retrieval within an AI assistant’s dialogue.
• Scalable Search: Chroma’s efficient ANN index is leveraged for fast, high‑dimensional similarity searches, allowing assistants to surface the most relevant documents in milliseconds.

Typical use cases span from knowledge‑base agents that answer domain questions, to content recommendation engines embedded in chat interfaces, and even real‑time code completion tools that pull context from a vector store of code snippets. In each scenario, the MCP server eliminates boilerplate by exposing a declarative tool set that AI assistants can call without custom code, accelerating iteration cycles for data‑centric applications.

What sets this implementation apart is its minimal footprint and tight integration with the broader MCP ecosystem. By packaging Chroma inside a FastAPI app, it inherits robust tooling—automatic OpenAPI docs, hot‑reload during development, and straightforward deployment to cloud or edge environments. The server’s SSE endpoint guarantees low‑latency, event‑driven communication, making it an ideal backbone for latency‑sensitive AI workflows that need to fetch or update embeddings on the fly.