Vectorize MCP Server

The Vectorize MCP Server bridges the powerful vector‑search and text extraction capabilities of Vectorize with AI assistants that speak Model Context Protocol. By exposing Vectorize’s APIs as an MCP server, developers can seamlessly embed semantic search, document understanding, and retrieval‑augmented generation into their AI workflows without wrestling with raw HTTP requests or authentication plumbing. This abstraction is especially valuable for teams that already rely on Vectorize pipelines for indexing and transforming unstructured data, allowing them to leverage those assets directly from an AI assistant.

At its core, the server offers a set of MCP resources that wrap Vectorize’s pipeline execution and vector retrieval endpoints. When an AI assistant receives a query, it can issue a vector search request through the MCP interface; the server translates this into a call to Vectorize’s search API, returning ranked documents or embeddings that the assistant can then use to answer user questions. Similarly, text extraction tasks—such as pulling structured fields from PDFs or converting raw documents into searchable vectors—are exposed as simple tool calls. Because the MCP layer handles authentication via environment variables, developers can keep credentials secure while still granting fine‑grained access to specific pipelines.

Key capabilities include:

• Semantic Retrieval: Execute vector queries against a Vectorize pipeline and receive contextually relevant results in a single call.
• Document Extraction: Trigger pre‑configured extraction workflows that transform raw files into structured data or embeddings.
• Pipeline Management: Use the MCP interface to invoke different Vectorize pipelines by ID, enabling dynamic switching between models or preprocessing steps.
• Secure Integration: Environment‑based credentials keep tokens out of code, reducing the risk of accidental leaks.

Typical use cases span a wide range of AI‑driven applications. A customer support bot can query Vectorize for the most relevant knowledge‑base articles, while a legal assistant can extract clauses from contracts stored in a vector store. In research settings, an AI tutor might retrieve scholarly papers that match a student’s query and summarize key points. Because the server presents these operations as standard MCP resources, any assistant that understands MCP—Claude, GPT‑4o, or custom agents—can tap into Vectorize without bespoke adapters.

The integration workflow is straightforward: configure the MCP server once, then use normal MCP tool calls from your assistant. The assistant sends a request to the server, specifying the pipeline ID and query text; the server handles authentication, forwards the request to Vectorize, and streams back results. This decoupling of data access from AI logic means developers can focus on crafting prompts and handling responses, while the MCP server manages all interactions with the external vector service.

In summary, the Vectorize MCP Server delivers a clean, secure, and developer‑friendly bridge between advanced vector search capabilities and AI assistants. By exposing Vectorize pipelines as MCP resources, it empowers teams to enrich their conversational agents with semantic search and document extraction without sacrificing security or introducing unnecessary complexity.