Vectorize MCP Server
by vectorize-io
A Model Context Protocol (MCP) server implementation that integrates with Vectorize for advanced Vector retrieval and text extraction. It allows users to perform vector search, text extraction, and deep research using the Vectorize platform.
Last updated: N/A
What is Vectorize MCP Server?
The Vectorize MCP Server is an implementation of the Model Context Protocol that connects to the Vectorize platform. It provides functionalities for vector retrieval, text extraction, and deep research, enabling users to leverage Vectorize's capabilities within other applications and services.
How to use Vectorize MCP Server?
The server can be installed using npx or through VS Code. It requires setting environment variables for Vectorize Organization ID, Token, and Pipeline ID. Once installed, it can be configured in various environments like Claude, Windsurf, Cursor, and Cline. It provides tools for retrieving documents, extracting text, and performing deep research using JSON configurations.
Key features of Vectorize MCP Server
Vector Retrieval
Text Extraction and Chunking
Deep Research
Integration with Vectorize
Configuration via JSON
VS Code Installation Support
Use cases of Vectorize MCP Server
Semantic search over documents
Automated text extraction from various file formats
Generating research reports based on a query
Integrating Vectorize capabilities into AI assistants
Building knowledge bases
FAQ from Vectorize MCP Server
What is the Model Context Protocol (MCP)?
What is the Model Context Protocol (MCP)?
The README doesn't explicitly define MCP, but it's implied to be a protocol for interacting with model contexts, in this case, Vectorize.
What are the required environment variables?
What are the required environment variables?
The server requires VECTORIZE_ORG_ID, VECTORIZE_TOKEN, and VECTORIZE_PIPELINE_ID to be set.
How do I install the server?
How do I install the server?
You can install it using npx, VS Code one-click install buttons, or manual installation through VS Code settings.
What tools are available?
What tools are available?
The server provides tools for retrieving documents, extracting text and chunking, and performing deep research.
Where can I find the official API documentation?
Where can I find the official API documentation?
The official API documentation for retrieval, extraction, and deep research can be found at https://docs.vectorize.io/api/api-pipelines/api-retrieval, https://docs.vectorize.io/api/api-extraction, and https://docs.vectorize.io/api/api-pipelines/api-deep-research respectively.