data-ai MCP Server
Vector MCP Server for AI Agents - Supports ChromaDB, Couchbase, MongoDB, Qdrant, and PGVector
Discovered via github-seeds:mcp-hot and last synced 3mo ago.
1. Install the package
uvx --from vector-mcp vector-mcp
2. Add to claude_desktop_config.json
{
"mcpServers": {
"vector-mcp": {
"command": "npx",
"args": [
"vector-mcp"
]
}
}
}Config file location: ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) / %APPDATA%\Claude\claude_desktop_config.json (Windows)
Lists all collections in the vector database.
Creates a new collection or retrieves an existing one in the vector database.
Tag(s)
Adds documents to an existing collection in the vector database. This can be used to extend collections with additional documents.
Deletes a collection from the vector database.
Retrieves and gathers related knowledge from the vector database instance using the question variable.
MCP                   *Version: 1.10.0* ## Overview This is an MCP Server implementation which allows for a standardized collection management system across vector database technologies. This was heavily inspired by the RAG implementation of Microsoft's Autogen V1 framework, however, this was changed to an MCP server model instead. AI Agents can: - Hybrid search for document information (lexical/vector) - Create collections with documents stored on the local filesystem or URLs - Add documents to a collection - Utilize collection for retrieval augmented generation (RAG) - Delete collection Supports: - ChromaDB - PGVector - Couchbase - Qdrant - MongoDB This repository is actively maintained - Contributions and bug reports are welcome! Automated tests are planned ## MCP ### MCP Tools
Agent                   *Version: 1.41.0* --- ## Overview **Vector Mcp** is a production-grade Agent and Model Context Protocol (MCP) server designed to interface directly with Integrate RAG into AI Agents via MCP Server. Supports multiple Vector database technologies.. --- ## Key Features - **Consolidated Action-Routed MCP Tools:** Minimizes token overhead and eliminates tool bloat in LLM contexts by grouping methods into optimized, togglable tool modules. - **Enterprise-Grade Security:** Comprehensive support for Eunomia policies, OIDC token delegation, and granular execution context tracking. - **Integrated Graph Agent:** Built-in Pydantic AI agent supporting the Agent Control Protocol (ACP) and standard Web interfaces (AG-UI). - **Native Telemetry & Tracing:** Out-of-the-box OpenTelemetry exports and native Langfuse tracing. --- ## CLI or API This agent wraps the Integrate RAG into AI Agents via MCP Server. Supports multiple Vector database technologies. API. You can interact with it programmatically or via its integrated execution entrypoints. Detailed instructions on how to use the underlying API wrappers, extended schema bindings, and developer SDK references are maintained in [docs/index.md](docs/index.md). --- ## MCP This server utilizes dynamic Action-Routed tools to optimize token overhead and maximize IDE compatibility. ### Available MCP Tools
`True`
Functionality
`True`
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