vector-mcp 0.1.10

Integrate RAG into AI Agents via MCP Server. Supports multiple Vector database technologies.

Vector Database MCP Server

Version: 0.1.10

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:

• 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 - 90% Tested
• Couchbase - 80% Tested
• Qdrant - 80% Tested
• MongoDB - 80% Tested

This repository is actively maintained - Contributions and bug reports are welcome!

Automated tests are planned

Usage:

MCP CLI

Short Flag	Long Flag	Description
-h	--help	Display help information
-t	--transport	Transport method: 'stdio', 'http', or 'sse' [legacy] (default: stdio)
-s	--host	Host address for HTTP transport (default: 0.0.0.0)
-p	--port	Port number for HTTP transport (default: 8000)
	--auth-type	Authentication type: 'none', 'static', 'jwt', 'oauth-proxy', 'oidc-proxy', 'remote-oauth' (default: none)
	--token-jwks-uri	JWKS URI for JWT verification
	--token-issuer	Issuer for JWT verification
	--token-audience	Audience for JWT verification
	--oauth-upstream-auth-endpoint	Upstream authorization endpoint for OAuth Proxy
	--oauth-upstream-token-endpoint	Upstream token endpoint for OAuth Proxy
	--oauth-upstream-client-id	Upstream client ID for OAuth Proxy
	--oauth-upstream-client-secret	Upstream client secret for OAuth Proxy
	--oauth-base-url	Base URL for OAuth Proxy
	--oidc-config-url	OIDC configuration URL
	--oidc-client-id	OIDC client ID
	--oidc-client-secret	OIDC client secret
	--oidc-base-url	Base URL for OIDC Proxy
	--remote-auth-servers	Comma-separated list of authorization servers for Remote OAuth
	--remote-base-url	Base URL for Remote OAuth
	--allowed-client-redirect-uris	Comma-separated list of allowed client redirect URIs
	--eunomia-type	Eunomia authorization type: 'none', 'embedded', 'remote' (default: none)
	--eunomia-policy-file	Policy file for embedded Eunomia (default: mcp_policies.json)
	--eunomia-remote-url	URL for remote Eunomia server

Using as an MCP Server

The MCP Server can be run in two modes: stdio (for local testing) or http (for networked access). To start the server, use the following commands:

Run in stdio mode (default):

vector-mcp --transport "stdio"

Run in HTTP mode:

vector-mcp --transport "http"  --host "0.0.0.0"  --port "8000"

Creating Collection

AI Prompt:

Create a collection called zapdos with the documents in this directory:
/home/user/Documents/Chroma

AI Response:

The collection named "zapdos" has been successfully created or retrieved from the vector database
using the documents in the directory /home/user/Documents/Chroma.
Let me know if you'd like to perform any further actions, such as querying the collection or adding more documents!

Retrieving from Collection

AI Prompt:

Create a collection called zapdos with the documents in this directory:
/home/user/Documents/Chroma

AI Response:

The collection named "zapdos" has been successfully created or retrieved from the vector database
using the documents in the directory /home/user/Documents/Chroma.
Let me know if you'd like to perform any further actions, such as querying the collection or adding more documents!

Deleting Collection

AI Prompt:

Delete the collection called memory

AI Response:

The collection named "memory" has been successfully deleted.
Let me know if you'd like to create a new collection or perform any other actions!

Deploy MCP Server as a Service

The MCP server can be deployed using Docker, with configurable authentication, middleware, and Eunomia authorization.

Using Docker Run

docker pull knucklessg1/vector-mcp:latest

docker run -d \
  --name vector-mcp \
  -p 8004:8004 \
  -e HOST=0.0.0.0 \
  -e PORT=8004 \
  -e TRANSPORT=http \
  -e AUTH_TYPE=none \
  -e EUNOMIA_TYPE=none \
  knucklessg1/vector-mcp:latest

For advanced authentication (e.g., JWT, OAuth Proxy, OIDC Proxy, Remote OAuth) or Eunomia, add the relevant environment variables:

docker run -d \
  --name vector-mcp \
  -p 8004:8004 \
  -e HOST=0.0.0.0 \
  -e PORT=8004 \
  -e TRANSPORT=http \
  -e AUTH_TYPE=oidc-proxy \
  -e OIDC_CONFIG_URL=https://provider.com/.well-known/openid-configuration \
  -e OIDC_CLIENT_ID=your-client-id \
  -e OIDC_CLIENT_SECRET=your-client-secret \
  -e OIDC_BASE_URL=https://your-server.com \
  -e ALLOWED_CLIENT_REDIRECT_URIS=http://localhost:*,https://*.example.com/* \
  -e EUNOMIA_TYPE=embedded \
  -e EUNOMIA_POLICY_FILE=/app/mcp_policies.json \
  knucklessg1/vector-mcp:latest

Using Docker Compose

Create a docker-compose.yml file:

services:
  vector-mcp:
    image: knucklessg1/vector-mcp:latest
    environment:
      - HOST=0.0.0.0
      - PORT=8004
      - TRANSPORT=http
      - AUTH_TYPE=none
      - EUNOMIA_TYPE=none
    ports:
      - 8004:8004

For advanced setups with authentication and Eunomia:

services:
  vector-mcp:
    image: knucklessg1/vector-mcp:latest
    environment:
      - HOST=0.0.0.0
      - PORT=8004
      - TRANSPORT=http
      - AUTH_TYPE=oidc-proxy
      - OIDC_CONFIG_URL=https://provider.com/.well-known/openid-configuration
      - OIDC_CLIENT_ID=your-client-id
      - OIDC_CLIENT_SECRET=your-client-secret
      - OIDC_BASE_URL=https://your-server.com
      - ALLOWED_CLIENT_REDIRECT_URIS=http://localhost:*,https://*.example.com/*
      - EUNOMIA_TYPE=embedded
      - EUNOMIA_POLICY_FILE=/app/mcp_policies.json
    ports:
      - 8004:8004
    volumes:
      - ./mcp_policies.json:/app/mcp_policies.json

Run the service:

docker-compose up -d

Configure mcp.json for AI Integration

{
  "mcpServers": {
    "vector_mcp": {
      "command": "uv",
      "args": [
        "run",
        "--with",
        "vector-mcp",
        "vector-mcp"
      ],
      "env": {
        "DATABASE_TYPE": "chromadb",                   // Optional
        "COLLECTION_NAME": "memory",                   // Optional
        "DOCUMENT_DIRECTORY": "/home/user/Documents/"  // Optional
      },
      "timeout": 300000
    }
  }
}

Installation Instructions:

Install Python Package

python -m pip install vector-mcp

PGVector dependencies

python -m pip install vector-mcp[pgvector]

All

python -m pip install vector-mcp[all]

or

uv pip install --upgrade vector-mcp[all]

Repository Owners:

Special shoutouts to Microsoft Autogen V1 ♥️