Vector Database - A2A | AG-UI | MCP

Version: 1.0.0

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:

• 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

MCP

MCP Tools

A2A Agent

Architecture:

---
config:
  layout: dagre
---
flowchart TB
 subgraph subGraph0["Agent Capabilities"]
        C["Agent"]
        B["A2A Server - Uvicorn/FastAPI"]
        D["MCP Tools"]
        F["Agent Skills"]
  end
    C --> D & F
    A["User Query"] --> B
    B --> C
    D --> E["Platform API"]

     C:::agent
     B:::server
     A:::server
    classDef server fill:#f9f,stroke:#333
    classDef agent fill:#bbf,stroke:#333,stroke-width:2px
    style B stroke:#000000,fill:#FFD600
    style D stroke:#000000,fill:#BBDEFB
    style F fill:#BBDEFB
    style A fill:#C8E6C9
    style subGraph0 fill:#FFF9C4

Component Interaction Diagram

sequenceDiagram
    participant User
    participant Server as A2A Server
    participant Agent as Agent
    participant Skill as Agent Skills
    participant MCP as MCP Tools

    User->>Server: Send Query
    Server->>Agent: Invoke Agent
    Agent->>Skill: Analyze Skills Available
    Skill->>Agent: Provide Guidance on Next Steps
    Agent->>MCP: Invoke Tool
    MCP-->>Agent: Tool Response Returned
    Agent-->>Agent: Return Results Summarized
    Agent-->>Server: Final Response
    Server-->>User: Output

Usage

MCP CLI

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!

A2A CLI

Endpoints

• Web UI: http://localhost:8000/ (if enabled)
• A2A: http://localhost:8000/a2a (Discovery: /a2a/.well-known/agent.json)
• AG-UI: http://localhost:8000/ag-ui (POST)

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
    }
  }
}

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 ♥️