# SearXNG - A2A | AG-UI | MCP
*Version: 0.0.22*
## Overview
SearXNG MCP Server + A2A Server
It includes a Model Context Protocol (MCP) server and an out of the box Agent2Agent (A2A) agent
Perform privacy-respecting web searches using SearXNG through an MCP server!
This repository is actively maintained - Contributions are welcome!
### Supports:
- Privacy-respecting metasearch
- Customizable search parameters (language, time range, categories, engines)
- Safe search levels
- Pagination control
- Basic authentication support
- Random instance selection
## MCP
### MCP Tools
| Function Name | Description | Tag(s) |
|:--------------|:---------------------------------------------------------------------------------------------------------------------------------------|:---------|
| `web_search` | Perform web searches using SearXNG, a privacy-respecting metasearch engine. Returns relevant web content with customizable parameters. | `search` |
### 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):
```bash
searxng-mcp --transport "stdio"
```
#### Run in HTTP mode:
```bash
searxng-mcp --transport "http" --host "0.0.0.0" --port "8000"
```
AI Prompt:
```text
Search for information about artificial intelligence
```
AI Response:
```text
Search completed successfully. Found 10 results for "artificial intelligence":
1. **What is Artificial Intelligence?**
URL: https://example.com/ai
Content: Artificial intelligence (AI) refers to the simulation of human intelligence in machines...
2. **AI Overview**
URL: https://example.org/ai-overview
Content: AI encompasses machine learning, deep learning, and more...
```
## A2A Agent
This package also includes an A2A agent server that can be used to interact with the SearXNG MCP server.
### Architecture:
```mermaid
---
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
```mermaid
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
| 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 |
### 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)
| Short Flag | Long Flag | Description |
|------------|-------------------|------------------------------------------------------------------------|
| -h | --help | Display help information |
| | --host | Host to bind the server to (default: 0.0.0.0) |
| | --port | Port to bind the server to (default: 9000) |
| | --reload | Enable auto-reload |
| | --provider | LLM Provider: 'openai', 'anthropic', 'google', 'huggingface' |
| | --model-id | LLM Model ID (default: qwen/qwen3-4b-2507) |
| | --base-url | LLM Base URL (for OpenAI compatible providers) |
| | --api-key | LLM API Key |
| | --mcp-url | MCP Server URL (default: http://localhost:8000/mcp) |
| | --web | Enable Pydantic AI Web UI | False (Env: ENABLE_WEB_UI) |
### 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):
```bash
searxng-mcp --transport "stdio"
```
#### Run in HTTP mode:
```bash
searxng-mcp --transport "http" --host "0.0.0.0" --port "8000"
```
AI Prompt:
```text
Search for information about artificial intelligence
```
AI Response:
```text
Search completed successfully. Found 10 results for "artificial intelligence":
1. **What is Artificial Intelligence?**
URL: https://example.com/ai
Content: Artificial intelligence (AI) refers to the simulation of human intelligence in machines...
2. **AI Overview**
URL: https://example.org/ai-overview
Content: AI encompasses machine learning, deep learning, and more...
```
### Agentic AI
`searxng-mcp` is designed to be used by Agentic AI systems. It provides a set of tools that allow agents to search the web using SearXNG.
## Agent-to-Agent (A2A)
This package also includes an A2A agent server that can be used to interact with the SearXNG MCP server.
### CLI
| Argument | Description | Default |
|-------------------|----------------------------------------------------------------|--------------------------------|
| `--host` | Host to bind the server to | `0.0.0.0` |
| `--port` | Port to bind the server to | `9000` |
| `--reload` | Enable auto-reload | `False` |
| `--provider` | LLM Provider (openai, anthropic, google, huggingface) | `openai` |
| `--model-id` | LLM Model ID | `qwen/qwen3-4b-2507` |
| `--base-url` | LLM Base URL (for OpenAI compatible providers) | `http://ollama.arpa/v1` |
| `--api-key` | LLM API Key | `ollama` |
| `--mcp-url` | MCP Server URL | `http://searxng-mcp:8000/mcp` |
| `--allowed-tools` | List of allowed MCP tools | `web_search` |
### Examples
#### Run A2A Server
```bash
searxng-agent --provider openai --model-id gpt-4 --api-key sk-... --mcp-url http://localhost:8000/mcp
```
#### Run with Docker
```bash
docker run -e CMD=searxng-agent -p 8000:8000 searxng-mcp
```
## Docker
### Build
```bash
docker build -t searxng-mcp .
```
### Run MCP Server
```bash
docker run -p 8000:8000 searxng-mcp
```
### Run A2A Server
```bash
docker run -e CMD=searxng-agent -p 8001:8001 searxng-mcp
```
### Deploy MCP Server as a Service
The ServiceNow MCP server can be deployed using Docker, with configurable authentication, middleware, and Eunomia authorization.
#### Using Docker Run
```bash
docker pull knucklessg1/searxng-mcp:latest
docker run -d \
--name searxng-mcp \
-p 8004:8004 \
-e HOST=0.0.0.0 \
-e PORT=8004 \
-e TRANSPORT=http \
-e AUTH_TYPE=none \
-e EUNOMIA_TYPE=none \
-e SEARXNG_URL=https://searxng.example.com \
-e SEARXNG_USERNAME=user \
-e SEARXNG_PASSWORD=pass \
-e USE_RANDOM_INSTANCE=false \
knucklessg1/searxng-mcp:latest
```
For advanced authentication (e.g., JWT, OAuth Proxy, OIDC Proxy, Remote OAuth) or Eunomia, add the relevant environment variables:
```bash
docker run -d \
--name searxng-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 \
-e SEARXNG_URL=https://searxng.example.com \
-e SEARXNG_USERNAME=user \
-e SEARXNG_PASSWORD=pass \
-e USE_RANDOM_INSTANCE=false \
knucklessg1/searxng-mcp:latest
```
#### Using Docker Compose
Create a `docker-compose.yml` file:
```yaml
services:
searxng-mcp:
image: knucklessg1/searxng-mcp:latest
environment:
- HOST=0.0.0.0
- PORT=8004
- TRANSPORT=http
- AUTH_TYPE=none
- EUNOMIA_TYPE=none
- SEARXNG_URL=https://searxng.example.com
- SEARXNG_USERNAME=user
- SEARXNG_PASSWORD=pass
- USE_RANDOM_INSTANCE=false
ports:
- 8004:8004
```
For advanced setups with authentication and Eunomia:
```yaml
services:
searxng-mcp:
image: knucklessg1/searxng-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
- SEARXNG_URL=https://searxng.example.com
- SEARXNG_USERNAME=user
- SEARXNG_PASSWORD=pass
- USE_RANDOM_INSTANCE=false
ports:
- 8004:8004
volumes:
- ./mcp_policies.json:/app/mcp_policies.json
```
Run the service:
```bash
docker-compose up -d
```
#### Configure `mcp.json` for AI Integration
```json
{
"mcpServers": {
"searxng": {
"command": "uv",
"args": [
"run",
"--with",
"searxng-mcp",
"searxng-mcp"
],
"env": {
"SEARXNG_URL": "https://searxng.example.com",
"SEARXNG_USERNAME": "user",
"SEARXNG_PASSWORD": "pass",
"USE_RANDOM_INSTANCE": "false"
},
"timeout": 300000
}
}
}
```
## Install Python Package
```bash
python -m pip install searxng-mcp
```
```bash
uv pip install searxng-mcp
```
## Repository Owners
<img width="100%" height="180em" src="https://github-readme-stats.vercel.app/api?username=Knucklessg1&show_icons=true&hide_border=true&&count_private=true&include_all_commits=true" />
