MCP Search & Fetch
Provides tools to perform web searches using Ollama's hosted search API and fetch content from web pages.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Search & Fetchsearch for latest AI research papers"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Search & Fetch
An MCP server exposing Ollama's web search and web fetch capabilities as tools.
Features
web_search: Perform web searches using Ollama's hosted search API
web_fetch: Fetch content from web pages
Related MCP server: Ollama Web Search
Requirements
Ollama API key (get one from ollama.com)
Installation
pip install mcp-search-and-fetch
# Export your API key
OLLAMA_API_KEY=your_api_key_here
# Run the MCP server
mcp-search-and-fetchClone from Repository
# Clone the repository
git clone https://github.com/lkiesow/mcp-search-and-fetch.git
cd mcp-search-and-fetch
# Install dependencies
pip install -r requirements.txt
export OLLAMA_API_KEY=your_api_key_here
python mcp_search_and_fetch.pyDocker
To run the pre-built containers:
docker run -p 8000:8000 --env-file .env ghcr.io/lkiesow/mcp-search-and-fetch:latestOr, if you want to build your own containers:
docker build -t mcp-search-fetch .
docker run -p 8000:8000 --env-file .env mcp-search-fetchDocker Compose
An example ´docker-compose.yml` with Caddy as reverse proxy:
services:
search-and-fetch:
image: ghcr.io/lkiesow/mcp-search-and-fetch:1.0.1
container_name: search-and-fetch
restart: always
environment:
OLLAMA_API_KEY: secret.ollama.api.key
networks:
- mcp
caddy:
image: docker.io/library/caddy:2.10.2
container_name: caddy
restart: always
environment:
CADDY_DOMAIN: search-and-fetch.example.com
CADDY_API_KEY: secret.mcp.api.key
volumes:
- /opt/mcp-search-and-fetch/caddy:/etc/caddy
- caddy_data:/data
- caddy_config:/config
ports:
- 80:80
- 443:443
- 443:443/udp
networks:
- mcp
volumes:
caddy_data:
caddy_config:
networks:
mcp:And an example Caddyfile in the caddy directory like this:
{$CADDY_DOMAIN} {
@no_auth {
not header Authorization "Bearer {$CADDY_API_KEY}"
}
respond @no_auth "Unauthorized" 401
reverse_proxy /* search-and-fetch:8000
}Usage
Local (stdio)
python mcp_search_and_fetch.pyHTTP Server (Streamable HTTP)
# Set port and host in .env
export MCP_SERVER_PORT=8000
export MCP_SERVER_HOST=0.0.0.0
python mcp_search_and_fetch.pyConfiguration
You can either set environment variables or provide a .env file.
Take a look at the .env.sample for an example.
Environment Variable | Required | Default | Description |
| Yes | - | Your Ollama API key |
| No | - | Run HTTP server on specified port |
| No | 127.0.0.1 | Host to bind to |
Tools
web_search(query, max_results=3)
Performs a web search and returns results as JSON.
web_fetch(url)
Fetches content from a URL and returns it as JSON.
This server cannot be deployed
Maintenance
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Fetch pages as markdown, search web and news, extract structured data. For AI agents.
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