About MCP Host
MCP Host is a Python client for using Ollama models with one or more MCP servers. It supports stdio and SSE connections and lets the model call tools exposed by connected servers.
Key features
- 01Connect to multiple MCP servers
- 02Support stdio and SSE transports
- 03Use local or remote Ollama backends
- 04Execute server tools through the LLM
- 05Configure servers and models with JSON
- 06List connected servers and available tools
Use cases
- 01Use server tools in an Ollama chat session
- 02Connect to servers over stdio or SSE
- 03Review connected servers and their tools
MCP support
Connects to configured MCP servers over stdio or SSE, and makes their tools available to Ollama models for execution.
Details
From the project READMEMCP Host
A Python implementation of a Model Context Protocol (MCP) host that connects to Ollama LLM backends and MCP servers.
Features
- Multiple Server Support: Connect to any number of MCP-compatible servers
- Multiple Transport Types: Supports both stdio and SSE transports
- Ollama Integration: Seamless connection to local Ollama models
- Tool Execution: Enable LLMs to use tools from connected servers
- Simple CLI: Easy-to-use command-line interface
- JSON Configuration: Simple config file for server and LLM setup
Requirements
- Ollama running locally or on a remote server
Run the MCP Host
python mcp_host.py
Run the weather server(SSE)
python weather.py
Configuration
The MCP Host uses a JSON configuration file to define:
- MCP Servers: The servers that provide tools and resources
- LLM Provider: Configuration for the Ollama backend
Server Configuration
Each server needs:
- type: The transport mechanism (
stdioorsse) - For stdio servers:
- command: The command to run
- args: Command-line arguments (optional)
- env: Environment variables (optional)
- For SSE servers:
- url: The SSE endpoint URL
LLM Provider Configuration
- type: The provider type (currently only
ollamais supported) - model: The model name to use (e.g.,
llama3,mistral, etc.) - url: The Ollama API URL (default:
http://localhost:11434) - parameters: Additional parameters for Ollama (temperature, top_p, etc.)
Command-Line Options
usage: mcp_host.py [-h] [--config CONFIG] [--model MODEL]
[--message-window MESSAGE_WINDOW]
[--provider {ollama}] [--ollama-url OLLAMA_URL]
[--ollama-model OLLAMA_MODEL] [--debug] [--save-config]
MCP Host for LLM tool interactions
options:
-h, --help show this help message and exit
--config CONFIG Path to config file (default: config.json in current directory)
--model MODEL, -m MODEL
Override model specified in config
--message-window MESSAGE_WINDOW
Number of messages to keep in context
Provider Selection:
--provider {ollama} Select LLM provider
Ollama Options:
--ollama-url OLLAMA_URL
URL for Ollama API (e.g., http://localhost:11434)
--ollama-model OLLAMA_MODEL
Ollama model to use (e.g., llama3, mistral, etc.)
Other options:
--debug Enable debug logging
--save-config Save provider options to config file
Usage Examples
Basic Usage
python mcp_host.py
Debug Mode
python mcp_host.py --debug
Special Commands
During a chat session, you can use the following special commands:
tools: List all available tools from connected serversservers: List all connected MCP serversexitorquit: End the session
How It Works
- When you start MCP Host, it connects to all configured MCP servers
- Each server provides a list of available tools
- When you enter a query, it's sent to the Ollama LLM
- If the LLM decides to use tools, MCP Host executes those tool calls
- The results are sent back to the LLM
- The LLM provides a final response incorporating the tool results
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.