Ollama-FastMCP Wrapper

A proxy service that bridges Ollama with FastMCP thus allowing models to be used locally in conjunction with MCP servers and their tools directly on the local machine.

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✨ Features

• Connect/disconnect to multiple MCP servers at runtime (using FastMCP).
• Expose FastMCP tools as callable functions to Ollama models.
• Use Ollama locally with tool-augmented reasoning.
• Historical conversation with the LLM model persistable on disk with async I/O.
• Automatically summarise historical conversation with the model.
• Run as:
  • API Server (via FastAPI + Uvicorn)
  • Interactive CLI

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❓ What is MCP?

The Model Context Protocol (MCP) is a protocol that lets you build servers to expose data and functionality to LLM applications in a secure, standardized way. Limited to this wrapper, MCP use is limited to the Tools part.

⚡ Installation

1. Clone this repository and install dependencies:

   git clone https://github.com/andreamoro/ollama-fastmcp-wrapper.git

   Install dependencies according to the package manager you are using. If it's uv:

   cd ollama-fastmcp-wrapper
   uv sync

   Otherwise go with the traditional (obsolete) pip:

   cd ollama-fastmcp-wrapper
   pip install -r requirements.txt

       Python requirements include:        - aiofiles (async file I/O)        - fastapi        - fastmcp        - uvicorn        - ollama

2. Make sure you have:

• Ollama Client up and running:

  ollama serve

• An MCP server to use the Tools capability
  • Without an MCP server, you can use this wrapper as a conversation interface
  • Two example MCP servers are included in the mcp_servers/ directory:
    • math_server.py - Basic arithmetic operations (add, subtract, multiply, divide)
    • ipinfo_server.py - IP geolocation lookup with 20 preset organizations

⛵ Usage

Run the wrapper:

uv run python ollama_wrapper.py

You'll be asked which mode to start in:

• API mode → starts a REST API on http://127.0.0.1:8000
• CLI mode → starts a terminal-based chat loop

📚 Demo Scripts

The demos/ directory contains comprehensive usage examples in both shell and Python formats:

• basic_chat - Simple chat without tools
• math_operations - Using the math MCP server
• ipinfo_lookup - IP geolocation queries
• server_management - Connect/disconnect/list servers
• history_management - Conversation persistence
• temperature_test_multi_model.py - Advanced temperature testing across multiple models

See demos/README.md for detailed usage instructions and examples.

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🖥️ API Mode

Start the API:

uv run python ollama_wrapper.py
# choose "api"

📖 For comprehensive API documentation, usage patterns, and examples, see API_USAGE.md

API Endpoints

Root:

• GET / → API documentation and endpoint listing

Chat:

• POST /chat → Send a chat request (with optional MCP tools)

Model Management:

• GET /model → Get current session model
• GET /model/list → List all available Ollama models
• POST /model/switch/{model_name} → Switch session model and reset context
• GET /ollama/config → Get Ollama instance configuration (host, label, active model)
• GET /ollama/status → Quick health check for Ollama (5s timeout)

History:

• GET /history → Get current conversation history
• GET /history/clear → Clear the current conversation history
• GET /history/load/{file_name} → Load conversation history from disk
• GET /history/overwrite/{file_name} → Overwrite an existing conversation file
• GET /history/save/{file_name} → Save conversation history to disk

Servers:

• GET /servers → List available FastMCP servers from config
• POST /servers/{server_name}/connect → Connect to an MCP server
• POST /servers/{server_name}/disconnect → Disconnect from an MCP server
• GET /servers/{server_name}/tools → List available tools for a specific MCP server

Quick Start Example

# Start API server
uv run python ollama_wrapper.py api

# Simple chat request (uses session model from config)
curl -X POST http://localhost:8000/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Hello! Tell me about Python"}'

# Connect to MCP server and use tools
curl -X POST http://localhost:8000/servers/math/connect
curl -X POST http://localhost:8000/chat \
  -H "Content-Type: application/json" \
  -d '{
    "message": "What is 25 * 4?",
    "mcp_server": "math"
  }'

For detailed usage patterns including model switching, multi-model testing, temperature testing, and more, see API_USAGE.md.

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💬 CLI Mode

uv run python ollama_wrapper.py
# choose "cli"

📖 For comprehensive CLI documentation, commands, and usage patterns, see CLI_USAGE.md

Quick Start

# Start CLI
uv run python ollama_wrapper.py cli

# Chat naturally
You: Hello! How are you?
Bot: I'm doing well, thank you for asking!

# Use commands
You: /help       # Show available commands
You: /model      # Change model interactively
You: /clear      # Clear conversation context
You: /exit       # Exit CLI

For detailed usage patterns including model switching, persistent conversations, and temperature adjustment, see CLI_USAGE.md.

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⚙️ Configuration

Configuration Files

Version 0.4.0 introduces a separated configuration structure:

1. Wrapper Configuration (wrapper_config.toml - root directory):

   [wrapper]
   transport = "HTTP"              # Transport method: "HTTP" or "STDIO" (default: HTTP)
   host = "0.0.0.0"                # Server host address (default: 0.0.0.0)
   port = 8000                     # Server port (default: 8000)
   history_file = ""               # Path to conversation history file (default: none)
   overwrite_history = false       # Overwrite history file on exit (default: false)
   max_history_messages = 20       # Maximum messages before summarization kicks in (default: 20)
   
   [ollama]
   host = "localhost"              # Ollama instance host (default: localhost)
   port = 11434                    # Ollama instance port (default: 11434)
   timeout = 300                   # Request timeout in seconds (default: 300)
   label = ""                      # Optional label to identify this Ollama instance
   model = { default = "llama3.2:3b", temperature = 0.2 }  # Model settings

   Wrapper Settings:

   • max_history_messages: Maximum number of messages before automatic summarization (default: 20)
     • When the message count exceeds this limit, older messages are summarized to save context
     • Helps maintain conversation continuity while keeping token usage manageable
     • Adjust based on your model's context window and use case

   Ollama Settings:

   • host: Ollama instance host (use with port for remote/tunneled instances)
   • port: Ollama instance port (default: 11434)
   • timeout: Request timeout in seconds (default: 300). Prevents the wrapper from hanging indefinitely when SSH tunnels drop or remote Ollama becomes unresponsive.
   • label: Human-readable label to identify this Ollama instance (prompted at startup if not set)
   • For server-side settings (parallelism, VRAM, network access), see OLLAMA_SERVER.md

   Model Settings:

   • default: Default model name if not specified in requests
   • temperature: Controls response randomness (0.0-2.0)
     • Low (0.0-0.3): Consistent, deterministic responses (recommended for factual tasks)
     • Medium (0.7-1.0): Balanced creativity
     • High (1.5-2.0): Very creative, less predictable
     • Temperature can be overridden per request via API

2. MCP Servers Configuration (mcp_servers/mcp_servers_config.toml):

   [[servers]]
   name = "math"
   command = "uv"
   args = ["run", "--with", "fastmcp", "mcp_servers/math_server.py"]
   host = "http://localhost:5000/mcp"
   port = 5000
   enabled = true
   
   [[servers]]
   name = "ipinfo"
   command = "uv"
   args = ["run", "--with", "fastmcp", "mcp_servers/ipinfo_server.py"]
   host = "http://localhost:5001/mcp"
   port = 5001
   enabled = true
   token_file = "mcp_tokens.toml"  # Optional: specify token file (default: mcp_tokens.toml)

3. API Tokens (mcp_servers/mcp_tokens.toml - gitignored):

   # Copy from mcp_tokens.toml.example and add your tokens
   [ipinfo]
   token = "your_ipinfo_token_here"

Configuration Priority

Command-line arguments take precedence over config file settings:

• If you specify --host or --port on the command line, those values will be used
• If not specified on command line, values from wrapper_config.toml will be used
• If not in config file, default values will be used

Transport Methods

• STDIO transport → spawn server locally
• HTTP transport → connect to remote MCP server

Command-Line Arguments

Positional Arguments:

• mode - Operation mode: api or cli (default: api)
• model - Ollama model to use (e.g., llama3.2:3b, gemma3:1b)

Optional Arguments:

• -c, --wrapper-config <file> - Path to wrapper configuration file (default: wrapper_config.toml)
• --mcp-config <filename> - MCP servers config filename in mcp_servers/ directory (default: mcp_servers_config.toml)
• --history-file <file> - Path to conversation history file to load/save
• -o, --overwrite-history - Allow overwriting existing history file
• -t, --transport <method> - Transport method: HTTP or STDIO (default: from config or HTTP)
• --wrapper-host <address> - Wrapper API server host address (default: from config or 0.0.0.0)
• --wrapper-port <number> - Wrapper API server port number (default: from config or 8000)

Ollama Connection Arguments:

• --ollama-host <address> - Ollama instance host (default: from config or localhost)
• --ollama-port <number> - Ollama instance port (default: from config or 11434)
• --ollama-timeout <seconds> - Request timeout in seconds (default: from config or 300)
• --ollama-label <label> - Label to identify the Ollama instance (default: local-server)

Examples

# Use custom wrapper config file
uv run python ollama_wrapper.py api -c my_wrapper_config.toml

# Override config file settings
uv run python ollama_wrapper.py api --wrapper-host 127.0.0.1 --wrapper-port 9000

# Specify transport method
uv run python ollama_wrapper.py api -t STDIO

# Load conversation history
uv run python ollama_wrapper.py cli --history-file my_conversation.json

# Start with specific model and auto-save history
uv run python ollama_wrapper.py api llama3.2:3b --history-file conversation.json -o

# Use alternate MCP servers config
uv run python ollama_wrapper.py api --mcp-config alternate_servers.toml

# Connect to remote Ollama via SSH tunnel (with custom timeout)
uv run python ollama_wrapper.py api --ollama-host localhost --ollama-port 11435 \
  --ollama-timeout 600 --ollama-label "remote-vps-via-tunnel"

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📊 Architecture Diagram

flowchart TD
    O[Ollama] --> W[Ollama-FastMCP Wrapper]
    W -->|API mode| A[API]
    W -->|CLI mode| C[CLI]
    A --> M{ MCP Server Available?} 
    C --> Z
    M -->|Yes| F[FastMCP Tools]
    F --> Z[Chat]
    M -->|No| Z

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💡 Release History / Roadmap

Please check the Changelog file for more information.

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License

MIT License © 2025 Andrea MORO