mcp-client-for-ollama

<p align="center"> <img src="https://github.com/jonigl/mcp-client-for-ollama/blob/main/misc/ollmcp-logo-512.png?raw=true" width="256" /> </p> <p align="center"> <i>A simple yet powerful Python client for interacting with Model Context Protocol (MCP) servers using Ollama, allowing local LLMs to use tools.</i> </p> --- # MCP Client for Ollama (ollmcp) [](https://www.python.org/downloads/) [](https://pypi.org/project/ollmcp/) [](https://pypi.org/project/mcp-client-for-ollama/) [](https://github.com/jonigl/mcp-client-for-ollama/actions/workflows/publish.yml) [](https://github.com/jonigl/mcp-client-for-ollama/actions/workflows/ci.yml) <p align="center"> <img src="https://raw.githubusercontent.com/jonigl/mcp-client-for-ollama/v0.15.0/misc/ollmcp-demo.gif" alt="MCP Client for Ollama Demo"> </p> <p align="center"> <a href="https://asciinema.org/a/jxc6N8oKZAWrzH8aK867zhXdO" target="_blank">🎥 Watch this demo as an Asciinema recording</a> </p> ## Table of Contents - [Overview](#overview) - [Features](#features) - [Requirements](#requirements) - [Quick Start](#quick-start) - [Usage](#usage) - [Command-line Arguments](#command-line-arguments) - [Usage Examples](#usage-examples) - [How Tool Calls Work](#how-tool-calls-work) - ✨**NEW** [Agent Mode](#agent-mode) - [Interactive Commands](#interactive-commands) - [Tool and Server Selection](#tool-and-server-selection) - [Model Selection](#model-selection) - [Advanced Model Configuration](#advanced-model-configuration) - [Server Reloading for Development](#server-reloading-for-development) - [Human-in-the-Loop (HIL) Tool Execution](#human-in-the-loop-hil-tool-execution) - [Performance Metrics](#performance-metrics) - [Autocomplete and Prompt Features](#autocomplete-and-prompt-features) - [Configuration Management](#configuration-management) - [Server Configuration Format](#server-configuration-format) - [Tips: Where to Put MCP Server Configs and a Working Example](#tips-where-to-put-mcp-server-configs-and-a-working-example) - [Compatible Models](#compatible-models) - [Ollama Cloud Models](#ollama-cloud-models) - [Where Can I Find More MCP Servers?](#where-can-i-find-more-mcp-servers) - [Related Projects](#related-projects) - [License](#license) - [Acknowledgments](#acknowledgments) ## Overview MCP Client for Ollama (`ollmcp`) is a modern, interactive terminal application (TUI) for connecting local Ollama LLMs to one or more Model Context Protocol (MCP) servers, enabling advanced tool use and workflow automation. With a rich, user-friendly interface, it lets you manage tools, models, and server connections in real time—no coding required. Whether you're building, testing, or just exploring LLM tool use, this client streamlines your workflow with features like fuzzy autocomplete, advanced model configuration, MCP servers hot-reloading for development, and Human-in-the-Loop safety controls. ## Features - 🤖 **Agent Mode**: Iterative tool execution when models request multiple tool calls, with a configurable loop limit to prevent infinite loops - 🌐 **Multi-Server Support**: Connect to multiple MCP servers simultaneously - 🚀 **Multiple Transport Types**: Supports STDIO, SSE, and Streamable HTTP server connections - ☁️ **Ollama Cloud Support**: Works seamlessly with Ollama Cloud models for tool calling, enabling access to powerful cloud-hosted models while using local MCP tools - 🎨 **Rich Terminal Interface**: Interactive console UI with modern styling - 🌊 **Streaming Responses**: View model outputs in real-time as they're generated - 🛠️ **Tool Management**: Enable/disable specific tools or entire servers during chat sessions - 🧑‍💻 **Human-in-the-Loop (HIL)**: Review and approve tool executions before they run for enhanced control and safety - 🎮 **Advanced Model Configuration**: Fine-tune 15+ model parameters including context window size, temperature, sampling, repetition control, and more - 💬 **System Prompt Customization**: Define and edit the system prompt to control model behavior and persona - 🧠 **Context Window Control**: Adjust the context window size (num_ctx) to handle longer conversations and complex tasks - 🎨 **Enhanced Tool Display**: Beautiful, structured visualization of tool executions with JSON syntax highlighting - 🧠 **Context Management**: Control conversation memory with configurable retention settings - 🤔 **Thinking Mode**: Advanced reasoning capabilities with visible thought processes for supported models (e.g., gpt-oss, deepseek-r1, qwen3, etc.) - 🗣️ **Cross-Language Support**: Seamlessly work with both Python and JavaScript MCP servers - 🔍 **Auto-Discovery**: Automatically find and use Claude's existing MCP server configurations - 🔁 **Dynamic Model Switching**: Switch between any installed Ollama model without restarting - 💾 **Configuration Persistence**: Save and load tool preferences and model settings between sessions - 🔄 **Server Reloading**: Hot-reload MCP servers during development without restarting the client - ✨ **Fuzzy Autocomplete**: Interactive, arrow-key command autocomplete with descriptions - 🏷️ **Dynamic Prompt**: Shows current model, thinking mode, and enabled tools - 📊 **Performance Metrics**: Detailed model performance data after each query, including duration timings and token counts - 🔌 **Plug-and-Play**: Works immediately with standard MCP-compliant tool servers - 🔔 **Update Notifications**: Automatically detects when a new version is available - 🖥️ **Modern CLI with Typer**: Grouped options, shell autocompletion, and improved help output ## Requirements - **Python 3.10+** ([Installation guide](https://www.python.org/downloads/)) - **Ollama** running locally ([Installation guide](https://ollama.com/download)) - **UV package manager** ([Installation guide](https://github.com/astral-sh/uv)) ## Quick Start **Option 1:** Install with pip and run ```bash pip install --upgrade ollmcp ollmcp ``` **Option 2:** One-step install and run ```bash uvx ollmcp ``` **Option 3:** Install from source and run using virtual environment ```bash git clone https://github.com/jonigl/mcp-client-for-ollama.git cd mcp-client-for-ollama uv venv && source .venv/bin/activate uv pip install . uv run -m mcp_client_for_ollama ``` ## Usage Run with default settings: ```bash ollmcp ``` > If you don't provide any options, the client will use `auto-discovery` mode to find MCP servers from Claude's configuration. ### Command-line Arguments > [!TIP] > The CLI now uses `Typer` for a modern experience: grouped options, rich help, and built-in shell autocompletion. Advanced users can use short flags for faster commands. To enable autocompletion, run: > > ```bash > ollmcp --install-completion > ``` > > Then restart your shell or follow the printed instructions. #### MCP Server Configuration: - `--mcp-server`, `-s`: Path to one or more MCP server scripts (.py or .js). Can be specified multiple times. - `--mcp-server-url`, `-u`: URL to one or more SSE or Streamable HTTP MCP servers. Can be specified multiple times. See [Common MCP endpoint paths](#common-mcp-endpoint-paths) for typical endpoints. - `--servers-json`, `-j`: Path to a JSON file with server configurations. See [Server Configuration Format](#server-configuration-format) for details. - `--auto-discovery`, `-a`: Auto-discover servers from Claude's default config file (default behavior if no other options provided). > [!TIP] > Claude's configuration file is typically located at: > `~/Library/Application Support/Claude/claude_desktop_config.json` #### Ollama Configuration: - `--model`, `-m` MODEL: Ollama model to use. Default: `qwen2.5:7b` - `--host`, `-H` HOST: Ollama host URL. Default: `http://localhost:11434` #### General Options: - `--version`, `-v`: Show version and exit - `--help`, `-h`: Show help message and exit - `--install-completion`: Install shell autocompletion scripts for the client - `--show-completion`: Show available shell completion options ### Usage Examples Simplest way to run the client: ```bash ollmcp ``` > [!TIP] > This will automatically discover and connect to any MCP servers configured in Claude's settings and use the default model `qwen2.5:7b` or the model specified in your configuration file. Connect to a single server: ```bash ollmcp --mcp-server /path/to/weather.py --model llama3.2:3b # Or using short flags: ollmcp -s /path/to/weather.py -m llama3.2:3b ``` Connect to multiple servers: ```bash ollmcp --mcp-server /path/to/weather.py --mcp-server /path/to/filesystem.js # Or using short flags: ollmcp -s /path/to/weather.py -s /path/to/filesystem.js ``` >[!TIP] > If model is not specified, the default model `qwen2.5:7b` will be used or the model specified in your configuration file. Use a JSON configuration file: ```bash ollmcp --servers-json /path/to/servers.json --model llama3.2:1b # Or using short flags: ollmcp -j /path/to/servers.json -m llama3.2:1b ``` >[!TIP] > See the [Server Configuration Format](#server-configuration-format) section for details on how to structure the JSON file. Use a custom Ollama host: ```bash ollmcp --host http://localhost:22545 --servers-json /path/to/servers.json --auto-discovery # Or using short flags: ollmcp -H http://localhost:22545 -j /path/to/servers.json -a ``` Connect to SSE or Streamable HTTP servers by URL: ```bash ollmcp --mcp-server-url http://localhost:8000/sse --model qwen2.5:latest # Or using short flags: ollmcp -u http://localhost:8000/sse -m qwen2.5:latest ``` Connect to multiple URL servers: ```bash ollmcp --mcp-server-url http://localhost:8000/sse --mcp-server-url http://localhost:9000/mcp # Or using short flags: ollmcp -u http://localhost:8000/sse -u http://localhost:9000/mcp ``` Mix local scripts and URL servers: ```bash ollmcp --mcp-server /path/to/weather.py --mcp-server-url http://localhost:8000/mcp --model qwen3:1.7b # Or using short flags: ollmcp -s /path/to/weather.py -u http://localhost:8000/mcp -m qwen3:1.7b ``` Use auto-discovery with mixed server types: ```bash ollmcp --mcp-server /path/to/weather.py --mcp-server-url http://localhost:8000/mcp --auto-discovery # Or using short flags: ollmcp -s /path/to/weather.py -u http://localhost:8000/mcp -a ``` ## Interactive Commands During chat, use these commands:  | Command | Shortcut | Description | |------------------|------------------|-----------------------------------------------------| | `clear` | `cc` | Clear conversation history and context | | `cls` | `clear-screen` | Clear the terminal screen | | `context` | `c` | Toggle context retention | | `context-info` | `ci` | Display context statistics | | `help` | `h` | Display help and available commands | | `human-in-loop` | `hil` | Toggle Human-in-the-Loop confirmations for tool execution | | `load-config` | `lc` | Load tool and model configuration from a file | | `loop-limit` | `ll` | Set maximum iterative tool-loop iterations (Agent Mode). Default: 3 | | `model` | `m` | List and select a different Ollama model | | `model-config` | `mc` | Configure advanced model parameters and system prompt| | `quit`, `exit`, `bye` | `q` or `Ctrl+D` | Exit the client | | `reload-servers` | `rs` | Reload all MCP servers with current configuration | | `reset-config` | `rc` | Reset configuration to defaults (all tools enabled) | | `save-config` | `sc` | Save current tool and model configuration to a file | | `show-metrics` | `sm` | Toggle performance metrics display | | `show-thinking` | `st` | Toggle thinking text visibility | | `show-tool-execution` | `ste` | Toggle tool execution display visibility | | `tools` | `t` | Open the tool selection interface | ### Tool and Server Selection The tool and server selection interface allows you to enable or disable specific tools:  - Enter **numbers** separated by commas (e.g. `1,3,5`) to toggle specific tools - Enter **ranges** of numbers (e.g. `5-8`) to toggle multiple consecutive tools - Enter **S + number** (e.g. `S1`) to toggle all tools in a specific server - `a` or `all` - Enable all tools - `n` or `none` - Disable all tools - `d` or `desc` - Show/hide tool descriptions - `j` or `json` - Show detailed tool JSON schemas on enabled tools for debugging purposes - `s` or `save` - Save changes and return to chat - `q` or `quit` - Cancel changes and return to chat ### Model Selection The model selection interface shows all available models in your Ollama installation:  - Enter the **number** of the model you want to use - `s` or `save` - Save the model selection and return to chat - `q` or `quit` - Cancel the model selection and return to chat ### Advanced Model Configuration The `model-config` (`mc`) command opens the advanced model settings interface, allowing you to fine-tune how the model generates responses:  #### System Prompt - **System Prompt**: Set the model's role and behavior to guide responses. #### Key Parameters - **System Prompt**: Set the model's role and behavior to guide responses. - **Context Window (num_ctx)**: Set how much chat history the model uses. Balance with memory usage and performance. - **Keep Tokens**: Prevent important tokens from being dropped - **Max Tokens**: Limit response length (0 = auto) - **Seed**: Make outputs reproducible (set to -1 for random) - **Temperature**: Control randomness (0 = deterministic, higher = creative) - **Top K / Top P / Min P / Typical P**: Sampling controls for diversity - **Repeat Last N / Repeat Penalty**: Reduce repetition - **Presence/Frequency Penalty**: Encourage new topics, reduce repeats - **Stop Sequences**: Custom stopping points (up to 8) - **Batch Size (num_batch)**: Controls internal batching of requests; larger values can increase throughput but use more memory. #### Commands - Enter parameter numbers `1-15` to edit settings - Enter `sp` to edit the system prompt - Use `u1`, `u2`, etc. to unset parameters, or `uall` to reset all - `h`/`help`: Show parameter details and tips - `undo`: Revert changes - `s`/`save`: Apply changes - `q`/`quit`: Cancel #### Example Configurations - **Factual:** `temperature: 0.0-0.3`, `top_p: 0.1-0.5`, `seed: 42` - **Creative:** `temperature: 1.0+`, `top_p: 0.95`, `presence_penalty: 0.2` - **Reduce Repeats:** `repeat_penalty: 1.1-1.3`, `presence_penalty: 0.2`, `frequency_penalty: 0.3` - **Balanced:** `temperature: 0.7`, `top_p: 0.9`, `typical_p: 0.7` - **Reproducible:** `seed: 42`, `temperature: 0.0` - **Large Context:** `num_ctx: 8192` or higher for complex conversations requiring more context > [!TIP] > All parameters default to unset, letting Ollama use its own optimized values. Use `help` in the config menu for details and recommendations. Changes are saved with your configuration. ### Server Reloading for Development The `reload-servers` command (`rs`) is particularly useful during MCP server development. It allows you to reload all connected servers without restarting the entire client application. **Key Benefits:** - 🔄 **Hot Reload**: Instantly apply changes to your MCP server code - 🛠️ **Development Workflow**: Perfect for iterative development and testing - 📝 **Configuration Updates**: Automatically picks up changes in server JSON configs or Claude configs - 🎯 **State Preservation**: Maintains your tool enabled/disabled preferences across reloads - ⚡️ **Time Saving**: No need to restart the client and reconfigure everything **When to Use:** - After modifying your MCP server implementation - When you've updated server configurations in JSON files - After changing Claude's MCP configuration - During debugging to ensure you're testing the latest server version Simply type `reload-servers` or `rs` in the chat interface, and the client will: 1. Disconnect from all current MCP servers 2. Reconnect using the same parameters (server paths, config files, auto-discovery) 3. Restore your previous tool enabled/disabled settings 4. Display the updated server and tool status This feature dramatically improves the development experience when building and testing MCP servers. ### Human-in-the-Loop (HIL) Tool Execution The Human-in-the-Loop feature provides an additional safety layer by allowing you to review and approve tool executions before they run. This is particularly useful for: - 🛡️ **Safety**: Review potentially destructive operations before execution - 🔍 **Learning**: Understand what tools the model wants to use and why - 🎯 **Control**: Selective execution of only the tools you approve - 🚫 **Prevention**: Stop unwanted tool calls from executing #### HIL Confirmation Display When HIL is enabled, you'll see a confirmation prompt before each tool execution: **Example:**  ### Human-in-the-Loop (HIL) Configuration - **Default State**: HIL confirmations are enabled by default for safety - **Toggle Command**: Use `human-in-loop` or `hil` to toggle on/off - **Persistent Settings**: HIL preference is saved with your configuration - **Quick Disable**: Choose "disable" during any confirmation to turn off permanently - **Re-enable**: Use the `hil` command anytime to turn confirmations back on **Benefits:** - **Enhanced Safety**: Prevent accidental or unwanted tool executions - **Awareness**: Understand what actions the model is attempting to perform - **Selective Control**: Choose which operations to allow on a case-by-case basis - **Peace of Mind**: Full visibility and control over automated actions ### Performance Metrics The Performance Metrics feature displays detailed model performance data after each query in a bordered panel. The metrics show duration timings, token counts, and generation rates directly from Ollama's response. **Displayed Metrics:** - `total duration`: Total time spent generating the complete response (seconds) - `load duration`: Time spent loading the model (milliseconds) - `prompt eval count`: Number of tokens in the input prompt - `prompt eval duration`: Time spent evaluating the input prompt (milliseconds) - `eval count`: Number of tokens generated in the response - `eval duration`: Time spent generating the response tokens (seconds) - `prompt eval rate`: Speed of input prompt processing (tokens/second) - `eval rate`: Speed of response token generation (tokens/second) **Example:**  #### Performance Metrics Configuration - **Default State**: Metrics are disabled by default for cleaner output - **Toggle Command**: Use `show-metrics` or `sm` to enable/disable metrics display - **Persistent Settings**: Metrics preference is saved with your configuration **Benefits:** - **Performance Monitoring**: Track model efficiency and response times - **Token Tracking**: Monitor actual token consumption for analysis - **Benchmarking**: Compare performance across different models > [!NOTE] > **Data Source**: All metrics come directly from Ollama's response, ensuring accuracy and reliability. ## Autocomplete and Prompt Features ### Typer Shell Autocompletion - The CLI supports shell autocompletion for all options and arguments via Typer - To enable, run `ollmcp --install-completion` and follow the instructions for your shell - Enjoy tab-completion for all grouped and general options ### FZF-style Autocomplete - Fuzzy matching for commands as you type - Arrow (`▶`) highlights the best match - Command descriptions shown in the menu - Case-insensitive matching for convenience - Centralized command list for consistency ### Contextual Prompt The chat prompt now gives you clear, contextual information at a glance: - **Model**: Shows the current Ollama model in use - **Thinking Mode**: Indicates if "thinking mode" is active (for supported models) - **Tools**: Displays the number of enabled tools **Example prompt:** ``` qwen3/show-thinking/12-tools❯ ``` - `qwen3` Model name - `/show-thinking` Thinking mode indicator (if enabled, otherwise `/thinking` or omitted) - `/12-tools` Number of tools enabled (or `/1-tool` for singular) - `❯` Prompt symbol This makes it easy to see your current context before entering a query. ## Configuration Management > [!TIP] > It will automatically load the default configuration from `~/.config/ollmcp/config.json` if it exists. The client supports saving and loading tool configurations between sessions: - When using `save-config`, you can provide a name for the configuration or use the default - Configurations are stored in `~/.config/ollmcp/` directory - The default configuration is saved as `~/.config/ollmcp/config.json` - Named configurations are saved as `~/.config/ollmcp/{name}.json` The configuration saves: - Current model selection - Advanced model parameters (system prompt, temperature, sampling settings, etc.) - Enabled/disabled status of all tools - Context retention settings - Thinking mode settings - Tool execution display preferences - Performance metrics display preferences - Human-in-the-Loop confirmation settings ## Server Configuration Format The JSON configuration file supports STDIO, SSE, and Streamable HTTP server types (MCP 1.10.1): ```json { "mcpServers": { "stdio-server": { "command": "command-to-run", "args": ["arg1", "arg2", "..."], "env": { "ENV_VAR1": "value1", "ENV_VAR2": "value2" }, "disabled": false }, "sse-server": { "type": "sse", "url": "http://localhost:8000/sse", "headers": { "Authorization": "Bearer your-token-here" }, "disabled": false }, "http-server": { "type": "streamable_http", "url": "http://localhost:8000/mcp", "headers": { "X-API-Key": "your-api-key-here" }, "disabled": false } } } ``` > [!NOTE] > **MCP 1.10.1 Transport Support**: The client now supports the latest Streamable HTTP transport with improved performance and reliability. If you specify a URL without a type, the client will default to using Streamable HTTP transport. ### Tips: where to put MCP server configs and a working example A common point of confusion is where to store MCP server configuration files and how the TUI's save/load feature is used. Here's a short, practical guide that has helped other users: - The TUI's `save-config` / `load-config` (or `sc` / `lc`) commands are intended to save *TUI preferences* like which tools you enabled, your selected model, thinking mode, and other client-side settings. They are not required to register MCP server connections with the client. - For MCP server JSON files (the `mcpServers` object shown above) we recommend keeping them outside the TUI config directory or in a clear subfolder, for example: ``` ~/.config/ollmcp/mcp-servers/config.json ``` You can then point `ollmcp` at that file at startup with `-j` / `--servers-json`. > [!IMPORTANT] > When using HTTP-based MCP servers, use the `streamable_http` type (not just `http`). Also check the [Common MCP endpoint paths](#common-mcp-endpoint-paths) section below for typical endpoints. Here a minimal working example let's say this is your `~/.config/ollmcp/mcp-servers/config.json`: ```json { "mcpServers": { "github": { "type": "streamable_http", "url": "https://api.githubcopilot.com/mcp/", "headers": { "Authorization": "Bearer mytoken" } } } } ``` > [!TIP] > When using GitHub MCP server, make sure to replace `"mytoken"` with your actual GitHub API token. With that file in place you can connect using: ``` ollmcp -j ~/.config/ollmcp/mcp-servers/config.json ``` Here you can find a GitHub issue related to this common pitfall: https://github.com/jonigl/mcp-client-for-ollama/issues/112#issuecomment-3446569030 #### Demo A short demo (asciicast) that should help anyone reproduce the working setup quickly. This example uses an [MCP server example with streamable HTTP protocol](https://github.com/jonigl/mcp-server-with-streamable-http-example) usage: [](https://asciinema.org/a/751387) #### Common MCP endpoint paths Streamable HTTP MCP servers typically expose the MCP endpoint at `/mcp` (e.g., `https://host/mcp`), while SSE servers commonly use `/sse` (e.g., `https://host/sse`). Below is an excerpt from the MCP specification (2025-06-18): > The server MUST provide a single HTTP endpoint path (hereafter referred to as the MCP endpoint) that supports both POST and GET methods. For example, this could be a URL like https://example.com/mcp. You can find more details in the [MCP specification version 2025-06-18 - Transports](https://modelcontextprotocol.io/specification/2025-06-18/basic/transports). ## Compatible Models The following Ollama models work well with tool use: - qwen2.5 - qwen3 - llama3.1 - llama3.2 - mistral For a complete list of Ollama models with tool use capabilities, visit the [official Ollama models page](https://ollama.com/search?c=tools). ### Ollama Cloud Models MCP Client for Ollama now supports [Ollama Cloud models](https://github.com/ollama/ollama/blob/main/docs/cloud.md), allowing you to use powerful cloud-hosted models with tool calling capabilities while leveraging your local MCP tools. Cloud models can run without a powerful local GPU, making it possible to access larger models that wouldn't fit on a personal computer. **Supported Ollama Cloud models include for example:** - `gpt-oss:20b-cloud` - `gpt-oss:120b-cloud` - `deepseek-v3.1:671b-cloud` - `qwen3-coder:480b-cloud` **To use Ollama Cloud models with this client:** 1. First, pull the cloud model: ```bash ollama pull gpt-oss:120b-cloud ``` 2. Run the client with your chosen cloud model: ```bash ollmcp --model gpt-oss:120b-cloud ``` > [!NOTE] > The model `deepseek-v3.1:671b-cloud` only supports tool use when thinking mode is turned off. You can toggle thinking mode in `ollmcp` by typing either `thinking-mode` or `tm`. For more information about Ollama Cloud, visit the [Ollama Cloud documentation](https://docs.ollama.com/cloud). ### How Tool Calls Work 1. The client sends your query to Ollama with a list of available tools 2. If Ollama decides to use a tool, the client: - Displays the tool execution with formatted arguments and syntax highlighting - Shows a Human-in-the-Loop confirmation prompt (if enabled) allowing you to review and approve the tool call - Extracts the tool name and arguments from the model response - Calls the appropriate MCP server with these arguments (only if approved or HIL is disabled) - Shows the tool response in a structured, easy-to-read format - Sends the tool result back to Ollama - If in Agent Mode, repeats the process if the model requests more tool calls 3. Finally, the client: - Displays the model's final response incorporating the tool results ### Agent Mode Some models may request multiple tool calls in a single conversation. The client supports an **Agent Mode** that allows for iterative tool execution: - When the model requests a tool call, the client executes it and sends the result back to the model - This process repeats until the model provides a final answer or reaches the configured loop limit - You can set the maximum number of iterations using the `loop-limit` (`ll`) command - The default loop limit is `3` to prevent infinite loops > [!NOTE] > If you want to prevent using Agent Mode, simply set the loop limit to `1`. #### Agent Mode Quick Demo: [](https://asciinema.org/a/476qpEamCX9TFQt4jNEXIgHxS) ## Where Can I Find More MCP Servers? You can explore a collection of MCP servers in the official [MCP Servers repository](https://github.com/modelcontextprotocol/servers). This repository contains reference implementations for the Model Context Protocol, community-built servers, and additional resources to enhance your LLM tool capabilities. ## Related Projects - **[Ollama MCP Bridge](https://github.com/jonigl/ollama-mcp-bridge)** - A Python API layer that sits in front of Ollama, automatically adding tools from multiple MCP servers to every chat request. This project provides a transparent proxy solution that pre-loads all MCP servers at startup and seamlessly integrates their tools into the Ollama API. - **[MCP Server with Streamable HTTP Example](https://github.com/jonigl/mcp-server-with-streamable-http-example)** - An example MCP server demonstrating the usage of the streamable HTTP protocol. ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## Acknowledgments - [Ollama](https://ollama.com/) for the local LLM runtime - [Model Context Protocol](https://modelcontextprotocol.io/) for the specification and examples - [Rich](https://rich.readthedocs.io/) for the terminal user interface - [Typer](https://typer.tiangolo.com/) for the modern CLI experience - [Prompt Toolkit](https://python-prompt-toolkit.readthedocs.io/) for the interactive command line interface - [UV](https://www.uvicorn.org/) for the lightning-fast Python package manager and virtual environment management - [Asciinema](https://asciinema.org/) for the demo recording --- Made with ❤️ by [jonigl](https://github.com/jonigl)