A simple yet powerful Python client for interacting with Model Context Protocol (MCP) servers using Ollama, allowing local LLMs to use tools.

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MCP Client for Ollama (ollmcp)

PyPI - Downloads

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
📋 MCP Prompts Support: Browse, invoke, and manage prompts from MCP servers with argument collection, preview, and safe rollback
☁️ 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
📜 History Management: View full conversation history, export to JSON for backup/analysis, and import previous sessions for continuity
🔍 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
⏹️ Abort Generation: You can abort model generation at any time by pressing 'a' during response streaming

Requirements

Python 3.10+ (Installation guide)
Ollama running locally (Installation guide)
UV package manager (Installation guide)

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 for typical endpoints.
--servers-json, -j: Path to a JSON file with server configurations. See 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 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:

ollmcp main interface

Command	Shortcut	Description
`abort`	`a`	While model is generating, abort the current response generation
`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
`export-history`	`eh`	Export chat history to a JSON file
`full-history`	`fh`	Display all conversation history
`help`	`h`	Display help and available commands
`import-history`	`ih`	Import chat history from a JSON file
`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
`prompts`	`pr`	Browse and view all available MCP prompts
`/prompt_name`	-	Invoke a specific prompt by name (e.g., `/summarize`)
`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
`thinking-mode`	`tm`	Toggle thinking mode on supported models
`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:

ollmcp tool and server selection interface

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:

ollmcp model selection interface

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:

ollmcp model configuration interface

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
🔄 Session Mode: Auto-approve all tools for the current query session
🛑 Query Abort: Abort entire query without saving to history

HIL Confirmation Display

When HIL is enabled, you'll see a confirmation prompt before each tool execution:

Example:

ollmcp HIL confirmation screenshot

HIL Confirmation Options

When prompted, you can choose from the following options:

y/yes: Execute this specific tool call
n/no: Skip this tool call and continue with the query
s/session: Execute this and all subsequent tool calls for the current query without further prompts
d/disable: Permanently disable HIL confirmations (can be re-enabled with hil command)
a/abort: Abort the entire query immediately without saving to history

[!TIP]
The session option is particularly useful when the model needs to execute multiple tools in sequence. Instead of confirming each one individually, you can approve all tools for the current query session, then HIL will reset automatically for the next query.

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
Session Auto-Approve: Use "session" during confirmation to approve all tools for current query
Query Abort: Use "abort" during confirmation to immediately stop the query without saving
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
Flexible Workflow: Session mode for efficient multi-tool queries, individual approval for sensitive operations
Clean Abort: Stop problematic queries immediately without polluting conversation history
Peace of Mind: Full visibility and control over automated actions

MCP Prompts

MCP Prompts provide reusable, server-defined conversation starters and context templates. Servers can expose prompts with descriptions, required arguments, and pre-formatted messages that help you quickly start specific types of conversations or inject structured context into your chat.

Features

📋 Browse Prompts: View all available prompts from connected servers with descriptions and argument requirements
⚡️ Quick Invocation: Use /prompt_name syntax to instantly invoke any prompt
🔤 Autocomplete: Type / to see prompt suggestions with fuzzy matching
📝 Argument Collection: Interactive prompts guide you through required parameters
👁️ Preview: Review prompt content before injection to ensure it fits your needs
🎯 Flexible Injection: Choose to execute immediately or inject-only (add to history without triggering model)
🧠 Context-Aware: Automatically adapts behavior based on whether prompt ends with user or assistant message
🔄 Safe Rollback: Automatic history cleanup if you abort or encounter errors
💬 Text Content: Supports text-based prompt messages (image/audio/resource support coming soon)

How to Use MCP Prompts

Browse Available Prompts:

code

prompts  # or 'pr'

This displays all prompts grouped by server, showing their names, required arguments, and descriptions.

Invoke a Prompt:

code

/prompt_name

For example, if a server provides a "summarize" prompt:

code

/summarize

Autocomplete:

Type / to see all available prompts with descriptions
Continue typing to filter prompts with fuzzy matching
Use arrow keys to navigate and press Enter to select

[!TIP]
Prompts are discovered automatically when you connect to MCP servers. If a server supports prompts, they'll be available immediately in the prompts list and autocomplete.

Workflow:

1. Type /prompt_name or select from autocomplete

2. If the prompt requires arguments, you'll be prompted to provide them

3. Review the prompt preview showing what will be injected

4. Choose how to use the prompt:

y/yes (default): Send the prompt to the model and get a response
For prompts ending with a user message: Uses that message as the query
For prompts ending with an assistant message: Adds "Please respond based on the above context." as the query
i/inject: Just add the prompt to conversation history without triggering the model (lets you type your own query afterward)
n/no: Cancel and return to chat

5. The prompt is injected based on your choice

6. If you abort during model generation (press 'a'), changes are automatically rolled back

Example:

ollmcp prompt feature screenshot

[!WARNING]
Content Type Limitations: MCP Prompts currently support text content only. The following content types are not yet supported and will be automatically skipped:
- 🖼️ Images - Image content in prompts
- 🎵 Audio - Audio content in prompts
- 📦 Resources - Embedded resource content
If a prompt contains these unsupported types, you'll see a warning during the preview, and only the text portions will be injected. Make sure your prompt still makes sense without the multimedia content before proceeding. Full multimedia support is planned for a future release.

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:

ollmcp ollama performance metrics screenshot

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.

History Management

The History Management feature allows you to view, export, and import your conversation history. This is useful for:

📜 Full History View: Review all conversations from your current session
💾 Export: Save conversations to JSON files for backup or analysis
📥 Import: Load previous conversation history to continue where you left off
🔄 Portability: Share or transfer conversations between sessions

History Commands

View Full History:

code

full-history  # or 'fh'

Displays all conversation history from the current session in a formatted view, showing both queries and responses.

Export History:

code

export-history  # or 'eh'

Exports your current chat history to a JSON file. You can specify a custom filename or use the default timestamp-based name (e.g., ollmcp_chat_history_2026-01-05_143022.json). Files are saved to ~/.config/ollmcp/history/ directory. The command includes file overwrite protection.

Import History:

code

import-history  # or 'ih'

Imports a previously exported chat history from a JSON file. The command validates the JSON structure to ensure compatibility. Imported history is added to your current conversation context.

History Storage:

Export location: ~/.config/ollmcp/history/
Default filename format: ollmcp_chat_history_YYYY-MM-DD_HHMMSS.json
JSON format includes both queries and responses with proper structure validation

Benefits:

Session Continuity: Resume conversations across different sessions
Backup: Keep records of important conversations
Analysis: Export history for external analysis or review
Sharing: Share conversation context with team members
Testing: Import test conversations for development and debugging

[!TIP]
When exporting, if you don't provide a filename, the system automatically generates a timestamped filename to prevent accidental overwrites.

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

MCP Prompts Autocomplete

Type / to trigger prompt autocomplete
Fuzzy matching on prompt names and descriptions
Shows prompt arguments and descriptions in the menu
Terminal-width-aware description truncation
Arrow (▶) highlights the best match

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:

code

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.

[!TIP]
Type / after the prompt symbol to see autocomplete suggestions for available MCP prompts.

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:

code

~/.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 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:

code

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 usage:

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.

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.

Ollama Cloud Models

MCP Client for Ollama now supports Ollama Cloud models, 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.

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:

Where Can I Find More MCP Servers?

You can explore a collection of MCP servers in the official MCP Servers repository.

This repository contains reference implementations for the Model Context Protocol, community-built servers, and additional resources to enhance your LLM tool capabilities.

**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** - An example MCP server demonstrating the usage of the streamable HTTP protocol.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

Ollama for the local LLM runtime
Model Context Protocol for the specification and examples
Rich for the terminal user interface
Typer for the modern CLI experience
Prompt Toolkit for the interactive command line interface
UV for the lightning-fast Python package manager and virtual environment management
Asciinema for the demo recording

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Made with ❤️ by jonigl

Mcp Client For Ollama

Documentation

MCP Client for Ollama (ollmcp)

Table of Contents

Overview

Features

Requirements

Quick Start

Usage

Command-line Arguments

MCP Server Configuration:

Ollama Configuration:

General Options:

Usage Examples

Interactive Commands

Tool and Server Selection

Model Selection

Advanced Model Configuration

System Prompt

Key Parameters

Commands

Example Configurations

Server Reloading for Development

Human-in-the-Loop (HIL) Tool Execution

HIL Confirmation Display

HIL Confirmation Options

Human-in-the-Loop (HIL) Configuration

MCP Prompts

Features

How to Use MCP Prompts

Performance Metrics

Performance Metrics Configuration

History Management

History Commands

Autocomplete and Prompt Features

Typer Shell Autocompletion

FZF-style Autocomplete

MCP Prompts Autocomplete

Contextual Prompt

Configuration Management

Server Configuration Format

Tips: where to put MCP server configs and a working example

Demo

Common MCP endpoint paths

Compatible Models

Ollama Cloud Models

How Tool Calls Work

Agent Mode

Agent Mode Quick Demo:

Where Can I Find More MCP Servers?

Related Projects

License

Acknowledgments

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