Which integrations are available for this server?

Provides comprehensive management of local Ollama installations, including listing and removing models, chatting with locally installed models, starting and monitoring the Ollama server, performing health checks, and getting model recommendations for specific tasks based on locally available models.

How do I use Ollama MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Ollama MCP Server list my local models and suggest one for coding" 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.

Ollama MCP Server

by paolodalprato

Overview Schema Related Servers Score Discussions

Python

Local

Ollama MCP Server

License: MIT Python 3.10+ MCP Compatible

A self-contained Model Context Protocol (MCP) server for local Ollama management, developed with Claude AI assistance. Features include listing local models, chatting, starting/stopping the server, and a 'local model advisor' to suggest the best local model for a given task. The server is designed to be a robust, dependency-free, and cross-platform tool for managing a local Ollama instance.

⚠️ Current Testing Status

Currently tested on: Windows 11 with NVIDIA RTX 4090
Status: Beta on Windows, Other Platforms Need Testing
Cross-platform code: Ready for Linux and macOS but requires community testing
GPU support: NVIDIA fully tested, AMD/Intel/Apple Silicon implemented but needs validation

We welcome testers on different platforms and hardware configurations! Please report your experience via GitHub Issues.

Related MCP server: Ollama MCP Server

🎯 Key Features

🔧 Self-Contained Architecture

Zero External Dependencies: No external MCP servers required
MIT License Ready: All code internally developed and properly licensed
Enterprise-Grade: Professional error handling with actionable troubleshooting

🌐 Universal Compatibility

Cross-Platform: Windows, Linux, macOS with automatic platform detection
Multi-GPU Support: NVIDIA, AMD, Intel detection with vendor-specific optimizations
Smart Installation Discovery: Automatic Ollama detection across platforms

⚡ Complete Local Ollama Management

Model Operations: List, suggest, and remove local models.
Server Control: Start and monitor the Ollama server with intelligent process management.
Direct Chat: Communicate with any locally installed model.
System Analysis: Assess hardware compatibility and monitor resources.

🚀 Quick Start

Installation

git clone https://github.com/paolodalprato/ollama-mcp-server.git
cd ollama-mcp-server
pip install -e .

Configuration

Add to your MCP client configuration (e.g., Claude Desktop config.json):

{
  "mcpServers": {
    "ollama-mcp": {
      "command": "python",
      "args": [
        "X:\\PATH_TO\\ollama-mcp-server\\src\\ollama_mcp\\server.py"
      ],
      "env": {}
    }
  }
}

Note: Adjust the path to match your installation directory. On Linux/macOS, use forward slashes: /path/to/ollama-mcp-server/src/ollama_mcp/server.py

Requirements

Python 3.10+ (required by MCP SDK dependency)
Ollama installed and accessible in PATH
MCP-compatible client (Claude Desktop, etc.)

Ollama Configuration Compatibility

This MCP server automatically respects your Ollama configuration. If you have customized your Ollama setup (e.g., changed the models folder via OLLAMA_MODELS environment variable), the MCP server will work seamlessly without any additional configuration.

🛠️ Available Tools

Model Management

list_local_models - List all locally installed models with their details.
local_llm_chat - Chat directly with any locally installed model.
remove_model - Safely remove a model from local storage.
suggest_models - Recommends the best locally installed model for a specific task (e.g., "suggest a model for coding").

Server and System Operations

start_ollama_server - Starts the Ollama server if it's not already running.
ollama_health_check - Performs a comprehensive health check of the Ollama server.
system_resource_check - Analyzes system hardware and resource availability.

Diagnostics

test_model_responsiveness - Checks the responsiveness of a specific local model by sending a test prompt, helping to diagnose performance issues.
select_chat_model - Presents a list of available local models to choose from before starting a chat.

💬 How to Interact with Ollama-MCP

Ollama-MCP works through your MCP client (like Claude Desktop) - you don't interact with it directly. Instead, you communicate with your MCP client using natural language, and the client translates your requests into tool calls.

Basic Interaction Pattern

You speak to your MCP client in natural language, and it automatically uses the appropriate ollama-mcp tools:

You: "List my installed Ollama models"
→ Client calls: list_local_models
→ You get: Formatted list of your models

You: "Chat with llama3.2: explain machine learning"  
→ Client calls: local_llm_chat with model="llama3.2" and message="explain machine learning"
→ You get: AI response from your local model

You: "Check if Ollama is running"
→ Client calls: ollama_health_check  
→ You get: Server status and troubleshooting if needed

Example Interactions

Model Management

"What models do I have installed?" → list_local_models
"I need a model for creative writing, which of my models is best?" → suggest_models
"Remove the old mistral model to save space" → remove_model

System Operations

"Start Ollama server" → start_ollama_server
"Is my system capable of running large AI models?" → system_resource_check

AI Chat

"Chat with llama3.2: write a Python function to sort a list" → local_llm_chat
"Use deepseek-coder to debug this code: [code snippet]" → local_llm_chat
"Ask phi3.5 to explain quantum computing simply" → local_llm_chat

Key Points

No Direct Commands: You never call ollama_health_check() directly
Natural Language: Speak normally to your MCP client
Automatic Tool Selection: The client chooses the right tool based on your request
Conversational: You can ask follow-up questions and the client maintains context

🎯 Real-World Use Cases

Daily Development Workflow

"I need to work on a coding project. Which of my local models is best for coding? Let's check its performance and then ask it a question."

This could trigger:

suggest_models - Recommends the best local model for "coding".
test_model_responsiveness - Checks if the recommended model is responsive.
local_llm_chat - Starts a chat with the model.

Model Management Session

"Show me what models I have and recommend one for writing a story. Then let's clean up any old models I don't need."

Triggers:

list_local_models - Current inventory
suggest_models - Recommends a local model for "writing a story".
remove_model - Cleanup unwanted models.

Troubleshooting Session

"Ollama isn't working. Check what's wrong, try to fix it, and test with a simple chat."

Triggers:

ollama_health_check - Diagnose issues
start_ollama_server - Attempt to start server
local_llm_chat - Verify working with test message

🏗️ Architecture

Design Principles

Self-Contained: Zero external MCP server dependencies
Fail-Safe: Comprehensive error handling with actionable guidance
Cross-Platform First: Universal Windows/Linux/macOS compatibility
Enterprise Ready: Professional-grade implementation and documentation

Technical Highlights

Internal Process Management: Advanced subprocess handling with timeout control
Multi-GPU Detection: Platform-specific GPU identification without confusing metrics
Intelligent Model Selection: Fallback to first available model when none specified
Progressive Health Monitoring: Smart server startup detection with detailed feedback

📋 System Compatibility

Operating Systems

Windows: Full support with auto-detection in Program Files and AppData ✅ Tested
Linux: XDG configuration support with package manager integration ⚠️ Needs Testing
macOS: Homebrew detection with Apple Silicon GPU support ⚠️ Needs Testing

GPU Support

NVIDIA: Full detection via nvidia-smi with memory and utilization info ✅ Tested RTX 4090
AMD: ROCm support via vendor-specific tools ⚠️ Needs Testing
Intel: Basic detection via system tools ⚠️ Needs Testing
Apple Silicon: M1/M2/M3 detection with unified memory handling ⚠️ Needs Testing

Hardware Requirements

Minimum: 4GB RAM, 2GB free disk space
Recommended: 8GB+ RAM, 10GB+ free disk space
GPU: Optional but recommended for model acceleration

🔧 Development

Project Structure

ollama-mcp-server/
├── src/
│   ├── __init__.py               # Defines the package version
│   └── ollama_mcp/
│       ├── __init__.py           # Makes 'ollama_mcp' a package
│       ├── server.py             # Main MCP server implementation
│       ├── client.py             # Ollama API client
│       ├── config.py             # Configuration management
│       ├── model_manager.py      # Local model operations
│       ├── hardware_checker.py   # System hardware analysis
│       └── ... (and other modules)
├── tests/
│   ├── test_client.py            # Unit tests for the client
│   └── test_tools.py             # Integration tests for tools
├── .gitignore                    # Specifies intentionally untracked files
└── pyproject.toml                # Project configuration and dependencies

Key Technical Achievements

Self-Contained Implementation

Challenge: Eliminated external desktop-commander dependency
Solution: Internal process management with advanced subprocess handling
Result: Zero external MCP dependencies, MIT license compatible

Intelligent GPU Detection

Challenge: Complex VRAM reporting causing user confusion
Solution: Simplified to GPU name display only
Result: Clean, reliable hardware identification

Enterprise Error Handling

Implementation: 6-level exception framework with specific error types
Coverage: Platform-specific errors, process failures, network issues
UX: Actionable troubleshooting steps for every error scenario

🤝 Contributing

We welcome contributions! Areas where help is especially appreciated:

Platform Testing: Different OS and hardware configurations ⭐ High Priority
GPU Vendor Support: Additional vendor-specific detection
Performance Optimization: Startup time and resource usage improvements
Documentation: Usage examples and integration guides
Testing: Edge cases and error condition validation

Immediate Testing Needs

Linux: Ubuntu, Fedora, Arch with various GPU configurations
macOS: Intel and Apple Silicon Macs with different Ollama installations
GPU Vendors: AMD ROCm, Intel Arc, Apple unified memory
Edge Cases: Different Python versions, various Ollama installation methods

Development Setup

git clone https://github.com/paolodalprato/ollama-mcp-server.git
cd ollama-mcp-server

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Code formatting
black src/
isort src/

# Type checking
mypy src/

🐛 Troubleshooting

Common Issues

Ollama Not Found

# Verify Ollama installation
ollama --version

# Check PATH configuration
which ollama  # Linux/macOS
where ollama  # Windows

Server Startup Failures

# Check port availability
netstat -an | grep 11434

# Manual server start for debugging
ollama serve

Permission Issues

Windows: Run as Administrator if needed
Linux/macOS: Check user permissions for service management

Platform-Specific Issues

If you encounter issues on Linux or macOS, please report them via GitHub Issues with:

Operating system and version
Python version
Ollama version and installation method
GPU hardware (if applicable)
Complete error output

📊 Performance

Typical Response Times (Windows RTX 4090)

Health Check: <500ms
Model List: <1 second
Server Start: 1-15 seconds (hardware dependent)
Model Chat: 2-30 seconds (model and prompt dependent)

Resource Usage

Memory: <50MB for MCP server process
CPU: Minimal when idle, scales with operations
Storage: Configuration files and logs only

🔐 Security

Data Flow: User → MCP Client (Claude) → ollama-mcp-server → Local Ollama → back through chain

👨‍💻 About This Project

This is my first MCP server, created by adapting a personal tool I had developed for my own Ollama management needs.

The Problem I Faced

I started using Claude to interact with Ollama because it allows me to use natural language instead of command-line interfaces. Claude also provides capabilities that Ollama alone doesn't have, particularly intelligent model suggestions based on both my system capabilities and specific needs.

My Solution

I built this MCP server to streamline my own workflow, and then refined it into a stable tool that others might find useful. The design reflects real usage patterns:

Self-contained: No external dependencies that can break
Intelligent error handling: Clear guidance when things go wrong
Cross-platform: Works consistently across different environments
Practical tools: Features I actually use in daily work

Design Philosophy

I initially developed this for my personal use to manage Ollama models more efficiently. When the MCP protocol became available, I transformed my personal tool into an MCP server to share it with others who might find it useful.

Development Approach: This project was developed with Claude using "vibe coding" - an iterative, conversational development process where AI assistance helped refine both the technical implementation and user experience. It's a practical example of AI-assisted development creating tools for AI management. Jules was also involved in the final refactoring phase.

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

Ollama Team: For the excellent local AI platform
MCP Project: For the Model Context Protocol specification
Claude Desktop/Code by Anthropic: As tool in MCP client implementation, testing and refactoring
Jules by Google: As tool in refactoring

📞 Support

Bug Reports: GitHub Issues
Feature Requests: GitHub Issues
Community Discussion: GitHub Discussions

Changelog

v0.9.0 (August 17, 2025): Critical bugfix release - Fixed datetime serialization issue that prevented model listing from working with Claude Desktop. All 9 tools now verified working correctly.
August 2025: Project refactoring and enhancements. Overhauled the architecture for modularity, implemented a fully asynchronous client, added a test suite, and refined the tool logic based on a "local-first" philosophy.
July 2025: Initial version created by Paolo Dalprato with Claude AI assistance.

For detailed changes, see CHANGELOG.md.

Status: Beta on Windows, Other Platforms Need Testing
Testing: Windows 11 + RTX 4090 validated, Linux/macOS require community validation
License: MIT
Dependencies: Zero external MCP servers required

Available Tools

9 tools

list_local_modelsA

List all locally installed Ollama models with details

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

A3.7/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It states it's a listing operation but doesn't describe what 'details' include, whether it requires Ollama server to be running, or how it handles errors. Significant behavioral context is missing for a tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence with zero waste - every word contributes essential information. Front-loaded with the core purpose, appropriately sized for a simple listing tool with no parameters.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter listing tool with no output schema, the description provides the core purpose but lacks important context about what 'details' includes and behavioral aspects. Without annotations or output schema, more completeness about return format and operational requirements would be helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0 parameters and 100% schema description coverage, the baseline is 4. The description appropriately doesn't discuss parameters since none exist, and the schema already fully documents this. No additional parameter information is needed or provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('List') and resource ('locally installed Ollama models'), with additional scope ('with details') that distinguishes it from simpler listing tools. It precisely communicates what the tool does without ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context (when you need to see installed models), but doesn't explicitly state when to use this tool versus alternatives like 'suggest_models' or 'select_chat_model'. No guidance on prerequisites or exclusions is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

local_llm_chatC

Chat with a local Ollama model

ParametersJSON Schema

Name	Required	Description
`message`	Yes	Message to send to the model
`model`	No	Model name (optional, uses first available if not specified)
`temperature`	No	Generation temperature 0.0-1.0 (default: 0.7)

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure but offers minimal insight. It states the tool chats with a model but doesn't describe response format, error handling, rate limits, or whether it maintains conversation state. For a chat tool with zero annotation coverage, this is a significant gap in transparency about how the tool behaves beyond basic functionality.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place by conveying essential information concisely.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a chat tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns (e.g., text response, structured data), error conditions, or dependencies like server status. For a tool that likely involves network calls and model interactions, more context is needed to guide effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all three parameters (message, model, temperature) with their types, descriptions, and defaults. The description adds no additional parameter semantics beyond what the schema provides, such as examples or constraints. Baseline 3 is appropriate when the schema handles all parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Chat with') and resource ('a local Ollama model'), making the purpose immediately understandable. It distinguishes from siblings like 'list_local_models' or 'remove_model' by focusing on interactive conversation rather than management tasks. However, it doesn't specify the exact scope (e.g., single-turn vs. multi-turn) or differentiate from 'select_chat_model' which might have overlapping functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance is provided on when to use this tool versus alternatives like 'select_chat_model' or 'test_model_responsiveness'. The description implies usage for general chat interactions but doesn't mention prerequisites (e.g., server running), exclusions, or comparative scenarios. This leaves the agent to infer context from tool names alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

ollama_health_checkB

Check Ollama server health and provide diagnostics

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

B3.4/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool performs a health check and provides diagnostics, but doesn't describe what specific diagnostics are included (e.g., server status, version, connectivity), whether it has side effects (e.g., pinging the server), or any rate limits or permissions required. For a diagnostic tool with zero annotation coverage, this leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's function ('Check Ollama server health and provide diagnostics'). It is front-loaded with the core purpose, has zero wasted words, and is appropriately sized for a simple, parameterless tool. Every part of the sentence earns its place by conveying essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on diagnostics content or behavioral traits. Without annotations or output schema, the description should ideally specify what 'health' and 'diagnostics' include to be more complete, but it meets the bare minimum for this simple context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and the input schema has 100% description coverage (though empty). The description doesn't need to explain parameters, so it appropriately avoids redundant information. Since there are no parameters, the baseline is 4, as the description correctly focuses on the tool's purpose without unnecessary parameter details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('check', 'provide diagnostics') and identifies the target resource ('Ollama server health'). It distinguishes this from siblings like 'system_resource_check' (general system) and 'test_model_responsiveness' (model-specific), but doesn't explicitly contrast them. The purpose is unambiguous but could be more precise about what 'health' entails.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when health diagnostics are needed, such as before operations or troubleshooting. However, it provides no explicit guidance on when to use this tool versus alternatives like 'system_resource_check' (which might overlap) or 'start_ollama_server' (which might be a prerequisite). Usage is contextually implied but lacks clear when/when-not directives or named alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

remove_modelC

Remove a model from local storage

ParametersJSON Schema

Name	Required	Description	Default
`model_name`	Yes	Name of the model to remove
`force`	No	Force removal even if it's the default model

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action is 'Remove' (implying destructive mutation) but lacks details on permissions needed, whether removal is reversible, error handling (e.g., if model doesn't exist), or side effects (e.g., impact on default settings). The force parameter hint in the schema suggests some behavioral nuance, but the description doesn't elaborate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, direct sentence with zero wasted words. It front-loads the core action and resource, making it highly efficient and easy to parse. Every word earns its place by conveying essential purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's destructive nature, lack of annotations, and no output schema, the description is insufficiently complete. It doesn't address critical context like what 'local storage' entails, confirmation requirements, success/failure indicators, or integration with sibling tools (e.g., checking models first with 'list_local_models'). For a mutation tool, this leaves significant gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents both parameters (model_name and force). The description adds no parameter-specific information beyond what's in the schema, such as format examples for model_name or implications of using force. This meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Remove') and target resource ('a model from local storage'), making the purpose immediately understandable. However, it doesn't differentiate from potential sibling tools like 'list_local_models' or 'select_chat_model' beyond the obvious destructive nature, which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a model to exist locally), exclusions (e.g., not for remote models), or relationships with siblings like 'list_local_models' for verification. This leaves the agent with minimal context for decision-making.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

select_chat_modelC

Present available models and help user select one for chat

ParametersJSON Schema

Name	Required	Description	Default
`message`	Yes	The message the user wants to send after selecting a model

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions presenting models and helping with selection, but fails to detail how this is done (e.g., interactive UI, list display, filtering criteria), what happens after selection (e.g., does it initiate chat automatically?), or any constraints like rate limits or permissions. This leaves significant gaps in understanding the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise and front-loaded, consisting of a single, clear sentence: 'Present available models and help user select one for chat'. Every word contributes directly to the tool's purpose, with no wasted information or redundancy, making it efficient for quick comprehension.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (involving model selection and chat initiation), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., selected model details, chat initiation status), behavioral nuances, or how it integrates with siblings like 'local_llm_chat'. For a tool with one parameter but potential behavioral depth, more context is needed to fully guide an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with one parameter 'message' documented as 'The message the user wants to send after selecting a model'. The description adds no additional meaning beyond this schema, as it doesn't explain parameter usage or constraints. Given the high schema coverage, a baseline score of 3 is appropriate, as the schema adequately handles parameter semantics without extra description input.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Present available models and help user select one for chat'. It specifies the verb ('present' and 'help select') and resource ('models'), making the function understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_local_models' or 'suggest_models', which may have overlapping functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides minimal guidance on when to use this tool. It implies usage when a user needs to select a model for chat, but offers no explicit context on when to choose this over alternatives like 'list_local_models' or 'suggest_models', nor does it mention prerequisites or exclusions. This lack of comparative guidance reduces its effectiveness for an AI agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

start_ollama_serverB

Attempt to start Ollama server if it's not running

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

B3.3/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden. It mentions the action ('start') and condition ('if it's not running'), but lacks details on behavioral traits such as what happens if the server is already running (e.g., error, no-op, success), required permissions, timeouts, or side effects. This is a significant gap for a tool that likely involves system operations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the purpose and condition without any fluff. It is front-loaded and every word earns its place, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of starting a server (a system operation with potential side effects), no annotations, and no output schema, the description is incomplete. It lacks crucial details like what the tool returns (e.g., success/failure, server status), error handling, or dependencies, leaving significant gaps for an AI agent to use it effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, and the baseline for 0 parameters is 4, as it avoids unnecessary details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('start') and target resource ('Ollama server'), and includes the conditional 'if it's not running' which adds specificity. However, it doesn't explicitly differentiate from sibling tools like 'ollama_health_check' or 'system_resource_check' which might also interact with server status.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context ('if it's not running'), suggesting this tool should be used when the server is down. However, it doesn't provide explicit guidance on when to use alternatives like 'ollama_health_check' to check status first, or mention prerequisites or when-not-to-use scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

suggest_modelsB

Suggests the best locally installed model for a specific task based on user needs.

ParametersJSON Schema

Name	Required	Description	Default
`user_needs`	Yes	Description of what the user wants to do with the model (e.g., 'I want to write code', 'I need help with creative writing', 'I want to analyze documents')
`priority`	No	Priority: 'speed' for fast responses, 'quality' for best results, 'balanced' for compromise

TDQS

B3.2/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool 'suggests' models but doesn't describe what the suggestion looks like (e.g., ranked list, single recommendation), whether it requires model availability checks, or any performance characteristics. The description is minimal and doesn't provide adequate behavioral context for a tool with no annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose. Every word earns its place: 'Suggests' (action), 'best locally installed model' (resource and scope), 'for a specific task' (context), 'based on user needs' (input basis). No wasted words or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the suggestion output looks like (e.g., model names, scores, explanations), doesn't mention potential errors (e.g., no models installed), and provides minimal behavioral context. Given the complexity of model selection and lack of structured output documentation, the description should do more.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already fully documents both parameters. The description adds no additional parameter semantics beyond what's in the schema - it mentions 'user needs' and 'priority' but provides no extra context about format, examples, or constraints. Baseline 3 is appropriate when schema does all the work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Suggests the best locally installed model for a specific task based on user needs.' It specifies the verb (suggests), resource (locally installed model), and scope (for a specific task). However, it doesn't explicitly differentiate from siblings like 'select_chat_model' or 'list_local_models' beyond the 'suggests' vs 'selects' or 'lists' distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context ('for a specific task based on user needs') and scope ('locally installed'), but doesn't provide explicit guidance on when to use this tool versus alternatives like 'select_chat_model' or 'list_local_models'. It mentions the 'priority' parameter which hints at trade-offs, but lacks clear when/when-not instructions or named alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

system_resource_checkC

Check system resources and compatibility

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

C2.5/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden but only states the action without behavioral details. It doesn't disclose whether this is read-only, what specific resources are checked (e.g., CPU, memory, disk), compatibility with what systems, or any side effects like performance impact. This leaves significant gaps in understanding the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient phrase that is front-loaded with the core action. However, it's slightly under-specified, as it could benefit from a bit more detail without becoming verbose, but it avoids unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, no output schema, and a simple input schema with 0 parameters, the description is incomplete. It doesn't explain what the check entails, what output to expect, or how it differs from sibling tools, making it inadequate for an agent to use effectively without additional context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add parameter semantics, but this is acceptable given the lack of parameters, aligning with the baseline for zero parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Check system resources and compatibility' restates the tool name 'system_resource_check' with minimal elaboration, making it tautological. It specifies the action ('check') and target ('system resources and compatibility'), but lacks detail on what resources or compatibility aspects are examined, and doesn't differentiate from siblings like 'ollama_health_check' or 'test_model_responsiveness'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites, context (e.g., before model operations), or exclusions, leaving the agent to infer usage from the name alone among siblings like health checks or model testing tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

test_model_responsivenessC

Test the responsiveness of a specific model by sending a simple prompt.

ParametersJSON Schema

Name	Required	Description	Default
`model_name`	Yes	The name of the model to test.

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool tests responsiveness by sending a simple prompt, but lacks details on what 'responsiveness' entails (e.g., latency, availability, error handling), whether it's a read-only or mutative operation, or any side effects like rate limits or authentication needs. This leaves significant gaps in understanding the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded with the core action and resource, making it easy to parse quickly, and every part of the sentence contributes essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete for a tool that tests model responsiveness. It doesn't explain what 'responsiveness' means, what the simple prompt entails, or what the expected output or success criteria are. For a testing tool with no structured behavioral data, this leaves too many open questions about how to interpret results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with the single parameter 'model_name' clearly documented. The description adds no additional meaning beyond the schema, such as examples or constraints on model names. With high schema coverage, the baseline score of 3 is appropriate as the schema handles the parameter documentation adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Test the responsiveness') and target resource ('a specific model'), providing a specific verb+resource combination. However, it doesn't differentiate this tool from potential alternatives like 'ollama_health_check' or 'system_resource_check' among the sibling tools, which might also test system or model status.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or compare it to sibling tools like 'ollama_health_check' or 'local_llm_chat', leaving the agent to infer usage context without explicit direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

B3.2/5.0

Disambiguation4/5

Most tools have distinct purposes, but 'local_llm_chat' and 'select_chat_model' could cause confusion as they both relate to model selection for chatting, with some functional overlap. The other tools are clearly differentiated, such as health checks, model management, and resource monitoring.

Naming Consistency3/5

The naming is mixed, with some tools using snake_case (e.g., 'list_local_models', 'local_llm_chat') and others using more verbose or inconsistent patterns (e.g., 'ollama_health_check', 'system_resource_check'). While readable, there is no uniform verb_noun convention, and terms like 'check' and 'test' are used interchangeably.

Tool Count5/5

With 9 tools, the count is well-scoped for managing and interacting with Ollama models and servers. It covers installation, selection, testing, health, and resource checks without being overwhelming or too sparse, fitting the server's purpose effectively.

Completeness4/5

The tool set provides good coverage for model lifecycle (list, remove, test) and server management (health, start), with minor gaps such as updating models or handling advanced configurations. Core workflows like chatting and diagnostics are supported, allowing agents to perform most essential tasks.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

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