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MCP Ollama Server

show_model

Retrieve detailed specifications and configuration information for a specific Ollama model to understand its capabilities and parameters before use.

Instructions

Get detailed information about a specific model

Args:
    name: Name of the model to show information about

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes

Implementation Reference

  • The 'show_model' tool handler: an async function decorated with @mcp.tool() that fetches and formats detailed model information (license, format, size, system prompt, template) using ollama.show(name).
    @mcp.tool()
    async def show_model(name: str) -> str:
        """Get detailed information about a specific model
    
        Args:
            name: Name of the model to show information about
        """
        try:
            model_info = ollama.show(name)
            if not model_info:
                return f"No information found for model {name}"
    
            # Format the model information
            details = [
                f"Model: {name}",
                f"License: {model_info.get('license', 'Unknown')}",
                f"Format: {model_info.get('format', 'Unknown')}",
                f"Parameter Size: {model_info.get('parameter_size', 'Unknown')}",
                f"Quantization Level: {model_info.get('quantization_level', 'Unknown')}"
            ]
    
            # Add system prompt if available
            if model_info.get('system'):
                details.append(f"\nSystem Prompt:\n{model_info['system']}")
    
            # Add template if available
            if model_info.get('template'):
                details.append(f"\nTemplate:\n{model_info['template']}")
    
            return "\n".join(details)
        except Exception as e:
            return f"Error getting model information: {str(e)}"
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 tool retrieves 'detailed information' but doesn't specify what that entails, whether it's read-only, requires permissions, has rate limits, or what the output format is. This leaves significant gaps for a tool with no output schema.

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 efficiently structured with a clear purpose statement followed by parameter details in a concise format. Every sentence adds value without redundancy, making it easy to parse and understand quickly.

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. It doesn't explain what 'detailed information' includes, how results are returned, or any behavioral traits like error handling. For a tool with no structured support, this leaves the agent under-informed about its operation.

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 description adds meaningful context for the single parameter 'name' by explaining it's 'Name of the model to show information about', which clarifies its purpose beyond the schema's basic title. With 0% schema description coverage and only one parameter, this effectively compensates for the lack of schema 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 a specific verb ('Get detailed information') and resource ('about a specific model'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_models' (which likely lists multiple models) or 'ask_model' (which likely queries a model), missing full sibling distinction.

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 sibling tools like 'list_models' or 'ask_model', nor does it specify prerequisites or contexts for usage, leaving the agent to infer based on tool names alone.

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

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