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get_model

Retrieve detailed configuration information for AI models, including system prompts and parameter settings, to support model management and deployment decisions.

Instructions

Get details for a specific model including system prompt and parameters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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. It states it 'Get details' but doesn't disclose behavioral traits such as whether it's a read-only operation (implied but not stated), error handling (e.g., if model_id is invalid), authentication needs, rate limits, or response format. For a tool with no annotations, this leaves significant gaps in understanding its 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 front-loads the core action ('Get details for a specific model') and adds useful specifics ('including system prompt and parameters'). There is no wasted text, and it's appropriately sized for the tool's complexity.

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 has an output schema (which handles return values), no annotations, and low parameter coverage, the description is minimally adequate. It states the purpose but lacks behavioral context and parameter details. For a simple read operation with output schema support, it meets basic needs but could be more informative about usage and errors.

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 0%, with one parameter 'model_id' documented only as 'Model ID' in the schema. The description doesn't add any parameter-specific semantics beyond implying 'model_id' is needed to identify the model. It doesn't explain format, constraints, or examples. With low coverage, the description fails to compensate, resulting in a baseline score.

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 verb 'Get' and the resource 'details for a specific model', specifying what information is retrieved ('including system prompt and parameters'). It distinguishes from sibling tools like 'list_models' (which lists models) and 'get_models_config' (which gets configuration). However, it doesn't explicitly contrast with these siblings in the text.

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 (e.g., needing a model ID), when not to use it (e.g., for listing models), or refer to sibling tools like 'list_models' for broader queries. The description assumes context without explicit instructions.

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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