model_info

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

No annotations provided, so description carries full burden. It discloses that the tool queries all providers if no provider arg is given and lists the metadata returned. This is sufficient for a read-only inspection tool, though it doesn't mention potential latency or caching.

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

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

Two sentences, front-loaded with key action and resource, no redundant words. Every sentence adds essential information.

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

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

Given no output schema, the description adequately lists the metadata fields. However, it could mention the output format (e.g., JSON object) and edge cases (e.g., model not found). Still, for a simple info tool, it is largely complete.

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

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

Schema covers all parameters with descriptions, but the description adds distinct value by explaining that omitting the provider arg queries all detected providers, clarifying behavior beyond the schema's enum list.

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

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

Description uses specific verb 'show' and resource 'detailed metadata for a specific model', listing exact fields (family, parameter size, quantization, context length). It distinguishes from sibling tools like list_models (list all) and suggest_model (recommendations) by focusing on single-model details.

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

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

Description clearly states the tool is for inspecting a specific model and mentions the provider argument behavior. However, it does not explicitly state when not to use it (e.g., if you need a list, use list_models instead), leaving some inference to the agent.

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