ppt_list_ai_models

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

With no annotations, the description carries the burden. It adds useful behavioral guidance (repeat exact model IDs, don't invent categories/recommendations), but doesn't disclose return format, pagination, or any side effects/auth requirements beyond what's implied.

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?

The description is three sentences, each with a clear purpose: defining the tool, pointing to the alternative, and imposing output fidelity rules. No wasted words.

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?

The tool is simple and has an output schema, but with 0% parameter documentation and no parameter info in the description, the agent is left guessing on required fields. The usage guidance and purpose are clear, but the parameter gap makes it incomplete.

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 description coverage is 0%, and the description provides no information about any of the four parameters (api_key, base_url, provider, capability). The description fails to compensate for the missing schema explanations, leaving the agent without clues about how to fill these fields.

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?

The description uses a specific verb+resource ('low-level raw model discovery') and explicitly distinguishes from sibling `ppt_list_route_models` by recommending it for normal OCR flows. This makes the tool's purpose clear and differentiated.

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?

It explicitly directs users to prefer `ppt_list_route_models` in normal OCR flows, implying this tool is for raw/low-level discovery. However, it doesn't state specific scenarios where this tool should be used over the alternative, only the negative condition.

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