List Hedra Models

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds useful behavioral context: filtering by modality, the return format (a 'models' array with id/name/modality), and that omitting the parameter lists all. This goes beyond annotations, though it doesn't mention pagination or rate limits—minor gap for a list tool.

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 well-structured into Args, Returns, and Examples sections. It front-loads the core purpose in the first sentence and every subsequent line adds practical value—examples, alternative references, and conditional usage. No fluff.

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?

For a simple list tool with one optional parameter and no output schema, the description covers everything: purpose, use cases, parameter semantics, return shape, and alternatives. It is fully self-sufficient for an agent to select and invoke the tool correctly.

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 coverage is 100%, so baseline is 3. The description adds example usage ('What video models can I use?' -> modality="VIDEO") and clarifies the default behavior ('Omit to list all'), which the schema does not explicitly state. This adds real value beyond the structured field.

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 clearly states the tool lists all AI models across image, video, and audio/avatar categories, naming specific partners. It distinguishes itself from siblings by explicitly mentioning alternatives like hedra_get_model_input_schema and hedra_submit_job, and provides a 'Don't use when' case.

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?

The description gives explicit guidance: use this first to discover a model's public id before calling other tools, includes concrete 'Use when' examples (e.g., 'What video models can I use?') and a clear 'Don't use when' with the alternative tool named. This is textbook usage differentiation.

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