list_models

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

Given no annotations, the description carries the burden. It mentions a prerequisite (connection to MCP server) but does not explicitly state it is a read-only operation or disclose any side effects. For a simple listing, this is adequate but not fully transparent.

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 concise and front-loaded, but includes a promotional phrase ('Access 1,000+ production-ready models') that adds little functional value. Overall it is tight and well-structured.

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 listing tool, the description is complete. It explains what will be returned, mentions the server connection requirement, and no output schema is needed. Nothing essential is missing.

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?

The single parameter 'category' is well-defined in the schema with an enum and description. The main description does not add extra meaning, but the schema already provides full coverage, so the baseline is met and slightly exceeded by the concise enum explanation.

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 identifies the tool as listing available models, with specific output details (model IDs, capabilities, cost estimates). It distinguishes itself from sibling tools like generate_image or get_model_params by focusing on the catalog discovery aspect.

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 implies usage when the user wants to see available models before generation, but it does not explicitly mention alternatives or when not to use it. The purpose is clear enough that an agent would infer the appropriate context.

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