list_models

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

Annotations already declare readOnlyHint=true and destructiveHint=false, establishing a safe read operation. The description adds behavioral context by specifying the HTTP method, the status values returned, and the default:true selection rule, going beyond the structured metadata without contradiction.

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 with no filler. The first states purpose and scope, the second offers a practical selection rule. Both are essential and immediately understandable, making the description highly efficient.

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 parameterless listing tool with an output schema and read-only annotations, the description adequately covers what the tool returns and provides a default model heuristic. Nothing critical is missing given the context signals and sibling tool set.

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 tool has zero parameters, so there is no parameter ambiguity. The schema is trivially fully covered, and the description adds no parameter details because none are needed. Baseline 4 for a no-parameter tool is appropriate.

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 explicitly states the tool lists available analysis models, specifies the REST endpoint (GET /v1/models), and enumerates the returned metadata (status, capabilities). This clearly distinguishes it from sibling tools like get_analysis or create_analysis.

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 when to use the tool (when needing to see available models) and provides a concrete guideline to prefer the default:true model unless there's a reason not to. It does not explicitly contrast with alternatives or state exclusions, so it falls short of a 5.

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