get_models

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

With no annotations, the description fully covers behavioral aspects: it lists the resource (models), explains pagination, and details that output is stored and referenced via object ID. It does not mention read-only nature explicitly but implies it. No destructive behavior is indicated.

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 with clear sections: purpose, inclusion scope, usage guidance, parameter definitions, and output handling. It is concise yet comprehensive, with no unnecessary sentences.

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 explains the return value (page of models, object ID, error message) and how to use the object ID. Could mention pagination metadata, but overall 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?

The schema has 0% parameter description coverage, but the description fully explains each parameter: limit, page_number, and connected. It adds meaning beyond the schema by detailing their purpose and effect (e.g., 'works with integration').

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 models with configuration options, distinguishes between predefined and custom models, and specifies its use for discovering model names, providers, and defaults. This differentiates it from other list tools like list_indexes or list_pipelines.

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 explicitly says 'Use this tool to discover which model names...' providing clear usage context. It does not explicitly mention when not to use or alternatives, but the context from sibling tools implies it's the go-to for models.

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