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

readOnlyHint=true already signals a safe read, and the description adds meaningful context beyond annotations: querying does not start a research task, the response includes the default item and credit multipliers, and the model_id to use later is the one actually returned. It does not cover caching or rate limits, but for a static catalog that is minor.

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 gating condition is front-loaded in the first clause, followed by what is returned and the downstream id rule. Three dense sentences with no filler, and each one carries a distinct constraint.

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?

With no output schema, the description still explains what a call returns (enabled models, default, multipliers) and what it does not do (start a task). For a zero-parameter read tool, an agent has everything needed to decide whether to call it and how to use the result.

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 takes no parameters, so the 4 baseline applies. The mention of using the returned model_id is directed at downstream usage rather than this tool's own inputs, so it adds only marginal semantic value here.

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 states a specific verb (read/list) and resource (currently enabled research models), and explicitly lists what is returned: the enabled models, the default item, and credit multipliers. It is clearly distinguishable from write-oriented siblings such as start_research_task and prepare_research_task.

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 gives an explicit trigger (only when the user actively queries or explicitly specifies a model) and explicit non-triggers (general research tasks should use the server default; do not proactively query, recommend models, or ask for confirmation). The alternative path — using the server default — is named directly.

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