rug_pull_detector__list_baselines

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

No annotations are provided, so the description carries the burden. It implies a read-only enumeration of stored baselines, which is the right safety signal for a zero-parameter tool, but it doesn't say what happens when no baselines exist (empty list vs error) or what the entries contain.

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?

A single sentence with no waste, front-loaded with the verb and scope. Appropriate size for a zero-parameter enumeration tool.

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?

There is no output schema, so the description is the only signal about the return value, yet it doesn't state what a baseline entry looks like (server identity, when captured) or whether the result is paginated. For a trivial list call this is adequate but not 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 tool takes zero parameters, so the baseline of 4 applies. There is nothing for the description to disambiguate at the argument level.

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?

States a specific verb (list) and resource (stored baselines) with a scope qualifier ('every server this scanner has a stored baseline for'). An agent can distinguish it from rug_pull_detector__baseline_server and check_server by the 'stored baseline' phrasing, though it does not name them explicitly.

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?

No guidance on when to call this versus baseline_server (create/update a baseline) or check_server (detect rug pulls). The natural 'discover what's covered before checking' workflow is left entirely to inference.

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