list_metrics

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

No annotations are provided, so the description carries the full behavioral burden, and it discloses almost nothing: no statement that this is a side-effect-free read, that it takes no inputs, or what the returned collection looks like. Only the scope (keys get_financials accepts) hints at behavior.

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 front-loaded sentence with no filler; a discovery tool needs no more preamble than this.

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 and no annotations, the description should have covered the return shape (e.g. a flat list of key names) and confirmed the read-only, argument-free nature. It is adequate for such a simple tool but leaves the response form unspecified.

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 and the schema is an empty object, so there is nothing for the description to disambiguate. Baseline of 4 applies for a no-argument tool.

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?

Clear verb (List) plus resource (metric key) and an explicit tie to the consuming sibling get_financials, which is exactly what distinguishes a discovery tool from the consumer tool. An agent can tell instantly that this enumerates valid metric keys rather than returning financial values.

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?

By naming get_financials the description implies this is the prerequisite discovery step before calling that tool, but it never states the when-to-use condition or that no arguments are needed. Usage is inferable rather than explicit.

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