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

Annotations already establish read-only, idempotent, non-destructive behavior, so the description doesn't need to restate those. It adds value by disclosing the data window (trailing 14 days), the 'self-maintenance share' concept, and the playing-house flag semantics, which are nontrivial behavioral/output traits. No contradiction with the annotations.

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 compact sentences front-load the high-value metric list and then state the use cases. Every clause contributes information; there is no filler or repetition of annotation fields.

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 read-only panel with no output schema, the description enumerates the main content groups an agent should expect: credits, runs, accept/deny counts, pending cards, and date/flag. It could be more explicit about the response shape or default all-agent behavior, but the metric inventory is enough for a straightforward get tool.

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?

Schema description coverage is 100%, so both parameters are already documented. The description adds only a slight reinforcement of the optional agent_id/whole-team distinction in its opening phrase; no parameter details beyond the schema are needed or provided.

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 names a specific resource ('agent outcome panel') and enumerates the concrete metrics it returns, such as trailing-14-day credits, accepted vs denied outputs, and a playing-house flag. This level of specificity differentiates it from sibling tools like get_agent_performance and get_credit_usage without needing to inspect schemas.

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 explicit trigger scenarios ('whether an agent is worth its spend, what an agent has been doing, or why credits are being used') and identifies the audience (executives/managers reviewing AI team). It does not explicitly name alternatives or state when not to use it, so it falls just 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.