how_am_i_feeling

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

With no annotations provided, the description carries the full burden and does so admirably. It discloses what the output contains (component-level detail, active incidents, local_system, community_vibes), how to interpret degradation, and explicit instructions to avoid repeating raw scraped text. This is rich, actionable behavioral context.

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 longer than average but every sentence earns its place: purpose, triggers, exclusions, proactive use, and output interpretation. It is front-loaded with the core purpose and structured logically, making it easy for an agent to extract actionable rules.

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 compensates by explaining return values in detail: component-level detail, active incidents, local_system metrics, and community_vibes handling. It gives complete guidance for synthesizing results into a self-aware response, covering interpretation and caveats. For a tool with this complexity, the description is fully 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?

Schema coverage is 100% with one optional 'model' parameter described inline ('The model name to check...'). The description adds no additional parameter meaning beyond the schema, so the baseline of 3 is appropriate. The auto-detection behavior is already stated in the schema description.

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 opens with a specific verb+resource: 'Check your own model/provider health, status, and community-reported quality.' It clearly distinguishes itself from sibling tools by stating the decision rule: prefer this tool when asking about the assistant/system, not an external service. Trigger examples further disambiguate intent.

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 provides explicit when-to-use guidance with trigger examples, a decision rule for when to skip ('pure social small talk'), and proactive use cases if the assistant notices its own performance issues. Alternatives are implied by contrasting with external-service queries, and exclusions are clearly stated.

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