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.

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

With no annotations provided, the description carries the burden of behavioral disclosure. It adds important presentation behavior: 'Lead with the overall picture... do NOT list every service. Only call out services that are NOT operational or have unknown status.' This is a useful behavioral trait. However, it does not mention whether the tool performs external network calls, whether it can be slow, or any side effects. Since it's a read-only health check, this is acceptable but not rich enough for a 4.

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 three sentences, each earning its place: purpose, when to use, and how to present results. It is front-loaded with the primary action, and the additional presentation instructions are relevant and concise. It could be slightly tighter by merging some ideas, but overall it is well-structured and not wasteful.

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 tool with 1 optional parameter and no output schema, the description is contextually complete. It explains what the tool does, when to invoke it, and how to present the results. The absence of an explicit return format is not a significant gap since the presentation instructions effectively describe the expected output behavior. Sibling differentiation is not required for completeness.

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 input schema covers 100% of the parameter semantics, including the description 'Filter to a specific category of services. Omit for all.' The tool description itself does not add any parameter-level detail. Per the rubric, with high schema coverage, the baseline of 3 is appropriate.

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 clearly states the tool's purpose: 'Check the health of major internet infrastructure and services.' It uses a specific verb ('check') and resource ('major internet infrastructure and services'), and the scenarios ('unexpected API errors, connection timeouts, degraded responses') further clarify its scope. It distinguishes itself from siblings by addressing broad infrastructure health rather than personal mood or a specific service status.

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: 'Call this when you encounter unexpected API errors, connection timeouts, or degraded responses from cloud services.' It also gives example user queries ('is AWS down?'). However, it does not mention any when-not-to-use scenarios or explicitly name alternative sibling tools, so it misses the full 5.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context by listing data sources (official status, components, incidents, Downdetector, StatusGator) and instructs how to present results. However, it does not disclose potential latency, failure modes, or limits on service name validity, leaving some behavioral aspects uncovered.

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 composed of three focused sentences: purpose, usage context, and result presentation. Every sentence adds value, and the structure is front-loaded with the core purpose. There is no fluff or repetition.

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 compensates by explaining what the tool returns (data from multiple sources) and how to synthesize it. It covers the essential aspects for a single-parameter investigation tool. Minor gaps include error handling when a service is not found, but overall it is sufficiently 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%, and the parameter 'service' is already described clearly with examples. The tool description adds a few extra examples ('aws s3') and states 'Accepts any service name,' but that is largely redundant with the schema. The baseline of 3 applies because the schema does the heavy lifting.

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 clearly states the tool's function: 'Investigate the status of a specific service in detail.' It uses a specific verb ('investigate') and identifies the resource ('a specific service'), and it distinguishes itself from siblings like 'is_the_internet_on_fire' by focusing on individual services rather than global status.

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 usage context: 'Call this when you're seeing problems with a particular service' followed by concrete examples (deploys failing, API returning 500s). It gives clear when-to-use guidance but does not explicitly mention when not to use it or name alternative tools, so it misses the 'when-not' component.

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