phone-lookup

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

No annotations are provided, so the description carries the full burden. It does disclose the type of output (community verdict, rating, spam level, operator, etc.), which informs the agent what to expect. However, it does not explicitly state that the operation is read-only, mention any rate limits, or describe what happens if the number is not found. 'Look up' implies non-destructive behavior, but additional context would improve transparency.

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 a single, well-structured sentence that front-loads the action and resource, then efficiently lists the key output attributes. Every word adds value with no redundancy.

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 simple lookup tool with one parameter and no output schema, the description covers the essential context: what the tool does, the target resource, and the nature of the returned data. It does not explain potential failure modes, but given the simplicity and the sibling context, 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?

The schema already provides 100% coverage of the single parameter 'number' with a clear description. The tool description adds no additional parameter semantics beyond what the schema already states, so the baseline score 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 uses a specific verb ('Look up') and clearly identifies the resource ('a phone number on ktozvonil.net'). It also lists the specific data returned (community verdict, rating, spam level, etc.), which distinguishes it from the sibling tool 'post_comment' that clearly performs a different action.

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 makes it clear that this tool is for retrieving information about a phone number, while the sibling 'post_comment' is for adding content. There are no explicit exclusions or alternative recommendations, but the context is sufficient for an agent to choose correctly between lookup and posting.

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?

No annotations are provided, so the description carries the full burden. It discloses the OAuth token requirement, the AI-agent-generated labeling, the separate display section, and the fact that it does not affect community rating. This is strong behavioral context, though it omits details like return value or error 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?

The description is two sentences, front-loaded with the primary action ('Post a phone-number review'). It efficiently includes auth requirements and behavioral notes without redundancy or filler.

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 simple write tool with no output schema, the description is adequately complete. It covers purpose, auth, and key side effects. The missing return value information is a minor gap, but the schema and context provide sufficient guidance.

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 parameters with descriptions for text, number, and rating. The description adds no parameter-specific meaning beyond the schema, only global context about the review being AI-generated and non-influencing. Baseline 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 states a specific verb and resource: 'Post a phone-number review' on behalf of the authenticated user. It clearly distinguishes from the sibling lookup_phone by emphasizing it is a write operation, and clarifies the review's special labeling and non-impact on rating.

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?

Provides clear context for when to use: when posting a review as an authenticated user with write scope. It does not explicitly name alternatives or when-not-to-use scenarios, but the contrast with sibling lookup_phone is implicit, and the auth requirement sets clear conditions.

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