lookup-disclose-io

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

Annotations already declare readOnlyHint=true and openWorldHint=false. The description adds 'No network calls — instant response,' which is valuable behavioral context. However, it does not describe the output format or any side effects, leaving some uncertainty for the agent.

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 very concise with three short sentences that front-load the purpose. No unnecessary words or repetition; every sentence adds value.

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?

Given the simple tool (one param, no output schema, annotations present), the description covers purpose, behavior, and usage context. However, it lacks explicit mention of the return value format or classification categories, which would help an agent interpret the output.

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 required string parameter described as 'The input to classify.' The description adds meaning by listing example input types (domain, IP, package, repository), which clarifies what the parameter accepts beyond the generic 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 clearly states the verb 'classify' and the resource 'input as a domain, IP, package, repository, etc.', providing specific examples. It distinguishes from the sibling tool 'lookup_security_contact' by emphasizing classification before a full lookup.

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 gives explicit context for use: 'Useful for understanding what an asset is before performing a full lookup.' It also notes 'No network calls — instant response' to set expectations. However, it does not explicitly state when not to use or list alternatives, though the sibling is different enough.

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?

Annotations provide readOnlyHint and openWorldHint, and the description adds context about the informational nature and result types (e.g., sorted by confidence). No contradictions; the description enhances understanding beyond 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 sentences max. The first sentence states purpose, followed by input and output details. Every sentence contributes meaning without redundancy or unnecessary length.

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?

Despite no output schema, the description clearly lists return types (bug bounty programs, security.txt, etc.) and notes sorting by confidence. It also sets context as informational. Sufficient for an AI agent to understand the tool's behavior.

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 descriptions. The description adds value by providing concrete examples for the 'asset' parameter and explaining the 'asset_type' parameter's role in forcing type. This enriches the schema information.

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: 'Find security reporting channels for responsible vulnerability disclosure.' It lists a wide array of input types and output categories, distinguishing it from the sibling 'classify_asset' which likely serves a different purpose.

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 explicitly states when to use the tool (to find security contacts) and sets expectations with 'informational only — not legal advice.' It does not explicitly state when not to use it or compare directly to siblings, but the context is clear enough for appropriate selection.

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