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

With no annotations, the description carries full disclosure responsibility. It explicitly states the tool does not host, publicly list, or register the API, and that docs-derived results are quarantined pending review. This gives important behavioral expectations beyond the simple action.

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, front-loaded with the core action, then outputs, then privacy caveat. Every sentence adds distinct value without redundancy, achieving excellent conciseness.

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 tool's simplicity (2 params, no output schema), the description covers the return value (API name, tools, artifacts, next steps) and key caveats (quarantine, no hosting). It could mention error handling or output format specifics, but it is sufficiently complete for an agent to invoke correctly.

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 already fully describes both parameters (url and from_docs), so the description adds minimal parameter-level detail. It does clarify that from_docs=true switches to documentation recovery, slightly extending the schema, but overall 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 that the tool takes an OpenAPI URL (or docs URL with from_docs=true) and converts it into usable agent tools. It uses specific terms like 'first-call-correct' and lists concrete outputs, making the purpose unambiguous even without sibling comparisons.

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 clear context on what inputs to submit (OpenAPI spec URL, or docs page with from_docs=true) and notes that docs-based results are quarantined. It lacks explicit 'when not to use' guidance, but the intended use case is well-defined.

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 behavioral disclosure burden. It discloses that the tool is 'Free, instant' and 'lists only what the public index lists,' which clarifies scope and expectations. It does not detail the response format, but for a simple listing tool this is a reasonable level of 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?

Three short sentences, each earning its place: the first states what it does, the second gives the use case, and the third adds limitations and behavioral traits. Information is front-loaded and there is no redundant 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 parameterless discovery tool with no output schema, the description covers what the agent needs: what is listed, why it is useful, when to use it, and what it does not include. Nothing essential is missing for correct invocation.

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 tool takes zero parameters, so there is nothing to document. The description still adds meaningful scope by clarifying that it lists every public API surface on the host, which is all the semantic context needed for a parameterless call.

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 action ('list') and the resource ('every API surface served on this host') and adds the key purpose of providing the MCP URL for reconnection. This is specific enough to distinguish it from the sibling comprehend_api, which logically focuses on understanding an API rather than discovering it.

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 explicitly gives a use case: 'when you landed on the host root and need a specific API.' It does not explicitly name the alternative or say when not to use it, but the context is clear enough for an agent to decide when this discovery tool is appropriate.

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