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

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses that no LLM call happens, which manages cost and latency expectations, but it does not describe side effects, output shape, error behavior, or how the brief is processed.

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 concise sentence with no filler, and it front-loads the core action and the important no-LLM distinction. However, it is so short that it misses operational details, though that is more a completeness concern than a conciseness flaw.

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?

The output schema exists and can document return values, but the input semantics are severely under-specified øg and no annotations compensate. The description does not say what belongs in 'brief', what a 'prompt variant' concretely looks like, or when this tool is preferable to its siblings.

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 0% and the single parameter 'brief' has no property descriptions, only additionalProperties: true. The description never mentions 'brief' or explains what structure should be passed, so an agent has almost no guidance for constructing a valid input.

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?

States a specific verb and resource: 'Build focused, detailed, and structured prompt variants.' The phrase 'without calling an LLM' further clarifies its role and separates it from siblings get_schema and validate_brief.

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?

No guidance is provided about when to use this tool versus get_schema or validate_brief. The 'without calling an LLM' clause implies a context but does not state preconditions, exclusions, or alternatives explicitly.

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, the description carries the full burden. It clearly indicates a read operation ('Return') and does not imply any side effects. However, it does not explicitly state that it is non-destructive or requires any special context, though for a simple get this is likely sufficient.

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-formed sentence with no filler. It is front-loaded with the action and object, making it immediately scannable.

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?

The tool is extremely simple (no parameters, no nested objects, has an output schema). The description fully explains what it does, and the output schema itself documents the return structure. Nothing essential is missing.

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 has zero parameters, so the schema covers 100% of parameter semantics vacuously. The description adds no parameter information, but none is needed. Per calibration, a baseline of 4 is appropriate for zero-parameter tools.

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 clear verb 'Return' and a specific resource 'JSON schema for a deterministic prompt brief'. It is immediately distinct from sibling tools like validate_brief and build_prompt_variants, which focus on validation and generation rather than retrieval of a schema.

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?

No guidance is given on when to use this tool versus the siblings. Since the tool has zero parameters and a narrow purpose, an agent might still benefit from a note that this is for retrieving the schema before validating or building variants, but none is provided.

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 disclosing behavior. It only states that it validates and reports a score, but gives no details about the validation criteria, whether the operation is read-only, potential side effects, or the nature of the output beyond a score. This is a significant gap for a tool that processes arbitrary object input.

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, compact sentence that is easy to parse and front-loads the core purpose. There is no extraneous wording, and it is appropriately sized for a simple tool, though it sacrifices depth for brevity.

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 has a nested object parameter and an output schema, the description is too sparse to be complete. It does not explain the semantics of a prompt brief, the meaning of the completeness score, or how the output schema relates to the validation. An agent would need additional context to use it confidently.

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 defines a single parameter 'brief' as an object with additionalProperties true, but provides no description. The tool description does not clarify what a 'prompt brief' is, what structure is expected, or what properties are relevant. With 0% schema description coverage, the description fails to compensate, leaving the agent with no guidance on how to construct valid input.

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 (validate a prompt brief) and the result (report a completeness score). It is specific about the verb and resource, and although it does not explicitly differentiate from sibling tools, the purpose is distinct from get_schema (retrieving schema) and build_prompt_variants (generating variants), so an agent can infer its role.

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 no guidance on when to use this tool versus the siblings or in what workflow context it should be invoked. There is no mention of prerequisites, when validation is appropriate, or what happens if the brief is incomplete. The agent is left to infer usage from the purpose.

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