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

There are no annotations to rely on, so the description must carry the burden of behavioral transparency. It only states the return action without disclosing potential side effects, error handling, or whether the tool is read-only. This minimal disclosure leaves ambiguity about edge cases.

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 that conveys the essential information without superfluous content. It is well-structured and directly to the point.

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 simplicity of the tool and the absence of parameters or output schema, the description is largely complete. However, it does not clarify what constitutes 'active' or 'detected', which might be relevant for a user to fully understand the tool's behavior in ambiguous scenarios.

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 accepts no parameters, so there is nothing to explain. The description correctly does not mention any inputs, and the schema confirms zero parameters, making this dimension trivially satisfied.

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 explicitly states that the tool returns the currently active detected Figma backend, which is a clear and specific purpose. It distinguishes this tool from potential alternatives by focusing on the active backend only.

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 usage guidelines are provided. The description does not indicate when to use this tool versus the sibling tools (unified_status, unified_backends, unified_probe_backend), nor does it mention any conditions or prerequisites.

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 burden of disclosing behavior. It says it returns backends and capability summaries, but it doesn't specify what 'known' means (e.g., static list, dynamic discovery), whether it involves network calls, or what the output structure is. It is not misleading, but it is under-transparent.

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 one sentence, front-loaded, and wastes no words. It achieves conciseness without under-specification (though room remains for more detail).

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 no parameters, no output schema, and no annotations, the description gives a high-level purpose but lacks details like the format of 'capability summaries' or whether the result is a list or map. It is adequate for simple discovery but leaves meaningful questions for an agent.

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?

With 0 parameters, there is no additional meaning to provide. The baseline for no parameters is 4, and the description appropriately focuses on the purpose. It exceeds because it clarifies the output includes capability summaries, which is useful.

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 the tool returns known backends and their capabilities, which is clear and specific. It could be improved by distinguishing it from sibling tools like 'unified_status' or 'unified_active_backend', but it stands on its own.

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 implies usage for discovering backends, but does not explicitly state when to use it versus alternatives. Sibling tools exist for status and active backend, but no guidance is given on when to prefer this one.

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?

States the operation is safe and read-only, but no details on what the diagnostic entails, potential results, or side effects. Since no annotations exist, the description carries the full burden and is insufficient.

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?

Extremely concise and to the point. Includes essential information (safe, read-only, backend choices) without redundancy or unnecessary words.

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 absence of an output schema and annotations, the description lacks details about what the diagnostic returns or accomplishes. It's vague about the nature of the probe, leaving the agent uncertain about outcomes.

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 only parameter 'backend' is well-defined via enum and required, providing full schema coverage. The description adds minimal value beyond restating the options, so it meets the baseline.

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?

Clearly states the tool runs a safe read-only diagnostic and specifies the backend options (plumb or custom). Distinguishes from sibling tools like unified_status or unified_backends by focusing on probing a backend.

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 a hint of usage ('diagnostic', 'safe read-only') but lacks explicit guidance on when to choose this tool over alternatives. No mention of conditions or context for probing.

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 implies a read-only health status operation but does not explain what 'normalized' means, whether probing occurs, what counts as active, or any side effects or data-access implications.

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, front-loaded sentence with no filler. It efficiently conveys the core action and scope.

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 there is no output schema, no annotations, and several closely related sibling tools, the description is too thin. It does not explain the meaning of 'normalized health', what 'active backend detection' entails, or how this endpoint differs from unified_active_backend.

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 and schema coverage is 100%, so there is nothing for the description to add about parameters. The baseline of 4 is appropriate here.

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 clear verb ('Return') and specifies a resource ('current normalized health for Plumb and Custom backends plus active backend detection'), which conveys the core purpose. However, it does not explicitly distinguish itself from the sibling tool unified_active_backend, which likely also covers active backend detection.

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 siblings such as unified_backends, unified_active_backend, or unified_probe_backend. There is no context about scenarios, exclusions, or preferred alternatives.

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