score

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

With no annotations provided, the description carries the behavioral burden. It discloses what metrics are computed and the conditional behavior around reference text, but it does not mention whether the tool mutates anything, what it returns, or how it handles missing reference text beyond omitting accuracy. For a pure computation tool this is adequate but not rich.

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 with no fluff. The metrics are listed up front, and the conditional behavior is stated second. Every clause earns its place.

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 two-parameter computation tool with fully documented schema, the description is nearly complete. The only notable gap is that it does not describe the output shape or explicitly differentiate from detect_hallucinations, but this is minor given the tool's simplicity.

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 description coverage is 100%, so the schema already documents transcript_json and reference_text. The description adds the metric names and the role of reference_text in computing accuracy, but it does not add material detail about parameter formats or constraints beyond the schema.

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 names a specific verb ('Compute'), a clear resource (transcript quality), and enumerates the exact metrics: WER, CER, hallucination rate, words per minute. This distinguishes it from siblings like transcribe and estimate_cost, and the conditional accuracy clause adds further precision.

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 usage context is implied: use this tool to score transcript quality metrics. However, it does not explicitly state when to prefer this over detect_hallucinations, which also overlaps with 'hallucination rate', nor does it mention any when-not-to-use conditions.

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