Parse a transcript

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

No annotations are provided, so the description carries the full burden. It discloses that the tool normalizes transcript format and computes metrics (turn counts, airtime share, etc.), which is useful behavioral context. However, it does not describe the return format, behavior on unknown formats, or any side effects, leaving gaps.

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 two sentences long, front-loaded with the primary action, and every word earns its place. It avoids redundancy and is easy to scan.

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 description covers purpose, usage, and key outputs, and the schema covers all parameters. However, without an output schema or annotations, the description doesn't fully explain what the tool returns or edge-case behavior. For a parse tool of moderate complexity, this is a minor gap, so a 4 is appropriate.

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 description coverage is 100%, so the parameter semantics are already well-documented in the schema. The tool description adds no additional parameter details, but it does mention 'format is uncertain' which indirectly relates to the 'format' override parameter. Baseline 3 is appropriate.

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 specific verbs ('Normalize', 'compute structural metrics') and names the resource (transcript) and specific outputs (turn counts, airtime share, etc.). It clearly distinguishes itself from sibling tools like transcribe_audio and score_conversation by positioning itself as a pre-scoring validation step.

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: 'Run this when a transcript's format is uncertain, to confirm speakers were detected correctly before scoring.' This gives clear context and a specific use case, but it does not mention when not to use it or name alternative tools explicitly.

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