Score a dialogue transcript

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 thoroughly explains the return format (0-4 score per speaker per indicator, quoted evidence with turn numbers, confidence value, airtime metrics) and even demonstrates a specific use case for arc_segments. While it doesn't explicitly state read-only behavior, the described behavior is clear and non-destructive.

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 concise and well-structured: two sentences that front-load the core purpose, then detail the return format and end with a practical usage tip. Every sentence contributes value with no redundancy or 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?

Given the lack of an output schema, the description does well to explain the return structure and available indicators. It also provide a usage example for arc_segments. However, it does not clarify how this tool compares to compare_conversations or analyze_cohort, and some parameters (meta, speaker_aliases) are left to the schema, which is acceptable since the schema is rich.

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 100%, so the baseline is 3. The description adds minor value by highlighting the arc_segments tip ('Set arc_segments to 3 to see how the conversation changed from opening to close'), but does not substantially enrich the parameter meanings beyond what the schema already provides.

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 specific verb ('Score') and a specific resource ('a conversation for bridging quality'), and clearly distinguishes itself from siblings like parse_transcript or transcribe_audio by focusing on scoring. It lists the exact indicators and explains the output, making the tool's purpose unmistakable.

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 when to use the tool (when you need to score a conversation for bridging quality) but provides no explicit exclusions or comparisons to sibling tools like compare_conversations or analyze_cohort. The only usage tip is 'Set arc_segments to 3', which is helpful but not a full usage guideline.

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