Score Conversation

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

With no annotations provided, the description carries the disclosure burden. It clearly states the tool analyzes and produces derived metrics but does not reveal whether there are side effects (e.g., persistence, write-back to CRM), whether the raw transcript is retained, or what happens on unprocessable input. For an analysis tool this is moderate transparency, but no side-effect or input-limit behavior is mentioned.

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 dense sentence that front-loads the action and enumerates outputs efficiently. It is complete without being verbose, though it could arguably be split for readability. No wasted words, earns its length.

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 has 4 required parameters, no output schema, and no annotations, so the description is the sole guide. It explains the broad outputs well, but with no output schema it does not communicate the return format or structure of the extracted intelligence, and the sibling get_lead_score suggests a related scoring ecosystem whose differentiation is unresolved. Adequate but leaves room for more.

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 75%, so parameters are largely self-documenting (raw_outcome gets an example, call_duration gets units, source_system gets examples). The description adds that analysis is structured 'business intelligence' but does not clarify how call_duration or source_system influence the scoring, which would add value beyond the schema. One parameter has no description in schema, though the description partially covers the overall intent.

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 ('Analyze') with a clear resource ('a call transcript') and enumerates the exact structured outputs it produces: intent, sentiment, urgency, objections, outcome, topics, flags, revenue signal, churn risk, and a summary. This clearly distinguishes it from sibling tools like get_lead_score (score retrieval) and diagnose_workflow_error (error diagnostics).

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 call transcripts yielding business intelligence but provides no explicit when-to-use vs alternatives guidance. It doesn't exclude scenarios or direct the agent toward when the score_conversation tool is preferable over get_lead_score, which could be relevant given 'revenue signal' and 'churn risk' outputs overlap thematically.

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