confirm_speaker

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

Discloses side effects (transcript regeneration, future auto-suggestion) and data sensitivity (voice embedding as biometric info). No annotations provided, so description carries full burden; the warning about consent is a strong behavioral cue.

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 concise paragraphs: first states purpose and effects, second adds a critical warning and usage note. No irrelevant details, and key information is front-loaded.

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?

Covers purpose, side effects, and consent requirements. Missing explicit return value (e.g., success confirmation) but given sibling tools like get_transcript exist, the context is sufficient for a transactional tool.

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 0%, but the description partially compensates by explaining the role of consent_confirmed and implicitly tying name, job_id, cluster_label to the confirmation action. However, it does not explain consent_note or the exact format of parameters.

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 clearly states the tool confirms a speaker and learns voiceprint, with explicit effects: transcript regenerated with real name and auto-suggestion in future meetings. It distinguishes from siblings like list_speakers or purge_speaker by focusing on confirmation and learning.

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 guidance on consent: call with consent_confirmed=True after obtaining consent, else leave as anonymous. This helps the agent decide usage. However, it does not compare to alternatives like purge_speaker for removal or list_speakers for viewing.

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