transcribe_podcast

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

No annotations, but description fully discloses: uses Whisper + pyannote, returns lightweight metadata (not full transcript), caching by file hash, supported formats. Also explains how to retrieve transcript via get_ui_state.

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?

Well-structured with clear sections (STEP 1, What it does, Returns, Caching, Supported formats, After transcription). Slightly lengthy but every sentence adds value. Could be slightly more concise.

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 no output schema, description adequately describes return values (metadata fields). Covers supported formats, caching, and next steps. Complete for a transcription 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 100% so schema already documents parameters. Description adds context like auto-detection of speakers and language, and that enable_diarization defaults to true. Does not repeat schema but provides useful usage context.

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?

Clear verb+resource: 'Transcribe a podcast video/audio file'. Distinguishes from siblings by calling itself STEP 1 and referencing get_ui_state for reading transcript.

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?

Explicitly says it's the first tool to call, provides workflow: transcribe -> get_ui_state -> analyze -> suggest_clips. Distinguishes when to use get_ui_state instead for transcript reading.

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