speech_to_text

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

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It successfully describes several key behaviors: the API call to Whissle with cost implications, the file saving behavior with default directory logic, and the return format (TextContent with transcription and file path). It doesn't mention error handling, rate limits, or authentication requirements, but covers the essential operational behavior well.

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 well-structured and appropriately sized. It begins with the core purpose, then provides the critical cost warning, followed by organized parameter documentation and return information. Every sentence earns its place - the warning is essential, and the parameter explanations are necessary given the lack of schema descriptions.

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 tool's complexity (5 parameters, API calls with costs, file operations) and the absence of annotations, the description provides comprehensive coverage. It explains the purpose, usage constraints, all parameters, and the return format. With an output schema present, it doesn't need to detail return values further. The description is complete enough for an agent to understand when and how to use this 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?

With 0% schema description coverage, the description fully compensates by providing detailed parameter documentation. It explains all 5 parameters with clear semantics: what 'audio_file_path' is for, default values for 'model_name' and 'output_directory', what 'timestamps' controls, and the purpose of both 'boosted_lm_words' and 'boosted_lm_score'. The description adds significant value beyond the bare schema.

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's purpose: 'Convert speech to text with a given model and save the output text file to a given directory.' This specifies both the core function (speech-to-text conversion) and a secondary action (file saving). It distinguishes from siblings like 'diarize_speech' (which focuses on speaker identification) and 'list_asr_models' (which lists available models).

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 provides clear usage context with the cost warning: '⚠️ COST WARNING: This tool makes an API call to Whissle which may incur costs. Only use when explicitly requested by the user.' This gives important guidance about when to use (only when user explicitly requests) and implies when not to use (for casual exploration due to costs). However, it doesn't explicitly compare to alternatives like 'diarize_speech' for different speech processing needs.

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