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

Beyond annotations, the description discloses that the call is paid from the Gisoo credit of the key, comparable to POST /audio/speech, and that the USD cost is included in the text. It also explains the output shape (MCP audio block, base64, MP3 by default), which is important behavioral context for a paid generation tool.

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 English portion is front-loaded and efficient, covering limits, output type, model default, and cost. The Persian sentence largely repeats the conversion and credit-deduction points, adding slight redundancy but remaining short.

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?

With no output schema, the description usefully explains that audio is returned as an MCP audio block and notes the default format. It also covers payment and cost transparency. Minor gaps remain around voice/instructions behavior, but the core invocation context is complete.

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 80%, so the schema already documents most parameters. The description reinforces the 2000-character limit, default model, and MP3 default, and adds that input may be Persian or English, but it does not add much beyond what the schema already conveys.

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 states a specific verb and resource: converting up to 2000 characters of Persian or English text into spoken audio. It also specifies the return format (MCP audio block, base64, MP3 by default) and default model, making the tool's function immediately clear and distinguishable from sibling generation tools.

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?

It gives clear usage context: text-to-speech for up to 2000 characters, with Persian/English support and default model behavior. It does not explicitly compare against alternatives like generate_music or ask_model, but the generation context is clear enough for an agent to select it.

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