synthesize_long_text

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

With no annotations, the description carries the full burden of behavioral disclosure. It only mentions 'automatic chunking' as a behavior, but does not specify that it converts text to speech, saves an audio file, or what the output is. This is insufficient for a tool with no annotations.

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 sentence that clearly conveys the core purpose and the key feature of automatic chunking. It is concise, front-loaded, and every word contributes to the meaning.

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?

Despite the schema covering parameters, the description lacks essential context such as what the tool produces (an audio file), how output_path is used, and any length constraints. Without annotations, this is incomplete for a tool that is a variant of 'speak'.

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?

The input schema has 100% coverage, with each parameter described (e.g., 'text' as 'The long text to convert to speech'). The description adds only a hint about chunking behavior, which does not significantly go beyond the schema, so the baseline 3 is appropriate.

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 'Synthesize longer text with automatic chunking' specifies a clear action (synthesize), resource (longer text), and scope (automatic chunking). The 'longer text' differentiates it from sibling tools like 'speak', which likely handles shorter inputs.

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 use for longer text, especially with the mention of 'automatic chunking', but it does not explicitly state when to prefer this over 'speak' or other alternatives. There are no exclusion criteria or explicit alternatives mentioned.

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