responses

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description reinforces this with 'Stateless and non-destructive: no conversation state is created, continued, or stored by this tool.' It also discloses the failure mode for unsupported model ids (model_not_supported) and describes the response envelope structure. This adds meaningful behavioral context beyond the 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 information-dense but well-structured: it opens with the core operation, then covers return shape, model-dependent behavior, alternative tools, statefulness, and error behavior. Every sentence contributes necessary information, and the most important scoping detail 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?

There is no output schema, so the description must explain return values, and it does: id, model, status, output array with {index, content}, and usage total token count. It also explains the three output content variants, the error case, and the statelessness guarantee. For a tool with this complexity, the description is complete enough for an agent to invoke it correctly.

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 100%, so the baseline is 3. The description adds value by explaining how each parameter affects behavior: input strings are processed as independent single-turn requests, model selects the output item type, and dimensions controls embedding vector length. This goes beyond the schema's field-level descriptions by tying parameters to output behavior.

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: 'Run the unified Algenta response envelope over one string or a list of independent strings.' It clearly differentiates itself from siblings by describing model-dependent outputs and explicitly contrasting with chat_completions and embeddings. An agent can immediately understand what this tool does and how it differs from nearby alternatives.

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 gives explicit usage guidance: use chat_completions for an ordered multi-role transcript and embeddings when vectors are specifically needed. It also explains that the default model is text.tokenizer and that provider-backed chat models are advertised by list_models. This gives clear context for when to select this tool versus alternatives.

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