swift-ai

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

With no annotations, the description carries the full behavioral burden. It does add real value by disclosing cost behavior ('Billing per call: Credits: metered (~0.5 avg)'), which the agent cannot get from the empty schema. However, it says nothing about authentication requirements, rate limits, or what the returned list contains, leaving gaps for a zero-annotation 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?

Two short sentences, front-loaded with the action, which is good. But the middle clause 'on this API Group: List Models' is redundant with the title and consumes space without adding meaning, so it is not fully tight.

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?

For a no-param, no-annotation tool with no output schema, the description covers purpose and cost but omits what the response actually contains (e.g., model IDs/names) and any access prerequisites. Workable for a trivial GET, but not fully complete given nothing else documents the return.

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 tool takes zero parameters, so the schema baseline is 4 and there are no parameter semantics the description needs to compensate for. Nothing in the description misrepresents or overrides the empty object 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?

States a clear verb and resource ('Return a list of available models'), which is trivially distinguishable from the sibling POST endpoints (audio/speech, chat/completions, embeddings, moderations). It never explicitly names or contrasts those siblings, but no confusion is plausible. The trailing fragment 'API Group: List Models' merely restates the name and title rather than adding scope.

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?

No when-to-use, when-not-to-use, or alternative guidance is given; the agent must infer that this enumerates model IDs before calling a completion endpoint. The only contextual statement is a billing note, which is not usage guidance. Adequate only because the operation itself is unambiguous.

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

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

With no annotations, the description carries full behavioral burden. It discloses useful billing context (metered, ~187 credits avg) which is not available elsewhere, but says nothing about auth requirements, rate limits, or the binary/base64 nature of the returned audio.

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?

One short sentence plus two metadata fragments, with the core purpose front-loaded. No wasted prose, though the trailing metadata reads as appended rather than integrated.

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?

For a tool with no annotations, no output schema, and a nested body object, the description is thin. It omits how the audio is returned and any auth or usage prerequisites, leaving meaningful gaps for an agent to call 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% and the body object carries an inline example (model, input, voice). The description's phrase 'input text' loosely maps to one field but adds no syntax or format detail beyond the schema, so the baseline of 3 applies.

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 gives a specific verb and resource: 'Generates audio from the input text.' This clearly identifies a text-to-speech operation. It does not explicitly differentiate from the siblings, though chat/embeddings/moderations are obviously distinct, so it stops short of a 5.

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?

There is no guidance on when to use this tool versus alternatives, no prerequisites, and no mention of required model/voice setup. The agent is left to infer everything about invocation context.

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

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

With no annotations, the description carries the full burden and largely fails: it does not say whether streaming is supported, what authorization is required, how errors surface, or what the response looks like. The one genuine addition is cost transparency ('Billing per call: Credits: metered (~198.19 avg)'), which is real behavioral value for an agent weighing spend, but it is the sole disclosure.

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?

Three short clauses, front-loaded with the core action. The 'Group: Chat' fragment is low-value metadata but costs little; nothing is verbose or padded.

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?

A nested-object chat request with no output schema and no annotations needs the description to explain return values (completion shape, streaming behavior) and constraints. The description supplies none of that, leaving the agent reliant on the schema example alone.

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% and the schema already embeds a concrete example body with model, stream, and messages. The description adds no semantics beyond that, so the baseline 3 for a fully documented single parameter 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?

States a specific verb and resource: 'Creates a model response for the given chat conversation.' This is clearly distinguishable from siblings like post_embeddings or post_moderations, though the description never names those alternatives. The 'Group: Chat' tag adds categorization but no differentiation.

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?

There is no guidance on when to choose this tool over siblings such as post_embeddings or get_models, and no prerequisites or exclusions are stated. 'Group: Chat' is a taxonomy label, not usage instruction.

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

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

With no annotations, the description carries the full behavioral burden. It usefully discloses billing ('Billing per call: Credits: metered (~0 avg)'), which is real added context, but omits any statement about output vector format/dimensions, model-dependent behavior, or error handling.

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?

Three short clauses, front-loaded with the core action. The 'Group: Embeddings' fragment is mild metadata padding but costs almost nothing.

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?

For a model-inference call with no output schema and no annotations, the description should say something about the returned vector (shape, dimensions, usage tokens). It leaves the agent unable to predict the response shape.

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 is 100% and the single body parameter already carries an inline JSON example, so the schema does the heavy lifting. The description adds no additional parameter meaning beyond that, which matches the baseline 3 for high coverage.

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?

States a specific verb and resource: creates an embedding vector from the input text. It is clearly distinct from siblings like get_models, post_audio_speech, and post_moderations, though it never explicitly names or contrasts them.

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?

No when-to-use guidance, prerequisites, or alternatives are given. The only usage-adjacent content is the 'Group: Embeddings' tag, which is categorization metadata rather than routing guidance.

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

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

With no annotations, the description must carry transparency. It mentions billing (Credits metered) and group label, but fails to disclose any behavioral traits like whether changes are made, side effects, rate limits, or the nature of the response. It implies a read-only classification but does not explicitly state it. The lack of safety or side-effect information is a gap.

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 compact, two sentences, with the primary purpose in the first sentence. The billing/group metadata is concise, though it could be considered extraneous. It is front-loaded with the essential action, making it easily scannable.

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?

The description is brief and does not explain the output format, categories of harm, input constraints, or any error conditions. Given that this is a POST endpoint with a nested object and no output schema, the description should provide more context (e.g., what the response contains, max input length). It is incomplete for an agent to use effectively without additional documentation.

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% because the single 'body' parameter includes an example. The description adds no extra meaning beyond the schema—it doesn't explain the structure of 'body' or any constraints. Baseline of 3 is appropriate since the schema manages the parameter documentation adequately.

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 the tool classifies text for potential harm, which is a specific verb+resource. It differentiates from sibling tools (chat, embeddings, models) as a distinct moderation classification. However, 'Create Moderation' is a slightly misleading title since the tool doesn't create anything persistent; the purpose is clarified but not fully precise.

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 no guidance on when to use this tool versus the sibling tools (e.g., chat completions, embeddings). No conditions, prerequisites, or exclusions are given. The agent must infer from the name and purpose that it's for moderation, but no explicit alternatives or scenarios are mentioned.

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