swift-ai

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

The description mentions 'Billing per call: Credits: metered', disclosing a cost behavior, which adds context beyond the schema. However, no annotations are provided, and the description does not disclose other behavioral aspects such as whether the list is static or dynamic, pagination, or rate limits. It provides minimal behavioral transparency, but the billing note is a useful addition.

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 concise, consisting of one sentence plus a billing note. It is front-loaded with the main purpose at the beginning. The billing note adds value and is not redundant, so the structure is efficient with minimal waste.

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 has no parameters, no output schema, and no annotations, the description is short but covers the primary purpose and a key behavioral note (billing). However, it lacks information about the return format (e.g., list of IDs) and potential limitations, which could be improved for a complete understanding. The low complexity might justify a 3, as it is adequate but has minor gaps.

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 has zero parameters, so the schema is fully covered at 100% and there is no parameter detail to add. The description correctly indicates that the tool requires no input, making parameter semantics moot; a baseline of 4 is appropriate for a no-parameter tool.

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 'Return a list of available models on this API Group', using a clear verb and resource. It distinguishes from sibling tools that perform actions like chat, embeddings, and moderations, so the purpose is clear and well-differentiated.

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 alternatives. It does not mention typical use cases such as retrieving model IDs for subsequent API calls, nor does it exclude contexts. The sibling tools imply different actions, but no explicit guidance is given.

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 of behavioral disclosure. It only adds 'Group: Chat' and billing metadata; it does not describe response behavior, potential side effects, authentication needs, rate limits, or streaming implications. 'Creates a model response' is a minimal functional statement, not transparency.

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 and front-loaded with the core purpose. The addition of 'Group: Chat' and billing info is brief, though not strictly necessary, making it slightly less lean than ideal but still efficient.

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 tool has no output schema, yet the description does not explain what the model response will look like, what fields in 'body' are required, or how to handle streaming. The embedded example in the schema provides partial guidance, but the overall description is incomplete for a tool of this complexity.

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% for the single 'body' parameter, and the schema includes an example JSON payload. The tool description itself adds no parameter-level meaning, 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?

Description clearly states it 'Creates a model response for the given chat conversation,' using a specific verb and resource. This distinguishes it from siblings like get_models, post_embeddings, and post_moderations, which address different endpoints and purposes.

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 phrase 'for the given chat conversation' provides clear context that this tool is for chat-completion requests. However, it does not explicitly mention when not to use it or point to alternatives, stopping short of a perfect score.

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 of behavioral disclosure. It mentions the core effect (creating an embedding vector) and billing ('Credits: metered'), but does not disclose permissions, reversibility, or impact on resources. This is minimal transparency for a mutation 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 description is extremely concise: three short sentences that convey the purpose, group, and billing. No redundant information, and it is front-loaded with the primary action.

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 tool has moderate complexity (a nested object parameter) and no output schema. The description does not explain the return format or how to interpret the embedding vector, nor does it provide usage context beyond the basic action. This is inadequate for complete guidance.

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% (the 'body' property has a description with an example). The tool description adds no further parameter semantics beyond what the schema provides. Baseline 3 is appropriate since the schema handles the parameter documentation.

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 function: 'Creates an embedding vector representing the input text.' This is a specific verb+resource structure that distinguishes it from siblings like post_chat_completions and post_moderations.

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 offers no guidance on when to use this tool versus alternatives. It mentions 'Group: Embeddings' but does not explicitly state when embeddings are appropriate or when to avoid it. No alternatives or exclusions are provided.

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.