swift-ai

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

No annotations are supplied, so the description carries the full burden. It does disclose a real behavioral trait the schema cannot: billing is metered at roughly 1 credit per call. However, it says nothing about whether the call is read-only (only implied by 'List'), paging behavior, or whether the list is filtered by API group permissions.

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, with cost information second. The phrase 'API Group: List Models' is filler that duplicates the title and could be cut without losing 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?

For a parameterless listing tool this covers purpose and cost, which is most of what an agent needs. The notable residual gap is that no output schema exists and the description does not say what a model entry contains (e.g. id, context limits), which an agent would need in order to use the result.

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 there are no parameter semantics to explain and the description cannot be faulted for omitting them. This is the baseline for a parameterless 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?

The description gives a concrete verb and resource: 'Return a list of available models.' An agent can tell this is a discovery/enumeration tool and separate it from the POST generative siblings. It loses a point for the redundant trailing 'on this API Group: List Models', which restates the 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?

There is no guidance on when to call this versus the sibling endpoints, nor any statement that it is the intended way to discover valid model identifiers before calling post_chat_completions or post_messages. The natural use case is implied only by the noun 'models'.

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?

No annotations exist, so the description carries the full disclosure burden. It does add genuinely useful cost context ('Billing per call: Credits: metered (~16.71 avg)'), but it says nothing about authentication, rate limits, model availability, streaming semantics, or error behavior for what is a paid inference call.

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 with the purpose front-loaded and no redundant phrasing. The 'Group:' label is filler for an agent's decision-making, but it costs little.

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 one parameter and no output schema, the definition should at minimum sketch the response shape (choices/message content) or note streaming behavior, since no output schema exists to carry that. As written it is callable but leaves the agent guessing about what comes back and how this differs from sibling generation endpoints.

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?

There is only one parameter and schema description coverage is 100%, so the schema already documents the body and even supplies a full example. The description adds no meaning beyond that (no guidance on which body fields matter, model naming, or streaming), so the high-coverage 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?

States a specific verb and resource: 'Creates a model response for the given chat conversation,' which maps cleanly to the chat-completions concept. It does not, however, distinguish this from close siblings such as post_responses, post_messages, or post_generateContent, and the 'Group: OpenAI Endpoints' line is metadata rather than 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 when-to-use guidance at all. With siblings like post_responses and post_messages in the same family, an agent gets no signal about which endpoint to pick for a given conversation or provider, and no prerequisites or exclusions are stated.

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 supplied, the description carries the full burden. It does add genuine behavioral context beyond the schema: per-call metered credit billing, multimodal input support, and feature coverage (conversation history, system instructions, function calling, structured JSON output, safety settings). It omits other behavior an agent needs, such as authentication requirements, streaming vs. non-streaming, and failure modes.

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?

Purpose is front-loaded in the first sentence, followed by capability detail and then metadata. The 'Group: Google Endpoints. Billing per call: Credits: metered.' tail is terse and functional rather than padded, so nearly every sentence earns its place.

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?

This is a high-complexity generation endpoint with a nested free-form body, no annotations, and no output schema. The description covers capability scope and cost well, but because no output schema exists it should say more about the shape of the response, and it says nothing about auth or streaming behavior.

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 there is a single free-form 'body' parameter documented mainly by an example. The description meaningfully extends that by enumerating what the body can carry — multimodal parts, conversation history, system instructions, function calling, structured JSON output, safety settings, generation parameters — which an agent cannot infer from the example alone.

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 ('Google Gemini API endpoint for generating content using Gemini models') and lists the capabilities it covers. It distinguishes itself from siblings only implicitly, by calling itself the 'official Google Gemini API endpoint' versus the OpenAI-style post_chat_completions/post_responses, but never names those alternatives explicitly.

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 explicit when-to-use or when-not-to-use guidance, and no routing against the many overlapping siblings (post_chat_completions, post_responses, post_embeddings, post_moderations). The only usage-adjacent statement is that multimodal input depends on 'the capabilities of the selected model,' which is a constraint rather than an instruction on when to pick this endpoint.

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?

No annotations are supplied, so the description carries the full behavioral burden. It mentions metered billing, which is useful, but says nothing about side effects, idempotency, rate limits, error behavior, or what state a POST creates; the capability list describes features rather than runtime behavior.

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 lead sentence identifies the endpoint immediately, but the middle is a feature catalogue and the trailing 'Group' and 'Billing per call' fragments read as metadata rather than actionable guidance. Nothing is badly misplaced, yet several clauses do little to help an agent call the tool.

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 annotations, no output schema, and a nested free-form body object, the description should explain the response shape (content blocks, stop reasons) and key request semantics. It instead enumerates features, leaving the agent without the behavioral and return-value context this complexity demands.

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 body parameter already carries an example request, so the baseline is 3. The description does add some semantic mapping by naming capabilities that correspond to body fields (system instructions, tools, thinking, caching), but it gives no field-level syntax or constraints.

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 clear action and resource: generating conversational responses with Claude models, and enumerates concrete capabilities (multimodal input, tool use, extended thinking, prompt caching). It stops short of distinguishing the tool from siblings such as post_chat_completions or post_responses, leaving the agent unsure which generation endpoint to choose.

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 endpoint versus the five sibling generation/embedding endpoints, and no preconditions (auth, model availability) are mentioned. The agent is told what the endpoint supports but not when it is the right choice.

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.

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

No annotations are provided, so the description carries the full behavioral burden. It does add two genuine traits not available in structured fields: that the model can invoke caller-supplied code or built-in tools (web search, file search) and that each call is metered against credits. It omits statefulness, whether responses are stored, streaming behavior, and authentication expectations.

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 capability sentence is front-loaded and useful, and the metered-billing note earns its place. But 'Group: OpenAI Endpoints' is pure catalog boilerplate, and the middle sentence is a long run-on that stacks three separate capabilities without punctuation to aid scanning.

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 whose single parameter is an arbitrary nested JSON body and which has no output schema, the description should say something about the response shape or key body fields; it does neither. An agent knowing only this text would not know how to construct a valid body or what to expect back.

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 there is only one free-form nested 'body' object, so the baseline is 3. The description does add shallow meaning — that the body carries text or image inputs, output-type preference, and tool definitions — but gives no field names, formats, or a pointer to the underlying request shape, leaving the nested object largely opaque.

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') and additionally names the input modalities (text, image) and output modalities (text, JSON), which is more informative than the name alone. However it never distinguishes itself from the very similar sibling post_chat_completions; 'Group: OpenAI Endpoints' is a taxonomy label, not 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 when-to-use guidance: nothing says when this endpoint is preferred over post_chat_completions, post_embeddings, or post_generateContent for the same underlying model. The only implicit cue is 'Provide text or image inputs', which describes a capability rather than a selection condition, and no exclusions or prerequisites are given.

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