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

Annotations already declare destructiveHint=true, idempotentHint=true and readOnlyHint=false, so the safety profile is covered structurally. The description adds real value beyond that by spelling out the exact task states in which the operation is a no-op rather than a cancellation. It stops short of saying what the caller receives or whether a failed cancel raises an error, which keeps it at 4.

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 sentences, zero filler, and the supported scope is front-loaded before the negative cases. Every clause (supported models, waiting-to-run condition, non-cancellable states) carries distinct information.

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 one-parameter mutation with no output schema, the description covers the essential decision inputs: which tasks are eligible and which are not. It is slightly incomplete on the outcome of a rejected cancellation (error vs. silent no-op), but annotations already flag the destructive/idempotent nature.

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 a single parameter with 100% schema description coverage, so the schema already explains that `id` comes from `id`/`task_id` or a `poll_url`. The description adds no syntax, format, or value constraints for `id` beyond the schema, so the baseline 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 opens with a precise verb+resource ("Cancels a queued asynchronous task") and immediately bounds the scope to queued Seedance video tasks, distinguishing it from status/inspection siblings like get_task_status. An agent can tell exactly what this tool does and on what population without opening the schema.

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?

It gives an explicit precondition ("when cancellation is supported for the selected task") plus a positive list of supported models and an explicit negative list of states that cannot be cancelled (processing, completed, failed, expired, unsupported). This is when/when-not guidance with nothing left to inference.

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?

Annotations already indicate the tool is read-only, idempotent, and non-destructive. The description adds no further behavioral context (e.g., output format, rate limits, or open-world implications). With high annotation coverage, the description's lack of extra detail is acceptable, earning a baseline 3.

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, efficient sentence with no wasted words. It directly conveys the tool's purpose without extraneous information.

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's simplicity, two parameters, and comprehensive annotations, the description covers the essential functionality of comparing model details and pricing. It does not describe the return structure, but the absence of an output schema makes this less critical. Slightly incomplete due to the missing parameter explanation, but overall adequate.

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 0%, so the description must compensate. It clarifies that 'models' is a list of model IDs and that the tool compares details and pricing. However, it fails to explain 'include_raw', leaving one of two parameters undocumented. This partial coverage justifies a score of 2.

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 uses a specific verb 'Compare' and identifies the resource 'public TokenLab model details and pricing' for 'several model IDs'. This clearly distinguishes the tool from siblings such as 'get_model' (single) and 'list_models' (all).

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 usage for comparing multiple models but does not explicitly state when to use it versus alternatives or provide when-not-to-use guidance. A minimal viable score is appropriate as the context is implied but not articulated.

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?

Annotations already declare the non-read-only, non-idempotent, open-world, non-destructive profile, so the safety picture is covered. The description adds genuinely useful context that it is an asynchronous task returning a pollable task ID, but says nothing about cost, quota, or how the per-request Delivery policy affects execution.

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 tight sentences with the core action front-loaded and no filler. Nothing wasteful, though the brevity leaves room for the omitted usage and parameter context, so it is efficient rather than exemplary.

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 10-parameter, 60%-coverage, no-output-schema tool, the description is too thin: it omits how to choose between the prompt, image, and image_url input modes, what the returned task ID is used for beyond 'polling', and any input validation constraints. The async hint is the only operational detail provided.

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?

Ten parameters with only 60% schema description coverage (format, quality, image_url, model default, and delivery policy are undocumented), and the description adds no parameter meaning at all. It neither explains the prompt-vs-image generation paths nor the enum trade-offs, leaving a real coverage gap uncompensated.

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 3D model generation task') and names the provider Tripo3D, which clearly separates it from image/video/audio siblings. It stops short of explicitly distinguishing it from the other create_* generation tools, but the resource is unambiguous.

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 async framing ('Returns a task ID for polling') implies an indirect workflow, but the tool never names the follow-up tool (get_task_status) or when to prefer prompt-based vs image-based generation. Usage is only inferable, not 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?

Annotations already declare the mutation profile (readOnlyHint=false, idempotentHint=false, openWorldHint=true), so the bar is lower. The description earns credit by adding auth requirements ('x-api-key' or 'Authorization: Bearer') and capability notes (vision, streaming) that the annotations do not convey.

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 tight sentences with the purpose front-loaded, then capabilities, then auth. No filler, no repetition of the name.

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?

No output schema exists, yet the description says nothing about return shape, streaming response behavior, or the non-standard X-TokenLab-Delivery-Policy parameter. For a 15-parameter generation tool this leaves real gaps an agent would want covered.

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?

With 15 parameters at 73% schema description coverage, the schema does most of the documentation work. The description adds nothing parameter-specific beyond noting model/vision support, so the baseline 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 using Anthropic's native request format'), which implicitly separates it from format-specific siblings like create_gemini_content or create_chat_completion. However, it never names an alternative, so the agent must infer the routing rather than read it.

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?

It notes Claude model support with vision and streaming, and specifies the auth headers to use, which implies the usage context. But it gives no explicit when-to-use/when-not guidance or comparison to the other create_* completion siblings, leaving selection to inference.

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?

Beyond the annotations (readOnlyHint=false, idempotentHint=false, openWorldHint=true), the description discloses genuine operational behavior: the MCP boundary auto-corrects recognized PNG/JPEG/WebP/GIF payloads mislabeled as application/octet-stream, rejects unrecognized generic binary images, and flags that this happens before a billable request is sent. The billing implication is valuable context the annotations do not carry.

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?

Front-loads the core purpose in the first sentence, then adds the image-payload constraint with no filler. It is appropriately short, though it devotes two of three sentences to a single niche case rather than broader behavior.

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 29-parameter tool with nested objects and no output schema, the description covers only the inline-image validation edge case and omits behavioral context around streaming, tool calling, reasoning effort, and translation options. The 100% schema coverage carries the parameter burden, but the description is thin 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 coverage is 100%, so the schema already documents the 29 parameters and the baseline would be 3. The description adds real semantic value on the messages.content field by requiring an accurate image MIME type for inline image_url data URLs, which the schema does not spell out. It only addresses one corner of a very large parameter surface, so it is not a 5.

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 ("Create an OpenAI-compatible chat completion"), and the word "OpenAI-compatible" implicitly distinguishes it from siblings like create_anthropic_message and create_gemini_content. It stops short of explicitly naming alternatives to route between, so it is clear but not fully sibling-differentiating.

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 no guidance on when to choose this tool over create_anthropic_message, create_gemini_content, or create_response, nor any prerequisites or exclusions. The only usage-adjacent content is a validation constraint on image payloads, which does not tell an agent when to pick this call.

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?

Annotations already declare readOnlyHint=false, idempotentHint=false, and destructiveHint=false, so the mutation profile is covered structurally. The description adds nothing beyond that: no rate-limit notes, model applicability, batch behavior, or cost/latency context, despite the tool being an externally-hitting (openWorld) generation 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?

A single front-loaded sentence with zero redundancy or filler. It is efficient, though arguably under-specified for a 6-parameter 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?

There is no output schema, so the description is the only place to explain the return value — yet it says nothing about the shape of the returned vectors, the effect of dimensions or encoding_format on output, or batching of array inputs. For a generation tool with six parameters and no output schema, this leaves meaningful 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?

Schema coverage is 100%, so every parameter (input, model, dimensions, encoding_format, user, delivery policy) is already documented in the schema. The description adds no syntax, format, or batching detail beyond it, so the baseline 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 an embedding vector") with the input scope ("representing the input text"), so the core action is unambiguous. It does not, however, distinguish itself from the sibling create_multimodal_embedding, leaving the agent to infer the difference.

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, no exclusions, and no mention of the obvious alternative create_multimodal_embedding. The agent gets no criteria for selecting this tool over its sibling.

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?

Annotations already declare non-readonly, open-world, non-idempotent behavior, and the description adds real value beyond that: the dual naming convention, best-effort handling of unknown fields, and three concrete authentication mechanisms. It does not mention rate limits, quotas, or error behavior, so it falls short of a 5.

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?

Four front-loaded sentences, each carrying distinct information (contract, availability precondition, naming/unknown-field behavior, auth). The bolded Authentication block is well-placed; no filler, though the proto-naming sentence could be tightened.

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 14-parameter, deeply nested tool with no output schema, the description covers the contract, prerequisites, naming duality, and auth. It omits what the response looks like and any token/candidate limits, but given the annotations and rich schema this is close to complete.

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?

With ~79% schema coverage the schema carries most of the load, but the description explains the otherwise-puzzling duplication of parameters by stating that both lowerCamelCase and proto snake_case field names are preserved, which directly disambiguates the paired fields. That is meaningful semantics beyond the 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 specific verb+resource ('Generates content') and pins the exact request contract ('Gemini GenerateContent request shape'), which cleanly separates it from the many sibling generators (create_anthropic_message, create_chat_completion, create_response). An agent can route to it without opening the schema.

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?

It gives a prerequisite ('the model details must advertise Gemini requests and the model must be available') but never states when to pick this over create_chat_completion or create_anthropic_message. Usage is implied by the Gemini-specific framing rather than spelled out.

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?

Annotations already declare the safety profile (readOnly=false, destructive=false, idempotent=false, openWorld=true), so the description's only job is added context, and it adds none. It omits model-dependent behavior, that output is non-deterministic per call, and that the 26 parameters are largely model-specific and may be ignored or rejected depending on the selected model.

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?

It is a single front-loaded sentence with zero filler, which is efficient. It is arguably under-sized for a 26-parameter tool, but that gap is captured under contextual completeness rather than 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?

For a tool with 26 parameters, three operation families (text-to-image, image-to-image, image-edit), seven enums, and model-specific options, one sentence is far from sufficient. It omits required-vs-optional expectations beyond model/prompt, the need to consult the recommended-models endpoint, and how this differs from sibling generation/editing tools; with no output schema the description should also hint at the url/b64_json 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 description coverage is 100%, so every parameter including model, prompt, operation, and the model-specific fields is already documented in the schema; the baseline of 3 applies. The description adds no additional meaning about how model selection gates the other 24 parameters.

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 ('Creates an image') plus the required input ('given a prompt'), which is clear enough to know the general operation. However, it provides no differentiation from close siblings such as edit_image, edit_image_file, and create_image_file, leaving the agent to infer routing.

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, no exclusions, and no mention of alternatives. Given the crowded sibling set (edit_image, create_image_file, edit_image_file), the agent gets no help deciding between generation and editing or between this and file-based variants.

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?

Annotations already declare readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false, so the safety profile is covered structurally. The description adds nothing beyond that: no mention of cost/billing for generations, latency, async behavior (the 'async' param hints at long-running jobs), rate limits, or moderation rejection handling. With annotations present the bar is lower, but zero added context merits a 2.

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 single sentence is front-loaded and free of padding, which is structurally clean. However, the brevity here reflects under-specification rather than disciplined conciseness, so it earns only a middling score.

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 26-parameter generative tool spanning three distinct operations, with no output schema and no annotation detail about returns, one sentence is wholly inadequate. Nothing tells the agent how the returned image is delivered (url vs b64_json), how async jobs are polled via get_task_status, or how reference images are supplied.

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?

26 parameters with only 27% schema description coverage, and the description explains none of them. Critical unlabeled parameters include operation (text-to-image / image-to-image / image-edit), n, seed, size, aspect_ratio, resolution, quality, style, mask_url, background, response_format, and async. An agent cannot infer correct usage from a single sentence when three-quarters of the parameters carry no documentation anywhere.

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 'Creates an image given a prompt' is essentially the title restated (title: 'Create image') with the trivial addition of the prompt input. It never explains what the '_file' variant is or how it differs from the sibling tools create_image, edit_image, or edit_image_file, which is the one thing an agent most needs. A near-tautology of the name.

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, no mention of prerequisites, and no routing to alternatives. With siblings named create_image and edit_image_file present, an agent has no basis from the description for choosing this tool. Nothing is misleading, but nothing is offered either.

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?

Annotations already declare the safety profile (readOnlyHint=false, destructiveHint=false, idempotentHint=false). The description adds genuinely useful context beyond them: text input is generally available while image input may require feature enablement, a real prerequisite. It says nothing about cost, rate limits, or return format, so with annotations covering safety a 3 fits.

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 sentences, zero filler, with the core action stated first and the availability caveat second. Nothing redundant, though it is very terse given the tool's complexity.

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 should ideally describe what is returned (embedding vectors and their shape), which it does not. The feature-enablement note is a helpful prerequisite, but the agent still lacks return-value context for an embedding tool.

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 75% across 4 parameters, and the description adds no syntax or format detail beyond what the schema documents. Its only parameter-adjacent contribution is the feature-enablement caveat for image inputs, which the schema does not convey. Baseline 3 is appropriate given the schema does most of the work.

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 names a specific verb (creates), resource (embeddings), and scope (multimodal input items), which distinguishes it from the sibling create_embedding by implying combined text/image input. It stops short of explicitly naming that sibling, so the differentiation is implied rather than stated.

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?

Usage context is only implied: the multimodal framing suggests using this when image input is involved, and it notes image input may require feature enablement. There is no explicit when-to-use versus create_embedding or rerank_documents, and no stated alternative selection rule.

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?

Annotations already declare readOnlyHint=false, openWorldHint=true, idempotentHint=false and destructiveHint=false, so the mutation profile is covered. The description adds genuinely useful async context ('Returns a task ID for polling'), but says nothing about cost/credit consumption, latency, or what happens with the delivery-policy header — modest added value over 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?

Two tight sentences with the core purpose and the async return front-loaded; nothing is padded or repetitive. It is arguably under-sized for a 13-parameter multi-mode tool, but there is no wasted language.

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 output schema and 13 parameters supporting at least four distinct flows (music, lyrics, cover/extend, instrumental), the description leaves the agent without the mode selection logic, the conditional requirements, or the polling follow-up step. For this complexity level it is materially incomplete.

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 a middling 69% across 13 parameters, and the description contributes zero parameter meaning — no explanation of mv, tags, prompt, title, or the conditional requirements among action/audio_operation/continue_at. With conditional-required params present and coverage below 80%, the description should be compensating and does not.

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 music generation task using Suno') and names the async return ('task ID for polling'), which cleanly separates it from create_speech, create_video and create_image. However, the tool actually spans lyric generation and three uploaded-audio operations per the schema, and the description's narrower 'music generation' framing understates that 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 guidance, no mention of the MUSIC vs LYRICS action switch, no explanation of when the audio_operation modes (upload-cover/extend/add-instrumental) apply, and no pointer to the sibling poll tool (get_task_status) despite promising a task ID. The agent gets no routing help at all.

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?

Annotations already declare non-readonly, open-world, non-idempotent, non-destructive, so the safety profile is covered. The description adds genuinely useful behavioral context the annotations lack: model availability requirements, best-effort handling of unknown fields, and that model-specific support is not guaranteed. It omits return-shape/async behavior, but the additions are substantive.

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 sentences, front-loaded with the core action, then preconditions, then caveats. No filler, though the final two sentences could be tightened into one 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 21-parameter tool with deep nested objects (text, prompt, tools, reasoning) and no output schema, the description covers preconditions and unknown-field handling but says nothing about the returned response object or how previous_response_id/background/streaming interact. Adequate but with clear gaps 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 coverage is 95% across 21 parameters, so the schema does the heavy lifting and a 3 baseline is appropriate. The description adds only the generic 'unknown fields accepted on a best-effort basis' note, which arguably conflicts with the schema's additionalProperties:false rather than clarifying any specific parameter.

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 (Creates) and resource (a response) and disambiguates from siblings like create_chat_completion and create_anthropic_message by naming the 'OpenAI Responses API request shape' it accepts. The only residual ambiguity is that 'response' alone is vague without the API qualifier.

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?

Gives a real precondition — the model must advertise the Responses request format and be available — which is actionable selection guidance. However, it never says when to prefer this over the sibling create_chat_completion / create_anthropic_message / create_gemini_content tools, leaving the choice to inference.

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?

Annotations already declare the safety profile (readOnly=false, destructive=false, idempotent=false, openWorld=true), so the safety bar is low. But the description adds nothing behavioral: it does not mention that output is non-deterministic across calls, that it consumes paid TTS quota, or how streaming/large inputs behave — notable gaps for a generation tool whose annotations flag openWorld and non-idempotency.

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, front-loaded sentence with zero filler — the purpose is stated immediately. It is arguably under-specified rather than padded, so it scores well on conciseness but is not an exemplar of a complete-yet-tight definition.

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 12-parameter tool spanning multiple TTS model families with mutually exclusive voice/voice_id/prompt/instructions fields and no output schema, a single generic sentence is far too thin. It gives the agent no help choosing a model family or reconciling the overlapping voice-selection parameters.

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 schema already documents all 12 parameters (voice, model, speed, stream_format, response_format, etc.). The description adds no syntax, defaults, or format guidance beyond the schema, which is the expected baseline 3 when structured fields carry the load.

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 ('Generates audio from the input text') and even parenthesizes the canonical operation name (Text-to-Speech), so an agent knows exactly what this produces. It does not, however, distinguish itself from audio-producing siblings like create_music, translate_audio, or transcribe_audio, so the differentiation is left to the reader.

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 versus alternatives (create_music for music, translate_audio for speech translation), no prerequisites, and no mention of model/voice selection workflow despite model being a core choice. The agent must infer all of this from the name alone.

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?

Annotations already declare write/non-idempotent/open-world behavior. The description adds meaningful context beyond them: the task is asynchronous, returns a canonical task ID, usually returns a poll_url, and offers /v1/tasks/{id} as a fallback status endpoint. It does not cover failure, timeout, or cancellation behavior, but it adds useful lifecycle detail.

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 sentences, front-loaded with the core purpose, followed by the response/polling behavior. No redundant or filler content.

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 no output schema, the description usefully explains the return contract: task ID and usually poll_url. With 100% schema coverage, missing parameter explanations are not a gap. It could mention that inputs are highly model-dependent, but the schema carries that burden.

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 all 52 parameters are documented in the schema itself. The description adds no parameter-level meaning or syntax beyond what the schema provides, making the baseline 3 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 an asynchronous video generation task.' The video-generation resource distinguishes it from siblings like create_image, create_music, and create_3d_model. An agent can identify the tool's purpose without opening the schema.

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 such as create_image or edit_image. The only conditional guidance concerns post-creation polling, which is about handling the returned task rather than tool selection or prerequisites.

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?

Annotations already declare destructiveHint=true, idempotentHint=true, and readOnlyHint=false, so the safety profile is covered. The description adds useful context about which file classes can be deleted and the Anthropic Files API routing, but says nothing about permanence, error conditions, or side effects beyond what annotations imply.

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 sentences with the action front-loaded and no filler. The routing caveat is compact and earns its place, though the second sentence could be slightly tighter.

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 destructive tool whose annotations already carry the safety profile and with no output schema expected, the description adequately covers the deletion targets and the mode-dependent behavior. Nothing critical to correct invocation is missing.

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 both parameters (file_id and anthropic-beta) are already documented in the schema. The description explains the behavioral effect of anthropic-beta, which adds marginal value, but the baseline 3 applies when the schema does the heavy lifting.

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 (Deletes) and resource (batch file or bound image/reference file), clearly distinguishing the target from adjacent siblings like retrieve_file or upload_file. It is clear what the tool does, though it doesn't explicitly contrast itself with the other file-lifecycle tools.

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 retrieve_file or edit_image_file, nor any stated prerequisites or exclusions. The only conditional detail is the anthropic-beta routing behavior, which is a mode switch rather than usage 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?

Annotations already declare readOnlyHint=false, destructiveHint=false, idempotentHint=false and openWorldHint=true, so the mutation and external-call profile is covered structurally. The description adds the supported input modes (multipart upload, JSON URLs, images array) and a model restriction, but says nothing about async behavior, delivery policy, or what the edit returns.

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?

A single front-loaded sentence with no filler, and the input-mode enumeration is useful. It is efficient, though for a 20-parameter tool it is arguably thin rather than bloated.

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 complex 20-parameter, nested-schema, no-output-schema mutation tool, one sentence is not enough. The description does not cover the image input precedence (images vs image_urls vs image_url), the mask parameter, or the async/model-specific constraints.

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 only 35% across 20 parameters, so the schema does not carry the load. The description partially compensates by naming the three image-input shapes, which maps to image_url, image_urls, images and mask, but leaves the other parameters (size, quality, response_format, delivery policy, etc.) unexplained.

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 ('Edits an image') and names the three supported input modes, which is more than a tautology. However, it never distinguishes this tool from close siblings like edit_image_file or create_image, leaving the agent to guess which entry point applies.

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/when-not guidance and no alternative tool is named. The only implied constraint is 'for supported GPT Image models,' which narrows the model set but does not tell the agent when to pick this over edit_image_file or create_image.

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?

Annotations already declare the mutation profile (readOnlyHint=false, destructiveHint=false, idempotentHint=false, openWorldHint=true), lowering the bar. The description adds one genuine behavioral fact — that edits are only supported for certain GPT Image models — but says nothing about the async flag, cost implications, rate limits, or what happens to the source file, leaving significant behavior unexplained.

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?

A single front-loaded sentence with no filler; the input-mode clause is the most useful information and leads. It is arguably too terse for a 19-parameter tool, but nothing is wasted.

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 19-parameter, open-world mutation tool with no output schema and only 26% schema coverage, the description is far too thin. An agent cannot determine what most parameters do, how async mode behaves, or what the delivery-policy header affects without opening the schema.

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 only 26% across 19 parameters, so the description is expected to compensate. It clarifies the three source-image input forms (multipart 'image', repeated 'image[]', JSON array), which is useful, but the other ~15 parameters (size, quality, background, moderation, output_format, async, delivery policy) get no explanation in either place.

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 ('Edits an image') and adds the distinguishing detail that it accepts multipart uploads versus JSON image URLs, which hints at how it differs from the plain edit_image sibling. It stops short of naming the sibling or stating the selection rule 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?

The mention of input transport modes implies a file-upload context, but there is no explicit when-to-use guidance, no statement of when to prefer edit_image or create_image_file instead, and no prerequisites or exclusions. The agent must infer routing from the tool name alone.

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?

Adds genuine context beyond annotations: it states the response is synchronous structured decisions and that a decision does not authorize an external action. However it leaves the non-obvious readOnlyHint=false and idempotentHint=false flags unexplained (side effects, state mutation), so the behavioral profile is only partially clarified.

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?

Four short, front-loaded sentences that each carry a distinct point (purpose, prerequisite, return shape, safety note). Efficient with no obvious filler.

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?

Covers the return shape well in absence of an output schema and states the discovery prerequisite, but for a 4-parameter nested-object tool with 25% schema coverage it leaves the input formats under-specified.

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 only 25% and the schema has nested objects, so the description must compensate. It gives semantic grounding ('questions' are typed probability/Choice/Score, 'state' is shared, 'model' comes from discovery), but does not explain formats for model or state, leaving gaps.

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 ('Evaluate') and resource ('typed Noul probability, Choice, and Score questions against shared state'), which is enough to distinguish it from the surrounding model/pricing/chat siblings. The jargon ('Noul', 'System One endpoint') blurs it slightly but the core purpose is clear.

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?

Explicitly prescribes a prerequisite flow: 'Discover a decision model with list_models/get_model first', naming the alternative tools. It also contrasts with chat tools ('not chat text'), though it never states when-not to use it.

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?

Annotations declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false, covering safety and idempotency. The description adds 'agent-readable API overview' but does not elaborate on behavior beyond that. It does not contradict 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 with no extraneous words. It is front-loaded and 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?

Given the tool has no parameters and annotations cover safety, the description is minimal. However, it does not describe the return value or what the 'API overview' contains. For an agent, this may be insufficient for understanding the output.

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 are zero parameters, and schema description coverage is 100% by default. The description does not add parameter details, but it is not needed. Baseline 4 for no parameters.

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 'Fetch TokenLab's agent-readable API overview' clearly uses a specific verb ('Fetch') and resource ('TokenLab API overview'). The title reinforces this. No sibling tool has a similar name or purpose, so it is well-distinguished.

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 guidance is provided on when to use this tool versus alternatives or any prerequisites. While the tool is simple, there is no context about its typical usage (e.g., before making other API calls).

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?

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld), so the description's extra burden is low. It contributes by disclosing what the response contains and the caveat that pricing is returned 'when available', which is genuinely useful given there is no output schema.

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 tight sentences: the first front-loads what is returned, the second gives the usage context. No filler, no redundancy with the title.

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 output schema, the description does the necessary work of enumerating the return fields, and annotations cover safety. It only lacks guidance on error cases (e.g., unknown model ID) or how it relates to the pricing/model-listing siblings.

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 parameter ('model': 'The model ID') is already documented in the schema. The description adds no format, lookup, or ID-syntax detail beyond that, so the baseline 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 names a specific verb and resource ('Retrieves current model details') and enumerates the payload contents (supported operations, request parameters, endpoints, selector limits, pricing). It doesn't explicitly distinguish itself from near-siblings like list_models or get_model_pricing, but the detail level is well above tautology.

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?

It gives clear contextual guidance: 'Non-chat integrations should read these current model-specific request details before creating a request,' establishing the precondition for calling it. It stops short of naming alternatives or exclusions (e.g., when to prefer get_model_pricing or list_models), so it's a 4, not a 5.

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?

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds only the 'pricing-only' scope restriction, with no detail on return shape or pricing units/tiers.

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, purpose first, then usage constraint. No wasted words, though the second sentence's phrasing is vague rather than crisp.

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 simple single-parameter read tool with rich annotations and no output schema. The description covers purpose and scope adequately; only pricing-specific return expectations are left implicit.

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?

Only one parameter, and the schema already documents it at 100% coverage ('The model ID'). The description adds nothing about model ID format or lookup behavior, so baseline 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 (retrieves) and resource (model pricing detail) with scope qualifier 'pricing-only'. It separates itself from sibling get_pricing partially, though the distinction between the two pricing tools is not fully drawn.

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?

Gives one positive condition (price explanation) and one exclusion (not for non-chat request construction), but the exclusion is phrased in cryptic jargon and no explicit alternative sibling is named. Usage is implied rather than routed.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds one useful behavioral fact beyond that: only 'active' models are included, which tells the agent the result set is scoped. It still says nothing about pagination, result size, or freshness.

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?

A single front-loaded sentence with no filler. The core behavior (returns pricing for active models) leads, and the filtering caveat follows immediately.

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 should carry more of the return-value burden; 'public pricing surface' is evocative but vague about what fields come back or how many records. Combined with the missing differentiation from get_model_pricing, the definition is adequate but leaves real gaps for a read-only listing tool.

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 both optional parameters (tag, provider) are fully documented in the schema. The description merely restates them as 'optional provider and tag filters' without adding format, matching rules, or combinability semantics. Baseline 3 applies when the schema does the heavy lifting.

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 clear verb and resource: it returns the public pricing surface for active models. However, it does nothing to differentiate itself from the sibling tool get_model_pricing (nor from list_models/get_model), leaving the agent to guess which pricing tool to call.

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 is given. There is no mention of when this should be preferred over the near-identical sibling get_model_pricing, no prerequisites, and no exclusions. The agent must infer usage purely from the name.

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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint, so the safety profile is covered structurally. The description usefully adds that the poll_url is preferred and which job types the fixed endpoint covers, but says nothing about what a returned status/result contains or how polling should be paced.

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 tight sentences, no filler, with the core purpose front-loaded before the endpoint guidance. Every clause carries information the agent can act on.

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 an async status-polling tool with no output schema, the description should say something about the shape of the returned status/result (e.g., pending/succeeded/failed and where the result lives). That gap is partially offset by the endpoint routing detail and the annotation coverage.

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 parameter's provenance (id/task_id or embedded in poll_url) is already documented in the schema description. The tool description adds no syntax, format, or validation detail beyond that, so the baseline 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 ('Retrieves the status and result of an asynchronous generation task'), so the agent knows exactly what it returns. It also clarifies the scope (video, music, 3D jobs) and the canonical endpoint, though it never contrasts itself with the adjacent sibling cancel_task.

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?

Gives concrete routing guidance: prefer the poll_url from the create response, with /v1/tasks/{id} as the canonical fixed endpoint. That is a real when-to-use-which instruction, but it stops short of naming alternatives (e.g., cancel_task) or stating when not to poll.

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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, but the description adds zero behavioral context — no mention of pagination/cursor semantics, the beta header requirement, or the fact that scope_id currently returns an unsupported error. It contributes nothing beyond the structured fields.

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 words is not conciseness but under-specification; there is no front-loaded information because there is no information beyond the title restated.

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 7 parameters, cursor-based pagination, a beta-mode header, and a deliberately unsupported scoped cursor, the description is completely inadequate. An agent cannot call this correctly from the description 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 71%, so several parameters (after, limit) are undocumented even in the schema, and the description supplies no parameter meaning at all. It does not compensate for the coverage gap on a 7-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?

Tautological: description restates name/title.

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 the many file-related siblings, no mention of the Anthropic Files beta mode requirement, and no exclusions or prerequisites stated anywhere.

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?

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is fully covered. The description adds default-behavior context (native API calls default to full, integrations may default to compact) that isn't in the annotations, but says nothing about result volume, pagination, or how the two view shapes differ structurally.

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?

Four compact sentences, with the core purpose front-loaded and the parameter guidance following. No filler, though the default-behavior sentence is slightly tangential to the primary listing 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?

For a 5-parameter list tool with no output schema, the description covers view and recommended_for but is silent on the tag, category, and provider filters and on what the returned list contains. The schema carries those filters, so an agent can still call it, but the description is not fully self-sufficient.

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%, so the baseline is 3, but the description goes further by explaining the intent behind view=compact (model selection) versus view=full (OpenAI-compatible discovery shape) and by surfacing recommended_for for non-chat recommendations. It adds real semantic value on top of the schema, though it says nothing about tag/category/provider filtering.

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 opens with a specific verb+resource ("Lists the currently available models"), which is unambiguous. It does not, however, differentiate itself from sibling tools like get_model, compare_models, or get_model_pricing, so an agent must infer the boundary from names alone.

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?

It gives useful conditional guidance for one parameter ("Use view=compact for model selection and view=full for the OpenAI-compatible discovery shape"), which implies when each mode applies. But it never states when to choose this tool over get_model or compare_models, so overall routing guidance is only partial.

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?

Annotations declare readOnlyHint=false and idempotentHint=false but give no real safety detail, so the description carries most of the burden. It adds only 'semantic similarity' as mechanism and says nothing about ordering guarantees, what the response contains, or why a purely computational rerank is flagged non-read-only.

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?

A single efficient sentence with the core action front-loaded and no filler. It is well-sized, though it omits anything beyond the bare 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?

With 6 parameters, no output schema, and no return-value documentation, the description is too thin. An agent cannot tell what the reranked output looks like (ranked list, scores, whether documents are echoed) or how top_n/return_documents interact.

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 83%, so the schema already documents model, query, top_n, and documents. The description adds no per-parameter detail (e.g. default top_n behavior, what return_documents does) beyond what the schema provides, so the baseline 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 ('reranks'), resource ('documents'), and the mechanism ('by relevance to a query using semantic similarity'). However, it makes no attempt to distinguish itself from adjacent siblings like create_embedding, 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 when-to-use guidance, no prerequisites, and no named alternative. It never tells the agent when reranking is preferable to embedding or plain retrieval, nor when not to use it.

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?

Annotations already indicate read-only, idempotent, and open-world hints. The description adds no further behavioral context such as authentication needs, return format, or what 'retrieve' entails.

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?

Overly brief to the point of being uninformative. It is not concise in a helpful way—it fails to convey any useful information beyond the tool name.

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?

Without an output schema, the description should explain what is returned. It does not. With two parameters and no behavioral details, the description is grossly incomplete for an AI agent to understand its usage.

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 only 50% (file_id lacks a description). The tool description does not clarify the meaning of file_id or the purpose of the 'anthropic-beta' parameter beyond what the schema already provides.

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?

Tautological: description restates name/title.

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 guidance is provided on when to use this tool versus alternatives. For example, it does not clarify how it differs from 'retrieve_file_content' or 'list_files'.

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?

The description adds no behavioral details beyond what annotations already indicate; no mention of file size limits, content type, or other operational traits.

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?

Extreme under-specification; three words do not constitute a useful description.

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 no output schema and no parameter details, the description is completely inadequate for an agent to correctly invoke the tool.

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?

With 0% schema description coverage, the description fails to explain the required 'file_id' parameter, its format, or how to obtain it.

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?

Tautological: description restates name/title.

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 guidance is provided on when to use this tool versus alternatives; there is no mention of context, prerequisites, or exclusions.

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?

Annotations already declare readOnlyHint=false, destructiveHint=false, idempotentHint=false and openWorldHint=true, covering the safety profile. The description adds nothing beyond that – no mention of cost, file size limits, latency, or that the call is non-idempotent and may re-bill on retry – so it earns little credit beyond the structured hints.

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?

A single short sentence with the purpose front-loaded and zero filler. It is not padded, though it is arguably terse to the point of under-specification rather than optimally structured.

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 eight parameters, two enums, a hidden Delivery-Policy header, and no output schema, one sentence is insufficient. It never explains the required file/model pairing, the response_format implications, or the timestamp_granularities/verbose_json coupling, leaving the agent to reconstruct these from schema text 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%, so all eight parameters including the enums and the verbose_json dependency for timestamp_granularities are documented in the schema itself. The description adds no parameter-level meaning, so the baseline 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 ('Transcribes audio into text') with the domain clarified as Speech-to-Text, so the core action is unambiguous. However, it offers no differentiation from the sibling translate_audio tool, which a naive agent could easily confuse with transcription.

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 when-to-use guidance, no prerequisites (e.g., that the audio must first be uploaded or a local path supplied), and never mentions the alternative translate_audio tool. The agent must infer everything about selection from the tool name alone.

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?

Annotations already cover the safety profile (destructiveHint=false, idempotentHint=false, openWorldHint=true), lowering the bar, but the description adds only that output is English. It says nothing about supported source languages, return format, rate limits, or how the delivery-policy header affects 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?

A single front-loaded sentence with no filler or redundancy. It is appropriately sized for the thought it contains, though the brevity is partly under-specification rather than precision.

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 six-parameter tool with no output schema, the description omits essential context: what the translation returns, what response_format values do, how prompt/temperature influence results, and which source languages are supported. It is far too thin for the tool's actual 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 coverage is only 33%; model, prompt, temperature, and response_format have no schema descriptions, and the description supplies none either. The one documented parameter (file) and the enum'd delivery policy carry all the meaning, so the description fails to compensate for the coverage gap.

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 compound action on a specific resource: transcription of audio plus translation of the result to English. This distinguishes it functionally from siblings like transcribe_audio and translate_text, but it never names those siblings to make the routing explicit.

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 or when-not-to-use guidance. With siblings transcribe_audio and translate_text present, the agent is left to infer that this tool is the combined one-step path rather than using the two siblings in sequence.

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?

Annotations already declare readOnlyHint=false, openWorldHint=true, idempotentHint=false and destructiveHint=false, so the safety profile is covered. The description adds nothing beyond that – no auth requirements, rate limits, or behavior of the source/target language handling – and the 'current translation request format' phrase is opaque rather than informative.

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?

It is a single front-loaded sentence, which is good, but roughly half of it ('using the current translation request format') is meaningless filler that does not earn 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?

For a 7-parameter tool with 14% schema coverage and no output schema, the description is far too thin. It omits parameter guidance, the relationship to translate_audio, and any behavioral detail an agent would need 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 only 14% (one of seven parameters documented), so the description must compensate and does not. It says nothing about model, text, source_language, target_language, mime_type, or user, leaving most parameters undocumented in both schema and prose.

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 ('Translates') and resource ('text') with the target language as output, so the core action is unambiguous. However, it never distinguishes itself from the sibling translate_audio, and the trailing clause 'using the current translation request format' is vague filler that adds no clarity.

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, no prerequisites, and no mention of alternatives such as translate_audio or the relevant siblings. The agent is left to infer the context entirely.

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?

Annotations already declare readOnly=false, idempotent=false, openWorld=true, destructive=false, so the safety profile is covered. The description adds real value by explaining the two-mode behavior and that Anthropic mode omits the purpose field and returns Anthropic file metadata, but it does not mention that repeat uploads are non-idempotent (duplicates) or any auth requirements.

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 dense sentences with no filler, front-loading the primary action before the mode-specific behavior. Slightly technical phrasing but every clause carries information.

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 4-parameter upload tool with no output schema, the description covers both operating modes and notes the Anthropic return metadata. Remaining gaps (non-idempotency, error cases) are minor given the annotations and complete schema coverage.

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%, with the schema itself explaining file path, model default, purpose enum, and the anthropic-beta value. The description corroborates the anthropic-beta/purpose interaction but adds no syntax or format detail beyond the schema, so the baseline 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 states a specific verb and resource (uploads a batch or image file) and names the two API modes it supports. It distinguishes itself from siblings like list_files/retrieve_file, though it never explicitly contrasts with create_image_file or the other create_* tools that an agent might confuse it with.

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?

Usage is implied through the conditional mode description: the anthropic-beta value triggers Anthropic Files mode, and 'purpose' is described as required in TokenLab/OpenAI mode. However, there is no explicit when-to-use guidance or named alternative for choosing upload_file over create_image_file or edit_image_file, so the agent must infer.

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