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

Discloses immediate return, background execution, polling mechanism, concurrency behavior, timeout handling, TTL, and state loss on restart. Annotations (readOnlyHint=false, etc.) are consistent and the description adds rich behavioral context.

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?

Well-organized with clear sections (overview, when to use, important note, args, return, example). Each sentence is purposeful. Slightly verbose but efficient for the 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?

Given 5 parameters, no output schema, and complexity of background jobs, the description covers all key aspects: usage, parameters, return shape, polling instructions, and edge cases (timeout, TTL, server restart). Highly 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?

Schema coverage is 100%, but the description adds significant value: explains items array structure with example, cautions about concurrency rate limits, explains download default, timeout behavior, and TTL meaning. Goes well 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?

The description explicitly states 'Run multiple Replicate predictions in parallel as a background job' with specific verb and resource. It distinguishes from siblings by mentioning polling replicate_batch_status and contrasting with single prediction 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?

Provides explicit when-to-use: 'when you have 2–50 predictions to run and don't want to block.' Also warns about rate limits and suggests replicate_get_model_schema for correct identifiers.

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 discloses the return format (structuredContent with detailed fields), explains overall_status values with meanings, and provides polling guidance. Annotations already declare readOnlyHint and idempotentHint, and the description adds behavioral context beyond those without contradiction.

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 well-structured with a summary line, argument list, return type explanation, status definitions, and a tip. Every sentence serves a purpose, and the structure aids readability without unnecessary verbosity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers all aspects needed for a polling tool: purpose, parameters, return structure (including example fields), status interpretations, and a practical polling interval. Given the tool's complexity, it leaves no critical gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already describes both parameters (100% coverage), but the description adds practical value for include_results, explaining the trade-off between full results and counts-only summary for large batches. This extra context justifies a score above baseline 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it polls the status of an async batch job, specifically linking to replicate_batch_start. It uses precise language ('poll the status') and distinguishes itself from sibling tools like replicate_batch_start (starts jobs) and other status 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?

The description explicitly connects to replicate_batch_start, implying usage after starting a batch job. It also provides a concrete polling tip (every 10-30 seconds). However, it does not explicitly state when not to use it or contrast with single-prediction status tools, though the sibling context makes it clear.

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 include destructiveHint=true and idempotentHint=true. Description adds that the tool cancels in-progress predictions and returns a PredictionSummary with updated status (typically 'canceled'). This provides behavioral detail beyond annotations, such as the return type and typical outcome.

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 paragraphs: first states purpose and usage context, second lists args and returns. Every sentence is essential; no redundancy. Front-loaded with core 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?

Given the simple operation (cancel by ID), the description covers all needed aspects: purpose, when to use, parameter source, and expected result. No output schema exists, but the description adequately describes the return type.

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% for the single parameter prediction_id. Description adds context: 'returned by an earlier generate_* call,' which helps the agent understand the source of the ID, going beyond the schema's basic description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'Cancel an in-progress prediction by its ID.' This is a specific verb+resource pair, and it distinguishes from sibling tool replicate_cancel_training which cancels training rather than predictions.

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?

Provides guidance: 'Useful for long-running async jobs (video, large LLM) when the user no longer needs the result.' This tells the agent when to use the tool. It also notes that the prediction_id comes from an earlier generate_* call, aiding context. Could explicitly mention when not to use, but clear enough.

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 annotations (destructiveHint, idempotentHint), it warns about cost and duration, and indicates the returned status. No contradictions with 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?

Very concise, two sentences with args and returns fully covering the tool's purpose and usage.

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?

Adequate for a simple cancel tool: explains when to use, parameter, and return type. Could mention error cases but not necessary.

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 has 100% coverage for training_id, but the description adds context that only in-progress trainings can be canceled, adding value 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?

The description clearly states the tool cancels an in-progress training run by ID, distinguishing it from sibling tools like replicate_create_training and replicate_get_training.

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?

Provides context to cancel when training is no longer needed to avoid costs, but does not explicitly contrast with alternatives like replicate_cancel_prediction.

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 non-readonly, non-idempotent, non-destructive. The description adds the crucial timeout behavior (polling via replicate_get_prediction), explains the download parameter is irrelevant for text, and describes the return format. No contradictions.

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 efficiently structured: a short purpose sentence, a list of use cases, a clear Args section with inline notes, a Returns section, and concrete examples. Every sentence adds distinct value with no redundancy.

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 absence of an output schema, the description sufficiently explains the return value (text_output[0]) and polling behavior when timeout is exceeded. All 8 parameters are covered with examples, making the tool fully understandable for an agent.

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 description enriches every parameter: explains model curated keys vs custom format, clarifies download default and reason, details timeout default and polling fallback, provides examples for prompt and system_prompt usage. This goes well 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?

The description uses a specific verb 'Run' with a clear resource 'large language model', and enumerates diverse text-generation use cases (Q&A, code, summarisation, translation). It inherently distinguishes from sibling tools that generate images, audio, or video.

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 text-in/text-out tasks and contrasts with multimodal siblings. However, it does not explicitly state when not to use this tool or mention alternative tools for specific sub-tasks like chat or code generation.

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 substantial value beyond annotations: it discloses URL expiry (~24h), timeout polling behavior, and a display requirement. It aligns with annotations (readOnlyHint=false, destructiveHint=false) and provides no contradictions.

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 well-structured with a summary, display requirement, parameter list, returns note, and examples. It is appropriately concise without missing essential details.

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, nested objects, and no output schema, the description covers key aspects: input constraints, timeout handling, and return format. It could mention how to extract the URL from results or handle multiple files, but overall it is sufficiently 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?

Despite high schema coverage (86%), the description adds meaning: it clarifies the role of reference_audio_url, lists default values, and explains timeout behavior. The Args section provides context not captured in the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with 'Synthesize speech in a cloned voice,' clearly stating the tool's core function. It distinguishes itself from sibling tools like replicate_generate_speech by focusing on voice cloning from a reference sample.

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 clear usage context: it specifies the reference audio length (~5-30 s) and directs users to replicate_upload_file for local files. However, it does not explicitly contrast this tool with alternatives like replicate_generate_speech.

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 provide readOnlyHint=false, openWorldHint=true, idempotentHint=false. The description adds value by explaining the asynchronous behavior (returns immediately with training ID) and the need to poll. 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 concise paragraph followed by a bulleted list of arguments. It is front-loaded with the purpose and every sentence is necessary and informative, with no redundancy.

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?

Despite no output schema, the description specifies the return type (TrainingSummary) with fields. It covers async behavior, prerequisites, and links to relevant tools for polling and schema introspection, making it fully informative for a training start 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 100% and all parameters have descriptions. The description adds extra context with examples, inline version pinning, and a cross-reference to replicate_get_model_schema for the input object, which goes 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?

The description uses a specific verb ('Kick off') and clearly identifies the resource (fine-tuning/training run on a trainable base model). It includes an example and distinguishes from sibling tools like replicate_get_training (polling) and replicate_run_model (inference).

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 explicitly mentions polling with replicate_get_training and states a prerequisite (destination must exist). However, it does not explicitly say when to use this tool versus alternatives like replicate_run_model, though the context makes it clear it's for training, not 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 indicate idempotency and non-destructiveness. The description adds return format details (structuredContent.output) and mentions model-specific shapes, providing behavioral context beyond 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 concise (about 120 words) and well-structured: purpose, use cases, args list, return value. No redundant sentences.

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 5 parameters, no output schema, and nested objects, the description covers main parameters and return format adequately. It lacks explanation for download and timeout_ms, but schema descriptions compensate. An example would improve completeness.

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 80% schema description coverage, the description adds value by explaining texts (independent embedding, max 256), model (curated list or custom), and extra_input with an example. Two parameters (download, timeout_ms) are not elaborated but have schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool converts text into numeric embedding vectors and lists specific use cases (RAG, semantic search, clustering, similarity scoring), distinguishing it from siblings that generate images, audio, etc.

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 clear use contexts but does not explicitly contrast with alternative tools or state when not to use it. However, the embeddings-focused purpose implicitly differentiates from siblings.

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 and idempotentHint=true, indicating safe reads. The description adds valuable context: prices are a 'hand-curated snapshot' and 'actual billing comes from Replicate,' and it clarifies required parameters for per-second models. No contradictions with 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 well-structured with clear sections for Args, Returns, and Examples. It front-loads the purpose in the first sentence. Every sentence adds value, and there is no redundancy or wasted words.

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 3 parameters and no output schema, the description explains the return structure (structuredContent with fields like resolved_model_id, estimated_usd, etc.) and provides multiple concrete examples. It covers the key aspects needed for an agent to understand and invoke the tool correctly. Minor gap: could mention that estimates are approximate, but that is already stated.

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%, but the description adds meaning beyond the schema by explaining parameter types (e.g., model can be owner/name or short key), providing examples of how parameters affect the estimate, and listing default values and ranges. This helps agents use parameters correctly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns an approximate dollar-cost estimate for a planned prediction before running it. It uses specific verbs ('estimate cost') and identifies the resource ('Replicate prediction'), and it distinguishes from sibling tools which focus on running models, listing, etc.

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 explicitly says when to call this tool: 'when the user asks "how much would X cost" or before launching a costly model.' While it doesn't list when not to use it or alternatives, the context makes it clear it's a unique cost estimation tool among many execution tools.

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 behavioral context beyond annotations: it mentions slowness (1-5 min), URL expiration (24h), timeout behavior (default 300000 ms, returning prediction ID on timeout), and return type (PredictionResult with local_paths). Annotations already indicate non-readonly, open-world, non-idempotent, and non-destructive, and the description aligns with these without contradiction.

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 well-structured: purpose first, then behavioral notes, then an Args list, then examples. It front-loads key information. While it is somewhat lengthy, every part serves a purpose, and it avoids redundancy with the schema.

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 absence of an output schema, the description adequately covers what is returned (PredictionResult with local_paths) and includes examples, parameter details, and behavioral notes. It addresses input requirements, display requirement, and timeout behavior. Minor omissions could include error handling or failure modes, but overall it is fairly comprehensive for a 6-parameter 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 high (83% of parameters have descriptions), so baseline is 3. The description adds additional semantics: for image_url it mentions using replicate_upload_file for local files, for model it gives curated keys and custom format, for timeout it explains what happens if exceeded, and for extra_input it provides an example. This adds meaningful value 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?

The description clearly states the tool's purpose: 'Generate a 3D mesh (GLB/OBJ) from a text prompt or a reference image.' This is specific and distinct from sibling tools like replicate_generate_image or replicate_generate_video.

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 clear guidance on when to use this tool (for 3D generation) and includes important usage notes such as requiring at least one of prompt or image_url, indicating that 3D generation is slow (1-5 minutes), and a display requirement for download URLs. It also gives examples for different scenarios. While it doesn't explicitly exclude alternatives, the context is sufficient.

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 non-readOnly (mutating) and non-destructive, but description adds context: download behavior, timeout handling, URL expiry (~24h), and model-specific parameter behavior. No contradictions. Score 4 because while annotations set baseline, description adds significant behavioral detail beyond them.

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?

Description is long but well-structured with headings, bullet points, and examples. Every sentence adds value. Slightly verbose for some details (e.g., model list repeated), but still efficient. Front-loads core purpose and display requirement.

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, description explains return format (PredictionResult with local_paths and URLs), covers edge cases (timeout returns prediction ID for polling), and model-specific constraints. Complete for a complex audio generation 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 100%, and the description enriches each parameter with model-specific meaning: prompt becomes 'tags' for ace-step, duration_seconds ignored for some models, extra_input includes lyrics example. This adds major value beyond schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it generates music or audio from text prompts, using verbs like 'generate' and specifying resource (music/audio). It distinguishes between models (musicgen, ace-step, etc.) and their capabilities, differentiating it from sibling tools like replicate_generate_speech or replicate_generate_video.

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 provides when to use each model (e.g., 'musicgen for instrumental up to 30s', 'ace-step for songs with lyrics'), what parameters to set (e.g., prompt mapping, duration support), and what not to do (e.g., 'do NOT pass duration_seconds for lyria'). Includes display requirement and URL expiry warning.

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 reveals multiple behavioral traits beyond annotations: URLs expire in ~24h, download behavior, timeout with polling fallback, error handling for missing API token, and the return structure with status fields. It also notes the display requirement for embedding images. No contradiction with 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 well-structured: purpose, usage, display requirement, args, returns, examples, error handling. It is front-loaded with the core action. While somewhat lengthy, each section is justified and no redundant sentences.

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 complexity (8 parameters, nested object, no output schema), the description covers all essential aspects: input details, return structure, error cases, polling mechanism, and even display instructions. It is comprehensive for an AI agent to invoke 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 coverage is 100% (all 8 parameters described), so baseline is 3. The description adds value by explaining defaults (model, aspect_ratio, download), providing examples, and guiding use of extra_input with replicate_get_model_schema. It goes beyond schema repetition.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates images from text prompts and specifies it handles 'draw/create/generate an image of' requests. It distinguishes from sibling tools like replicate_generate_audio or replicate_generate_video by focusing on image generation.

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 clear context on when to use the tool ('any draw/create/generate an image of request') and mentions the default model (Flux Schnell) for speed. It does not explicitly exclude alternatives but the domain is clearly image generation, which is sufficient given sibling tools.

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 non-readonly, open world, non-idempotent, non-destructive. Beyond that, the description adds valuable context: URL expiration (~24h), timeout behavior with polling fallback, voice cloning capability via extra_input, and download behavior. No contradiction with 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?

Well-structured with clear sections: purpose, display requirement, parameter list, return info. Each sentence is purposeful, no redundancy. Slightly verbose but appropriate for the 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?

Covers most aspects but lacks detailed return value documentation (no output schema). Missing error handling or rate limit info. The description mentions local_paths and URL behavior but does not fully describe PredictionResult structure.

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%, providing baseline 3. Description adds significant meaning: lists curated model keys, explains voice naming convention, gives concrete example for extra_input, and details timeout_ms behavior. Goes beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Convert text to natural-sounding speech,' providing a specific verb and resource. It does not explicitly differentiate from sibling tools like replicate_generate_audio or replicate_clone_voice, but the function 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?

No direct guidance on when to use this tool versus alternatives. The description implies usage for TTS but lacks explicit comparisons or exclusionary criteria, limiting agent decision-making.

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 discloses key behaviors beyond annotations: video generation is slow (1-5 min), URLs expire in ~24h, timeout handling returns pending=true and prediction_id for polling, and download defaults to true. Annotations only indicate readOnlyHint=false and openWorldHint=true; the description adds critical operational details.

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 well-structured: purpose first, then display requirement, then parameters in list form, then returns, then tip. It is somewhat long but every sentence adds value. Minor redundancy in parameter listing (mirrors schema) could be trimmed but still functional.

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 8 parameters (1 required) and no output schema, the description covers the async workflow, timeout and polling, download behavior, and display requirement. It references the return shape from replicate_generate_image, which is acceptable. The tips for handling long videos complete the picture.

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%, providing detailed parameter descriptions. The description adds value by explaining the model parameter format ('owner/name[:version]'), noting image_url support varies by model, and advising on timeout_ms for long videos. This operational context goes 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?

The description clearly states it generates a video clip from a text prompt and optionally an image. The verb 'generate' and resource 'video' are specific. It distinguishes from siblings (e.g., replicate_generate_image, replicate_generate_audio) by focusing on video generation.

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 the tool is for video generation, but it does not explicitly compare it to alternatives like replicate_generate_image or replicate_run_model. There is no 'when-to-use' or 'when-not-to-use' guidance. The tip about polling after timeout is useful post-call, not for initial decision.

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 readOnlyHint, idempotentHint, and non-destructive. The description adds that it returns 'structuredContent: DeploymentSummary' with specific fields, providing context beyond 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 exceptionally concise: one sentence plus a brief parameter list. It front-loads the key purpose and uses efficient formatting.

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 simple tool with one parameter and no output schema, the description is complete. It covers what the tool returns, and additional details (errors, pagination) are unnecessary.

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% with a clear description of the 'deployment' parameter. The description repeats this info without adding new semantics, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Get the configuration of one deployment' and lists specific fields (model, version, hardware, autoscaling min/max). It distinguishes from sibling tools like 'replicate_list_deployments' (list all) and 'replicate_run_deployment' (execute).

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 explicit when-to-use or when-not-to-use guidance is provided. The usage is implied by the name and description, but alternatives like listing deployments are not mentioned.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds value by detailing the structured content returned, including schema and metadata fields, which is beyond the annotation info.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured with clear 'Args' and 'Returns' sections. Every sentence serves a purpose, and the main use case is front-loaded. No unnecessary words.

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 one parameter, comprehensive annotations, and no output schema, the description compensates by fully specifying the return structure. It is complete enough for an agent to understand what the tool does and what to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds minor clarity by specifying the parameter format 'owner/name' or 'owner/name:version', but the schema already includes that. No significant additional meaning 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?

The description clearly states 'Retrieve metadata and the OpenAPI input/output schema for a specific Replicate model.' It uses a specific verb (retrieve) and resource (schema), and distinguishes the tool from siblings like `replicate_run_model` by noting it should be used before running a model.

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 explicitly states 'Use this before replicate_run_model to know which fields the model accepts and what they mean.' This provides clear context and an alternative tool. It does not mention when not to use, but the guidance is strong enough.

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 provide readOnlyHint, idempotentHint, destructiveHint. The description adds detailed behavioral context: it explains return shape (PredictionResult same as replicate_generate_image), possible statuses ('processing', 'starting', succeeded), and the behavior of the download parameter. The typical flow further illustrates the polling pattern.

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 well-structured: purpose sentence, bullet-pointed args, returns section, and numbered typical flow. It is concise with no extraneous information, front-loading key usage details.

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 explains the return shape and possible statuses. The typical flow completes the picture. Missing details like error handling or exact local download paths, but overall complete for a polling 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 100%, so parameters are documented. The description adds value by explaining that prediction_id comes from a previous call and that download only works if the prediction has succeeded. This provides meaningful context beyond the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Retrieve the current status and (if available) outputs of a Replicate prediction by its ID.' It uses a specific verb (retrieve) and resource (prediction by ID), and the typical flow distinguishes it from sibling tools like replicate_batch_status or replicate_get_training.

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 explicitly says 'Use this when a previous generate_* or run_model call returned pending=true (timed out before completion).' This provides clear context for when to use the tool. It lacks explicit exclusions of alternatives but the guidance is strong.

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 provide idempotent, read-only, and non-destructive hints. The description adds that the tool returns status, model version, and error, giving further insight into the response structure. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with a clear purpose statement, an Args section, and a Returns note. Every sentence adds value, and the structure is easy to parse.

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 only one parameter and no output schema, the description fully explains what the tool does and what it returns (TrainingSummary with status, version, error). No gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage with a description for training_id. The description adds that the ID comes from replicate_create_training, providing context beyond the schema's description of 'ID of the training run to inspect'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves the current state of a training run, listing specific data: status, trained model version, and errors. It distinguishes from siblings like replicate_create_training and replicate_list_trainings by focusing on a single training's detailed state.

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?

Specifies that training_id must come from replicate_create_training, implying this tool is used after creation to check progress. While it does not explicitly exclude alternatives like replicate_list_trainings or replicate_cancel_training, the context makes its use case clear.

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 non-read-only and non-destructive nature. The description adds context about display requirements (embed result inline) but does not disclose other behavioral traits like rate limits, auth needs, or failure modes. No contradiction with 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?

Description is structured with two paragraphs and a bullet list, front-loading the purpose. It could be slightly more concise (e.g., timeout explanation is verbose), but overall effective.

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?

Missing output schema means the description should clarify return format; it only mentions display requirements. With 7 parameters and 71% schema coverage, the description covers key ones but leaves gaps (e.g., no error handling or polling details for timeout).

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 71%, and the description adds meaningful context beyond the schema, such as clarifying mask semantics (white=keep, black=repaint) and model examples (flux-fill-pro). However, it omits details on timeout_ms and download, which are in the schema but not reinforced.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Fill masked regions of an image based on a text prompt' and distinguishes between inpainting and outpainting, making the tool's purpose specific and differentiated from sibling image tools like replicate_generate_image or replicate_remove_background.

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 explicit guidance on when to use this tool vs alternatives (e.g., replicate_generate_image for full image generation). The description implies use for inpainting/outpainting but lacks 'when not to use' or references to other tools.

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 non-readonly, non-destructive. The description adds that it produces MP4 video, URLs expire in ~24h, and that timeout returns prediction ID for polling. No contradictions. This context is valuable beyond structured 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?

Well-structured with overview, display requirement, parameter list, and examples. Every sentence adds value, though it's a bit long. No wasted words; front-loaded with the core 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?

No output schema, but description explains return type (PredictionResult with mp4 files) and display requirement. It covers timeout handling and model-specific extras. For a complex tool with 7 params and nested objects, it is sufficiently 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?

Schema coverage is high (86%), so baseline 3. The description adds meaning beyond schema: text maps to 'voice_script', extra_input usage, model defaults, and timeout_ms behavior. It compensates for the 14% uncovered (like timeout_ms details) and provides examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it animates a portrait to speak using either text (TTS+lipsync) or audio driving. It distinguishes from sibling tools like replicate_generate_video and replicate_generate_speech by focusing on lipsync. The verb 'animate' and resource 'portrait image' are specific.

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 explains when to use text vs audio_url, and that at least one is required. It mentions using replicate_upload_file for local files, and shows examples for both modes. However, it does not explicitly exclude cases like when model selection is inappropriate, but the overall guidance is clear.

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 provide readOnly, idempotent, not destructive. Description adds authentication context, return format details (structuredContent with DeploymentSummary fields), and explains what a deployment is. No contradiction.

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 efficient sentences for purpose/definition, then clear args and return. Every sentence adds value. Front-loaded with main 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?

Covers purpose, parameter, return structure. For a list tool with one param and no output schema, it is mostly complete. Could mention that multiple pages may exist (openWorldHint), but not critical.

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 covers limit parameter fully with description, default, min, max. Description mirrors this without adding significant new meaning. Baseline 3 due to high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'List the deployments on the authenticated Replicate account' and defines what a deployment is, distinguishing it from siblings like get_deployment (singular) and run_deployment (execute).

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?

Implied usage (when you want to list all deployments) but no explicit when-not or alternative references to get_deployment for a specific deployment.

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, etc. The description adds that it returns predictions from the authenticated account and includes the return structure, providing behavioral context beyond 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 concise (three sentences) and well-structured: purpose first, then parameter, then return format. No unnecessary words.

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 simple list tool with one parameter and good annotations, the description covers purpose, parameter behavior, and return structure (compensating for missing output schema). It is complete and self-contained.

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 describes the limit parameter fully. The description repeats the same info, adding no new meaning beyond what the schema provides. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns the most recent predictions on the authenticated account, with specific use cases (recover prediction ID, audit, check running). It is distinct from siblings that deal with single predictions or trainings.

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 explicit use cases (listing recent predictions for recovery, audit, or status check), helping the agent decide when to invoke. It does not name alternatives, but the context is clear enough given the large sibling list.

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, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the tool's safety is clear. The description adds the scope 'most recent' but does not elaborate on ordering, time limits, or pagination behavior beyond the limit parameter.

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 very concise, with the core purpose front-loaded. The Args section is brief but includes necessary detail. No wasted sentences.

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 simple list tool with one parameter, rich annotations, and many similar siblings, the description adequately covers the purpose and basic usage. It could mention return type structure more explicitly but is otherwise 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?

Schema coverage is 100% for the single parameter limit, including default and range. The description restates the schema info, adding no new meaning beyond 'How many trainings to return.' Baseline score 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 clearly states it returns recent training runs for the authenticated account, using specific verb and resource. It distinguishes itself from siblings like replicate_get_training (single training) and replicate_create_training (create).

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 on when to use this tool versus alternatives like replicate_get_training or replicate_create_training. The description lacks context for selection among many sibling tools.

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?

Discloses async nature, polling needed, parallel execution, template syntax for dependencies, per-step timeout, TTL with state loss on restart, and download option. Adds substantial context beyond annotations indicating a mutation.

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?

Well-structured with summary, important note, template reference, args, return, and example. Slightly lengthy but front-loaded and each sentence serves a purpose. Minor redundancy could be trimmed.

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 all parameters, dependencies, timeouts, TTL, download, and return value. Includes polling guidance and example. No output schema but explains what to expect. Complete for a complex pipeline 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 100%, but description adds significant value: explains template syntax for step dependencies, automatic dependency inference, and provides a detailed example. Baseline 3 exceeded due to extra context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it runs a DAG of predictions as a background job, returning a pipeline_id immediately. It distinguishes from siblings like replicate_run_model by emphasizing concurrent step execution and dependency handling.

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 advises that model must be a full Replicate identifier and not shortcuts, directing to replicate_get_model_schema for lookup. Provides an example but does not explicitly contrast with sibling tools like replicate_batch_start.

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 indicate read-only, idempotent, non-destructive. Description adds behavior details: polling loop, include_outputs toggle for performance, and that pipeline-level errors are caught at start, not here. No contradiction.

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?

Description is structured with sections (Args, Returns, Tip), but is somewhat long. Every sentence provides value, including the example structure and note about pipeline errors.

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?

Comprehensive for a status poller: explains return structure, statuses (running, completed, partial), and usage pattern. No output schema, but the description provides detailed structure.

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 covers both params (100% coverage). Description adds context: pipeline_id origin, include_outputs default and effect (full vs counts-only), beyond 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?

Description clearly states it polls status of a pipeline started with replicate_pipeline_start, lists return fields and statuses, distinguishing it from the start tool.

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 says to poll after starting a pipeline and gives polling interval tip (10-30s). Does not explicitly compare to other status tools among siblings, but usage is clear.

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 indicate readOnlyHint, idempotentHint, and destructiveHint are all appropriately set. The description adds: 'This does NOT run anything — it advises which model to use,' aligning with annotations and providing extra clarity. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: intro, workflow, args, return format, examples. Every sentence adds value without redundancy.

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?

Despite no output schema, the description provides a structured return format example. It covers inputs, outputs, and workflow, making it complete for an advisory 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 100%, and the description reiterates each parameter with additional context and examples. It explains the enum values, defaults, and optional behavior, adding significant value 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?

The description clearly states the tool ranks curated models by priority and returns recommendations. It uses specific verbs (rank, recommend) and identifies the resource (models in a category). It distinguishes itself from sibling tools like replicate_generate_* by explicitly noting it does not run anything.

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 includes a workflow: call this to pick a model, then call a generate tool. This provides clear guidance on when to use it. However, it does not explicitly mention when not to use or contrast with alternatives like replicate_estimate_cost.

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 behavioral context beyond annotations: explains it returns suggestions only, does not modify code, and details the search scope (popular models not in curated registry). Annotations already provide readOnlyHint and destructiveHint.

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?

Well-structured with one-line summary, usage advice, parameter list with defaults, return structure, and examples. No redundant sentences.

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?

Comprehensive for a read-only search tool with three optional parameters. Describes output structure in detail, compensating for missing output schema. Annotations support safe 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 100%, but description elaborates with default values, full list of category options, and practical examples. Adds meaning beyond schema descriptions.

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?

Clearly states it searches for popular Replicate models not in the curated registry and returns suggestions without modifying code. Distinguishes from siblings by focusing on uncurated popular models.

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?

Provides explicit usage advice: 'Use this to find new models worth adding. Then ask Claude to edit src/models.ts with the ones you want.' Does not explicitly exclude alternatives but context is clear.

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 indicate readOnlyHint=false and openWorldHint=true. Description adds that the output is a PNG and notes local download, but does not elaborate on timeout behavior (though mentioned in schema), side effects, or rate limits. Adequate but not comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose stated first, followed by a crucial display requirement, then concise parameter list, return type, and examples. No unnecessary words; every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Examples and return type help, but no output schema exists and description doesn't detail the PredictionResult structure or error scenarios. For a tool with 5 params and nested objects, more detail would be beneficial.

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?

Description explains image, model (curated keys vs custom), extra_input, and download meaningfully. However, it omits the timeout_ms parameter. With 60% schema coverage, the description adds value for most 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 clearly states the tool produces a transparent-background PNG, with examples differentiating from siblings like replicate_generate_image or replicate_inpaint. The title also reinforces the specific function.

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?

Provides explicit display instructions for embedding the result, but does not specify when to use this tool versus alternatives like replicate_segment or replicate_inpaint. No prerequisites or exclusions are given.

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

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

The description fully discloses that the tool is a blocking write operation (non-readonly), auto-downloads by default, supports optional timeout, and returns a standard prediction result with inline previews, URLs, local paths, and prediction ID. This adds value beyond the annotations (readOnlyHint=false) and schema by detailing the synchronous execution behavior and return format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: a clear opening sentence followed by a bulleted Args block. Every sentence is informative with no redundancy. The structure aids quick scanning for the agent.

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 4 parameters, no output schema, and moderate complexity, the description covers all essential aspects: the action, blocking behavior, download behavior, timeout handling, and return value components. It also mentions URL expiration for download=false, which is not in the schema. The description is fully adequate for agent understanding.

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 description essentially restates the schema parameter descriptions (e.g., deployment as 'owner/name', input as JSON object, download boolean, timeout optional with max wait). The description does not add significant new meaning beyond clarifying that the input shape matches the underlying model's expectations. Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool runs a prediction against a deployment's current release, and distinguishes it from siblings like replicate_run_model by noting the deployment context and the auto-download behavior. The verb 'run' and resource 'deployment' are specific and 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 description explains that the tool blocks until completion and auto-downloads outputs, mirroring the curated generate_* tools. It also notes that a timeout parameter can return a pending result for polling via replicate_get_prediction. However, it does not explicitly state when to use this tool versus alternatives like replicate_run_model, which may be confusing in some contexts.

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 openWorldHint=true and destructiveHint=false. Description adds value by explaining timeout behavior (returns prediction ID if exceeded), download default and effect, and that it runs any model. No contradictions.

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?

Description is lengthy but well-structured with sections, workflow, examples, and display requirement. Every sentence serves a purpose. Minor redundancy could be trimmed, but overall it is organized and front-loaded with key points.

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 generality (no output schema), the description thoroughly covers return value (PredictionResult), behavior for images, timeout handling, and workflow. It is complete enough for an agent to use correctly without additional cues.

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 baseline 3. Description adds rich context: for model parameter explains format and version pinning; for input recommends using get_model_schema first; for download explains when to use false; for timeout explains max and behavior. This significantly aids correct invocation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool runs 'ANY model' in the Replicate catalog by identifier, distinguishing it from curated specialized tools. It provides a specific verb and resource, and directly differentiates from siblings by listing categories without curated 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?

Explicit guidance on when to use (categories without curated tool) and when not to use (prefer curated tools). Recommends a workflow: search, get schema, then run. Provides examples of appropriate use cases and clear alternatives.

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 indicate readOnlyHint=true, destructiveHint=false, and idempotentHint=true, which are consistent. The description adds transparency by stating the return limit and structure, enhancing understanding beyond 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 concise, well-structured with clear sections (summary, args, returns, tip), and includes a practical usage tip 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 low parameter count, high schema coverage, and rich annotations, the description completes the picture with return structure details and workflow guidance, fully informing agent 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 100% with a single parameter described with min/max length and examples. The description reinforces these constraints and provides additional examples, adding value 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?

The description clearly states it searches the Replicate catalog by free-text query, returning up to 25 matching models. It uses a specific verb and resource, distinguishing it from sibling tools like replicate_get_model_schema and replicate_recommend_model.

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 a tip suggesting to call replicate_get_model_schema after finding a promising model, guiding the agent on proper workflow. However, it does not explicitly state when not to use this tool.

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?

Discloses async behavior with timeout and polling fallback via replicate_get_prediction, and display requirement for embedding results. Annotations are consistent (non-read-only, not idempotent, not destructive).

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?

Concise with front-loaded purpose and structured args. Slight redundancy with schema but minimal waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers key aspects: mask result display, timeout/async behavior, model selection. No output schema, but description explains how to use the result. Adequate for complexity level.

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?

Adds meaning beyond schema: explains prompt required for grounded-sam, model curated options, extra_input for SAM tuning. Doesn't enrich download parameter, but overall compensates for schema coverage (83%).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool produces segmentation masks, distinguishes between SAM 2 (point/box-prompt) and Grounded-SAM (text-prompt), and differentiates from sibling tools like replicate_generate_image or replicate_inpaint.

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?

Provides clear usage context: when to use SAM 2 vs Grounded-SAM, and that prompt is required for Grounded-SAM. Lacks explicit exclusions or when-not-to-use 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 indicate readOnlyHint=false and destructiveHint=false, which are consistent with the description (creates a prediction, not destructive). The description adds details on return format (PredictionResult with text_output), timeout behavior (returns prediction ID if exceeded), and model options, providing context beyond 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 concise and front-loaded with the main purpose. It lists parameters clearly without redundancy. It could be slightly more structured (e.g., separating description from args), but it is efficient and focused.

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 complexity (7 parameters, nested objects, no output schema), the description covers the return type, timeout behavior, and optional parameters. It does not specify file size limits or supported formats, but these are likely implied by the Replicate platform.

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 86% schema coverage, the baseline is 3. The description adds value by explaining the model parameter (curated keys vs 'owner/name'), the translate_to_english boolean, and the extra_input object for model-specific options, which supplements the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Transcribe an audio or video file to text using Whisper-family models on Replicate', with a specific verb (transcribe) and resource (audio/video to text). It distinguishes itself from sibling tools like replicate_generate_speech (text-to-speech) and replicate_vision (image processing).

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 transcription tasks and explains parameters like model selection, language hint, and translation. However, it does not explicitly mention when not to use this tool (e.g., for real-time transcription) or compare to alternatives like replicate_run_model with custom models.

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

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

With no annotations providing hints, the description fully covers behavioral traits: the file upload operation, the ~24-hour URL expiry, the two input modes and their constraints, and limitations of base64 mode for certain clients. No contradictions with annotations are present.

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 well-structured with clear sections, bullet points, and examples. Every sentence adds value, and key information is front-loaded. It is appropriately sized for a tool with multiple modes and parameters.

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 adequately explains the return format (structuredContent with url, file_id, name) and the URL expiry. It covers all necessary details for an agent to select and invoke the tool correctly, including examples.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema provides 100% parameter coverage with descriptions. The description adds further meaning: explains exclusivity of file_path and base64_data, auto-detection of mime_type, and the behavior of file_name. It clarifies edge cases and usage patterns 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?

The description clearly states the specific verb and resource: 'upload a file to Replicate's file storage and get back a URL'. It distinguishes this tool from its siblings, which are about predictions, models, and other operations.

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 explains when to use file_path vs base64_data, and notes that base64 is for callers with bytes in memory. It provides examples and clarifies the returned URL's usage. However, it lacks explicit when-not-to-use guidance or alternatives, though contextually it is clear.

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 are sparse, but description adds important behaviors: display requirement, URL expiry, download option, timeout handling, and model-specific defaults.

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?

Well-structured with sections and examples, though slightly verbose. Purpose is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers inputs well but lacks detail on output structure (PredictionResult) and error handling. No output schema exists.

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?

Description adds meaning beyond schema by explaining default scale values per model, extra_input example, and timeout behavior. Schema coverage is 67%, so description compensates.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool upsamples images with optional face restoration, distinguishing it from sibling tools like replicate_generate_image or replicate_inpaint.

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?

Examples provide use cases but no explicit guidance on when to choose this over other image tools like replicate_inpaint or replicate_remove_background.

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 indicate readOnlyHint=false (mutation) and openWorldHint=true (non-standard output). The description adds context: it returns PredictionResult with text_output, explains timeout behavior (default 5 min, polling via replicate_get_prediction), and mentions model-specific extras. No contradiction with 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 front-loaded with purpose and uses a clear Args/Returns/Examples structure. Every sentence adds value. Minor wasted space: could combine the two bullet sentences under Args. Overall efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers core functionality, return type, examples, and key parameters. It lacks mention of error handling, auth requirements, or cost information. However, for a 7-param tool with no output schema, it is reasonably 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?

Despite 57% schema coverage, the description adds significant meaning beyond the schema. It clarifies the model field (curated keys vs 'owner/name'), gives examples, explains the prompt default, specifies max_tokens range, and details timeout with polling fallback. This greatly aids correct invocation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Run a vision-language model to describe, caption, or answer questions about an image.' The title reinforces 'Analyse / caption an image with a vision model.' This distinguishes it from sibling tools like replicate_generate_image (generation) and replicate_remove_background (editing), making the specific verb+resource obvious.

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 clear context, including optional prompt, default model, and examples for usage. It implies the tool is for understanding images, not generating them. However, it does not explicitly state when not to use it (e.g., for image editing or audio tasks) or compare with alternatives like replicate_chat for text-only queries.

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