Agent Data Tools: AI

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

Adds substantial context beyond the annotations: exact pricing ($0.002/call), payment paths (x402 USDC or card key via /credits), the failed-calls-free policy, and a rationale for readOnlyHint=false (each call is charged). This explains an unusual annotation value the agent would otherwise have to guess at.

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

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

Three dense sentences, front-loaded with what the tool does before pricing and the read-only explanation. No filler; every clause carries information.

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

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

For a stateless chat-completion call with full schema coverage and no output schema, the description covers capability, model, use cases, and billing-why-not-read-only. Nothing an agent needs to invoke it correctly is missing.

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

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

Schema coverage is 100%, so the schema already documents all four parameters with limits and defaults. The description restates optional system prompt, 1,024-token ceiling, and adjustable temperature but adds no syntax or format detail beyond the schema, so the baseline 3 applies.

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

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

States a specific verb+resource (send a prompt, get an answer) and pins the exact model (Llama 3.1 8B Instruct) versus the task-specific siblings like ai_summarize_text or ai_classify. An agent can distinguish the generic chat call from specialized tools immediately.

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 lists use cases (drafting, reasoning steps, Q&A, any quick LLM call) and the 'without an AI provider account' condition, which routes agents here rather than to a hosted provider. It stops short of naming when to prefer a sibling task tool instead.

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 context the annotations cannot carry: per-call price ($0.002 via x402 USDC or card key at /credits), the fact that failed calls are free, and an explicit reconciliation of readOnlyHint=false ('not marked read-only because each call is charged'). That last point directly explains an annotation that would otherwise look odd for a compute-only tool.

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

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

Three sentences, front-loaded with the capability, then use cases, then cost/behavior. Every sentence carries distinct information; nothing is padding.

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

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

With no output schema, the description must describe the return value, and it does (best label, short reason, ranking). Cost, auth path, and the read-only rationale are all present, so an agent has everything needed to call and budget for it.

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

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

Schema description coverage is 100% and the description only restates the label-count constraint (2-20) already in the schema. The return-value detail (best label, short reason, ranking) is valuable but belongs to output description, not parameter semantics, so the baseline 3 applies.

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

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

States a specific technique and resource ('Zero-shot classification') with the exact input shape (text + 2-20 labels) and the exact return shape (best label, short reason, ranking). The named use cases (routing tickets, tagging content, intent detection, moderation queues) separate it from siblings like ai_sentiment and ai_keywords without the agent opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete when-to-use contexts ('Use for routing tickets, tagging content, intent detection and moderation queues'), which is stronger than implied usage. It stops short of naming an alternative or stating when NOT to use it (e.g., when a fixed sentiment scale suffices, use ai_sentiment).

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?

Goes well beyond the annotations: it discloses per-call pricing ($0.002), the payment paths (x402 USDC or card key via /credits), that failed calls are free, and — most valuable — explicitly rationalizes readOnlyHint=false ("not marked read-only because each call is charged"). With no output schema, it also supplies the return shape (1,024-dim vector) and the 4,000-character ceiling.

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

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

Front-loaded with the technical facts an agent needs (dimension, length limit, model, use cases) before the commercial details. Dense semicolon-packed prose with a couple of parentheticals, but no sentence is filler.

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

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

For a paid, single-parameter, no-output-schema tool, it covers the essentials: what comes back, input bounds, cost, billing mechanism, and failure billing. The only real gap is guidance on when to prefer a sibling task tool over an embedding call.

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

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

Schema description coverage is 100% for the single required `text` parameter, so the schema already carries the semantics; the description's only addition is an illustrative example value. That is the baseline case for a fully documented schema, no more.

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

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

States a specific verb and resource (produce a 1,024-dimension embedding vector for text), names the underlying model (BAAI BGE-M3), and quantifies the input limit and language coverage. It is clearly separable from every sibling (ai_classify, ai_translate, ai_summarize_text) without opening any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly names four use cases (semantic search, RAG retrieval, clustering, deduplication), which tells the agent when this tool applies. It stops short of the top mark because it names no alternatives or exclusions, e.g. routing classification or sentiment work to ai_classify/ai_sentiment instead.

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

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

Annotations already declare readOnlyHint=false and destructiveHint=false, and the description earns credit by explaining the counterintuitive flag ('not marked read-only because each call is charged'), plus disclosing pricing ($0.002/call via x402 USDC or card key) and that failed calls are free. It adds return behavior (null for missing fields) but omits rate limits or error semantics.

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

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

Front-loaded with the core action and output shape; each sentence carries distinct information (capability, use case, pricing, side-effect profile). Slightly dense with billing detail, but no filler.

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

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

With no output schema and only two params, the description supplies the missing pieces an agent needs: the JSON-object return with nulls for absent fields, the billing model, and the non-read-only rationale. Nothing material is left unexplained.

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% (text, fields with length/range constraints documented), so the baseline is 3. The description adds nothing about parameter syntax beyond what the schema states.

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?

Specific verb+resource: 'Pull named fields out of unstructured text ... and get them back as a JSON object.' It bounds the input domain with examples (emails, invoices, listings, bios) and the output shape, clearly distinguishing it from siblings like ai_summarize_text or ai_classify.

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?

'Use to turn messy text into structured data for an agent' gives an implied context, but no explicit when-not-to-use and no named alternative among the many sibling AI tools (e.g., ai_classify for categorization). Usage is inferable but not routed.

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

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

Adds real context beyond the annotations: the pricing model ($0.002/call via x402 USDC or card key), that failed calls are free, and it explicitly reconciles the readOnlyHint=false annotation ('Reads or computes only; not marked read-only because each call is charged'). This explains a behavioral trait an agent would otherwise find confusing. It does not cover rate limits or return shape, so not a 5.

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

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

Three short sentences, front-loaded with the output description, then use cases, then behavior/pricing. Every sentence carries information; the pricing clause is dense but justifies its place by explaining the readOnlyHint=false.

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 2-parameter tool with no output schema, the description covers the output nature (ordered keywords), use cases, and the cost/behavior profile. It is nearly complete; only explicit sibling routing and return format detail are absent.

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

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

Schema description coverage is 100%, so the schema already documents both 'text' (12,000 char limit) and 'count' (3-30, default 10). The description adds the output ordering ('most relevant first') but no parameter-specific meaning beyond what the schema provides, matching the 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?

States a specific resource and output: 'The most important keywords and key phrases of a text, most relevant first,' which clearly distinguishes keyword extraction from siblings like ai_summarize_text or ai_classify. It does not explicitly name an alternative sibling, but the resource itself 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?

Gives concrete use cases ('tagging, SEO, search indexing and topic detection') that imply when to reach for this tool over siblings. It stops short of naming a specific alternative or an exclusion condition, so it is clear context but not full when/when-not 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?

Adds real behavioral context beyond annotations: pricing ($0.002/call via x402 USDC or card key), that failed calls are free, and an explicit rationale for readOnlyHint=false (each call is charged) despite the operation only reading/computing. That directly explains an annotation that would otherwise look inconsistent. Return format is described only loosely (Markdown outline), which keeps it from a 5.

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

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

Front-loads the deliverable and format in the first sentence, then usage, example, and billing. Efficient overall, though the pricing sentence is dense and shifts register abruptly at the end.

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

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

With no output schema, two well-documented parameters, and annotations covering safety, the description supplies what an agent needs: what it returns, when to use it, cost, and failure behavior. Only the sibling-selection boundary is left implicit.

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

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

Schema description coverage is 100%, so both parameters are already documented, and the baseline is 3. The description's example (text=..., depth=2) illustrates the parameters in use but adds no semantics beyond the schema's own 'heading levels 1-3, default 2'.

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

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

States a specific verb and resource (produce a structured Markdown outline) and names both input modes (a text you send, or a topic), plus the exact output format (headings and bullet points). It does not differentiate itself from close siblings such as ai_summarize_text or ai_rewrite, so an agent must infer the boundary.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete usage contexts (planning articles, reports, talks, documentation) and a worked example with parameter values. It stops short of stating when NOT to use it or which sibling to pick instead for summarization or rewriting.

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 merely flag readOnlyHint=false, openWorldHint=true and non-idempotent; the description goes further by explaining that it is not read-only because each call is charged, quoting the price ($0.002/call via x402 USDC or card key), and noting failed calls are free. That is exactly the kind of cost/auth context annotations cannot convey.

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

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

Front-loaded with the core action and styles, followed by use cases, an example, then billing notes. All sentences carry information, though the pricing/auth detail is dense enough that it slightly stretches the single paragraph.

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

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

No output schema exists, yet the rewrite result is implied and the description covers input constraints, style selection, billing, and safety posture. It omits explicit mention of the returned rewritten text and the 8,000-character limit (which the schema carries), so it is complete enough without being exhaustive.

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 already documents both fields including the default, so the baseline is 3. The description adds marginal value by restating the style options and demonstrating them in the example (style=professional), which clarifies intended usage rather than pure 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?

States a specific verb (rewrite) and resource (text) plus the enumerated target styles, and the 'keeping the meaning and language' clause distinguishes it from ai_translate and ai_summarize_text in the sibling set. An agent can identify the operation without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete use cases (emails, replies, product copy, messages) and a worked example showing text/style pairing. No explicit when-not or named alternatives, so it falls short of a 5, but the 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?

The description resolves a subtle annotation puzzle: readOnlyHint=false with destructiveHint=false is otherwise confusing, and it explains why ('Reads or computes only; not marked read-only because each call is charged'). It also discloses pricing ($0.002/call), payment/auth paths (x402 USDC or a card key at /credits), and the failed-call refund policy — real behavioral context the annotations do not carry.

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

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

Front-loads what the tool returns, then usage, example, and cost. Dense but every clause carries information; the example sentence is slightly long relative to the rest, keeping it from a 5.

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

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

With no output schema, the description still states the return shape (polarity plus confidence score), the input domain and model, and the billing/failure semantics. An agent has everything needed to call it and interpret the result.

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

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

Schema coverage is 100% and the single 'text' parameter is already documented with its 4,000-character limit, so the schema does the heavy lifting. The description adds only a worked example, which is marginally useful but not new semantic detail.

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?

Names a specific verb+resource ('Positive or negative sentiment of English text with a confidence score') and even identifies the underlying model (DistilBERT fine-tuned on SST-2), so the agent knows exactly what it gets back. It does not explicitly distinguish itself from the closest sibling, ai_classify, which is the only missing element for a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

'Use to triage reviews, support messages, social posts and replies' gives concrete contexts that make selection obvious. There are no explicit when-not conditions and no named alternative (e.g. ai_classify for custom label sets), so it stops short of a 5.

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

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

Goes well beyond the annotations: discloses the $0.003/call price, that failed calls are free, the two payment paths (x402 USDC or card key via /credits), the base64 return shape, and explicitly reconciles readOnlyHint=false ('not marked read-only because each call is charged'). This is exactly the extra context annotations cannot carry.

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

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

Front-loads the core capability, then example, then pricing/auth. Dense but nearly every clause earns its place; the pricing/payment sentence is long, though it carries real decision-relevant 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?

No output schema exists, yet the description explains the return format (base64 audio plus content type), the language options, cost, and billing behavior. An agent has everything needed to call this correctly and predict the outcome.

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

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

Schema description coverage is 100%, so the schema already documents both 'text' and the 'lang' enum. The description restates the 1,000-character limit and language list and adds a worked example (text=..., lang=en), which is marginal but not new semantic depth.

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

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

States a concrete verb and resource ('turn ... text into natural speech as an audio file') plus the output encoding (base64 with content type) and supported languages. An agent can distinguish this from the inverse sibling 'transcribe' without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete use cases ('voice replies, audio notes, accessibility and phone or chat bots') and an inline example, so the agent knows when it fits. It does not, however, name an alternative or an explicit when-not-to-use condition (e.g., use transcribe for the reverse direction).

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?

Goes well beyond annotations, which only give generic readOnly/destructive/idempotent hints. It discloses the price and payment rails ($0.002/call via x402 USDC or card key), that failed calls are free, and even reconciles the unusual readOnlyHint=false by explaining that calls are charged — precisely the kind of context annotations cannot convey.

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

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

Three tight sentences that front-load purpose before routing and pricing. The pricing/payment detail is somewhat dense but earns its place given it explains the annotation quirk; nothing is filler.

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

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

No output schema exists, yet the description specifies the return shape (sentences plus bullet points), the billing and failure semantics, and the sibling alternative. Nothing an agent needs to select or invoke this correctly is missing.

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

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

Schema coverage is 100% and both parameters are already documented there, so the baseline is 3. The description restates the 12,000-character limit but adds nothing about the 'sentences' length parameter beyond what the schema says.

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

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

States a specific verb (summarize), resource (text), input limit (12,000 chars), and output shape (few sentences plus bullet key points). It also distinguishes itself from the URL-based '/summarize' variant, so an agent can tell the two apart without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly names the intended inputs (long emails, documents, transcripts, tool outputs) and routes the alternative case away with 'for a web page by URL, use /summarize'. Both when-to-use and the sibling boundary are stated, not implied.

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?

Goes well beyond the annotations: discloses the model, pricing ($0.002/call), payment paths (x402 USDC or card key via /credits), and that failed calls are free. Crucially it preemptively explains the apparent conflict with the annotations, stating the tool computes only but is not marked read-only because each call is charged.

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

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

Front-loaded with purpose, then parameters, then cost, then the read-only caveat — a sensible ordering with no filler. The pricing sentence is acronym-dense (x402 USDC, /credits) but each clause carries decision-relevant information, so it earns its space.

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 3-parameter tool with no output schema, the description covers purpose, parameter format, cost, and the annotation rationale. The only omission is any hint about the return shape or handling of long input, though the 4,000-character limit is already in the schema.

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

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

Schema coverage is 100% so the baseline is 3, but the description adds real value by listing concrete ISO codes (en, sv, de, fr, es, zh, ja) and clarifying that source language is optional and defaults to en. This reduces format-guessing errors beyond the schema's single 'e.g. sv'.

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

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

States a specific verb and resource ('Translate text between about 100 languages') and names the underlying model (M2M100 1.2B), which pins down capability and limits. Translation is clearly separable from siblings like ai_summarize_text, ai_rewrite, and ai_chat.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives a concrete use context ('localise messages and documents') plus a worked example invocation, so an agent knows when to reach for it. It stops short of naming alternatives or stating when not to use it (e.g. very long documents, needing style control).

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?

Explicitly reconciles the surprising readOnlyHint=false annotation ('Reads or computes only; not marked read-only because each call is charged'), which is exactly the kind of disclosure annotations cannot convey. It also discloses pricing, payment paths, and that failed calls are free — rich operational 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?

Three dense sentences, front-loaded with capability then usage then cost/auth. Every clause carries information, though the x402/card-key pricing detail is verbose relative to selection needs.

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

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

With no output schema, the description still conveys the return shape ('detailed description including any visible text'), the constraints, and the billing behavior, so an agent can invoke it confidently. Only minor detail on failure modes is absent.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds value beyond it: it restates the accepted formats and 3 MB ceiling and explains the question parameter's effect ('ask something specific') versus the default detailed 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?

Opens with a specific verb+resource framing — 'Vision model for an image at a public URL' — and enumerates the two modes (detailed description or targeted question). This clearly separates it from text-oriented siblings like ai_summarize_text or ai_translate without naming them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete use cases: 'alt text, OCR-style reading of screenshots, chart reading and content checks.' It states when to reach for this tool but does not name an alternative sibling or a when-not-to-use condition, so it falls short of a full 5.

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

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

Annotations already declare readOnlyHint=false, openWorldHint=true, idempotentHint=false and destructiveHint=false, but the description adds substantial context beyond them: the return format (1024x1024 JPEG as base64), latency ('a few seconds'), pricing ($0.02/call via x402 USDC or card key), a failed-call-is-free guarantee, and it explicitly explains WHY readOnlyHint is false (each call is charged, despite reading/computing only). That last point is a genuinely valuable reconciliation of behavior 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?

Front-loaded with what it is, then return shape, then use cases, then the parameter nuance and pricing. Dense but every sentence carries distinct information; the only slight excess is the parenthetical pricing detail, which is nonetheless operationally relevant.

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

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

With no output schema, the description takes on the burden of describing the return value (1024x1024 JPEG, base64) and does so, plus latency and cost. For a simple 2-parameter generation tool, nothing an agent needs to call it correctly is missing.

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

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

Schema coverage is 100%, so the baseline is 3 and both parameters are already documented in the schema. The description goes further by explaining the tradeoff behind 'steps' ('trades speed for detail') and including a worked example (prompt + steps=4), which adds interpretation rather than restating the range.

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

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

States a specific verb+resource ('Text-to-image generation with FLUX.1 schnell') naming both the operation and the underlying model. This cleanly distinguishes it from every sibling (ai_chat, ai_speech, transcribe, describe, ai_embed), none of which produce images.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete use cases ('illustrations, thumbnails, mockups, avatars and social media images'), which tells the agent when this is the right pick. However, it never names an alternative or states when not to use it (e.g. editing an existing image), so it stops short of full routing guidance.

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

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

With annotations present the bar is lower, yet this description goes further: it discloses pricing ($0.02/call, failed calls free), the 5 MB cap, accepted formats, and return contents. Critically it explains the non-obvious readOnlyHint=false with 'Reads or computes only; not marked read-only because each call is charged', resolving an inference the agent would otherwise have to guess at.

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

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

Purpose is front-loaded, followed by returns, constraints, use cases, an example, and the pricing/annotation note. Every sentence carries distinct information (model, formats, cost, return shape) with no repetition of the schema or title.

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

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

For a one-parameter tool with no output schema, the description fully covers what it returns (transcript text, detected language, duration, word count), the input constraints, cost, and the unusual annotation stance. Nothing an agent needs to call it correctly is missing.

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

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

Schema coverage is 100% for the single url parameter, so baseline is 3, and the description adds value beyond the schema by listing accepted codecs and restating the size cap, plus a concrete example URL showing the expected form. It lacks detail on URL reachability/auth expectations, keeping it out of the top band.

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

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

The description gives a specific verb and resource ('Transcribe speech from an audio file at a public URL') and even names the underlying model (Whisper large-v3-turbo), so the agent knows exactly what the tool produces. It does not, however, differentiate itself from the sibling ai_speech, which an agent could reasonably confuse with transcription, so it stops short of the top band.

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?

'Use for voice notes, podcast clips, meeting snippets and voice messages' gives concrete, immediately actionable context for when this tool applies. There is no explicit when-not or named alternative (e.g. ai_speech vs transcribe), which is what separates a 4 from a 5.

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