Reelfy Agent Tools

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

Annotations indicate a non-readonly, non-destructive, open-world operation. The description adds valuable behavioral context: cost per call (~$0.08) and return format (PNG URLs), which go beyond the 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?

The description is a single, front-loaded sentence that conveys purpose, model, cost, and output format without any redundant text.

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 two parameters and an output schema, the description provides essential operational details (cost, output type) and is fully sufficient for an 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?

The input schema covers both parameters with clear descriptions (e.g., 'What to draw', aspect ratio options). The tool description does not add additional parameter meaning, so the baseline of 3 applies.

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

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

Description clearly states the tool generates an AI image, identifies the specific model (Google Nano Banana 2), and indicates the output (PNG URLs). It is immediately distinguishable from sibling generation tools like ai_video or ai_music.

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 use for image generation scenarios with its explicit verb 'Generate' and the resource type. While it does not name alternatives or exclusions, the context is sufficiently clear for an agent to select it over 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 indicate non-read-only and non-destructive behavior. The description adds cost (~$0.08) and model details, but does not disclose further behavioral aspects like generation time, content policy, or rate limits. It provides some context but not comprehensive transparency.

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

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

The description is a single sentence that efficiently conveys the core purpose, model, style, and cost. It is front-loaded with the main action and contains no filler. This is exemplary conciseness.

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 two parameters and an output schema, the description covers the essential context: model, style, and cost. It could explicitly mention output format or usage constraints, but the output schema likely handles return values. The description is adequate and appropriately scoped.

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 fully describes both parameters with allowed values and defaults, and the description contributes no additional parameter information. Since schema coverage is 100%, the baseline score of 3 applies. The description does not enrich understanding of the 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 action ('Generate an AI image') and identifies the specific resource ('Flux 2') with unique attributes (Black Forest Labs, photorealism). This distinguishes it from sibling image tools like ai_image_gpt or ai_image_pro by naming the exact model. The purpose is unambiguous.

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

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

The description implies usage through the 'photorealism' style and cost (~$0.08), but it does not explicitly state when to choose this tool over siblings or provide exclusion criteria. There is no mention of alternatives or use cases, leaving guidance implicit rather than explicit.

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; description adds monetary cost (~$0.07) and output format (PNG URLs), which are valuable 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?

Single, efficient sentence that front-loads the action and packs cost and return format with no 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?

Simple tool with full schema and output schema; description covers action, model, cost, and return format, sufficient for selection and invocation.

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 100% of parameters with clear descriptions; the tool description doesn't add parameter-specific details, hence 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?

Description specifies exact action (Generate), resource (AI image), and model (OpenAI GPT Image 2), differentiating from sibling tools like ai_image_flux and ai_image_pro.

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 alternatives are mentioned; only via model name can an agent infer usage. The cost info is present but not framed as a comparative criterion.

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, so the safety profile is established. The description adds valuable behavioral context by disclosing the approximate cost (~$0.18) and the 2K resolution, which are consequences an agent should know. It does not describe every detail, but with annotations present, this is sufficient.

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

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

The description is a single, compact sentence that packs model, resolution, use case, and cost into a scannable format. Every element earns its place, with no redundancy or 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?

Given the tool's simplicity (2 parameters, output schema present, annotations provided), the description covers the essential aspects: what it does, when to use it, and cost. It does not describe return values, but the output schema handles that, so no significant 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?

Schema description coverage is 100%, with both 'prompt' and 'aspect_ratio' already well-documented in the input schema. The description does not add any parameter-specific semantics, so the baseline score of 3 is appropriate because the schema handles the heavy lifting.

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

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

The description uses the specific verb 'Generate' and identifies the resource as a 'hi-res 2K image' with the model 'Nano Banana Pro'. It clearly distinguishes from sibling image tools by naming the model and focusing on 'text-in-image & infographics', making the purpose unmistakable.

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 by stating it is 'best for text-in-image & infographics', which guides when to choose this tool. It does not explicitly list exclusions or compare against sibling tools, but the use-case signal is strong enough for effective selection.

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 (which only indicate non-read-only and open-world interaction), the description discloses meaningful operational detail: cost (~$0.15), runtime (1-3 minutes), model version (Suno V4.5), and output specifics (2 track variants + cover art). This gives the agent essential expectations for invocation and side effects.

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

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

A single, front-loaded sentence begins with the core action and includes key constraints (cost, time) and outputs. Every word contributes value; no redundancy or 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?

The description covers critical operational aspects (cost, duration, output details) and benefits from an output schema for return values. It does not mention prerequisites or async behavior, but for a generation tool with such clear scope, it is nearly 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 description coverage is 100%, with both 'prompt' and 'instrumental' already documented in the input schema. The tool description does not add any parameter-specific meaning beyond what the schema provides, 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?

Description states a specific verb 'Generate' targeting 'full song with vocals', with concrete deliverables ('2 track variants + cover art') and model (Suno V4.5). This clearly distinguishes it from sibling tools like ai_image, ai_video, and ai_voice, making its purpose unmistakable.

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 use for music generation but does not explicitly mention when to use it versus alternatives (e.g., ai_voice) or state exclusions. No contrasting guidance is provided, so usage context is only implicit rather than explicit.

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 (readOnlyHint=false, destructiveHint=false), the description discloses cost (~$0.50), generation time (1-4 minutes), and the specific model/audio feature. This adds useful behavioral context that annotations do not provide.

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

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

The description is a single, front-loaded sentence followed by two brief fragments. It conveys the essential action and key constraints without unnecessary words, earning every character.

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 an output schema exists, return values are already covered. The description covers the tool's purpose, duration, audio feature, cost, and latency, which is sufficient for a two-parameter generation tool with good schema and annotation coverage.

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

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

The input schema has 100% coverage, documenting both 'prompt' and 'aspect_ratio' with descriptions. The description itself does not add parameter-level meaning, so the baseline 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 uses a specific verb ('Generate') and clearly identifies the resource ('8-second AI video clip with native audio'). It also distinguishes itself from sibling tools like ai_image and ai_music by specifying the video output and model (Google Veo 3.1 Fast).

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 this tool is for creating AI video clips, but it does not explicitly say when to use it over alternatives or provide exclusions. Context from sibling tool names helps, but the description itself lacks explicit usage guidance.

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

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

Beyond the annotations (readOnlyHint: false, openWorldHint: true), the description adds valuable behavioral information: cost (~$0.60) and time (1-3 minutes). These are important for an agent to set expectations and decide whether to invoke the tool, and they do not contradict any annotations.

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

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

The description is a single sentence with three short pieces of information: purpose, cost, and time. It is front-loaded and every phrase delivers value with no redundancy or 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 relatively simple tool with two parameters and an output schema, the description covers the essential details: model, duration, audio absence, cost, and latency. Combined with the schema’s parameter descriptions and output schema, 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?

The input schema already provides 100% coverage with descriptive parameter documentation for 'prompt' and 'aspect_ratio'. The description doesn't add additional parameter meaning, so it meets the baseline but doesn't exceed it.

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

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

The description clearly states the tool's function: 'Generate a 5-second video with Kling 2.6 (no audio).' It specifies the exact verb, resource, model, duration, and audio constraint, which distinguishes it from sibling video tools like ai_video and ai_video_seedance.

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 for use—5-second videos, no audio, cost, and latency—allowing an agent to infer appropriate scenarios. However, it does not explicitly mention alternatives or when-not-to-use, so it falls 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?

The description adds important behavioral context beyond annotations, including exact duration/resolution, absence of audio, and cost (~$0.35). It is consistent with annotations (not read-only, not destructive) and gives the agent a clear expectation of what the tool produces and at what price.

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

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

A single, information-dense sentence includes all key specifications (duration, resolution, model, audio, speed, cost). No filler, and the most important details are 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?

For a straightforward generation tool with an output schema and simple parameters, the description covers all necessary context: what is generated, key specifications, and cost. Nothing essential 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?

The input schema already provides helpful descriptions for both parameters (prompt scene description and aspect ratio values). The description itself adds no parameter-specific meaning beyond what the schema offers, so the baseline of 3 applies.

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

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

The description clearly states the tool generates a video with specific characteristics (5-second, 720p, ByteDance Seedance 2.5), distinguishing it from sibling tools like ai_video and ai_video_kling. The verb 'generate' and resource 'video' 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 implies usage context—'no audio, fast & cheap' suggests it is ideal for quick, low-cost, silent video generation. It provides clear context but does not explicitly name alternatives or when-not-to-use scenarios, which would have earned a 5.

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

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

Annotations are present (readOnlyHint=false, destructiveHint=false), and the description adds useful behavioral context: it mentions the use of Claude (external AI) and the approximate cost (~$0.05). This goes beyond what annotations alone communicate. 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?

The description is a single, tightly written sentence that front-loads the action and resource, then lists specific use cases and cost. Every element adds value with 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 image-analysis tool with an output schema available, the description adequately covers purpose, use cases, and cost. It is complete enough for an agent to select and invoke the tool correctly.

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

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

Schema description coverage is 100%, with both parameters (q, image) clearly documented. The description does not add extra detail beyond the schema, so an average score is appropriate as the schema carries the semantic load.

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

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

The description clearly states the tool's action ('Analyze an image') with specific capabilities (OCR, chart reading, screenshot explanation) and distinguishes it from sibling tools like ai_music or ai_video by focusing on image analysis. The cost mention adds practical context.

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 clearly implies when to use this tool (for image analysis tasks) and lists concrete examples. However, it does not explicitly mention alternatives or exclusion criteria, though the sibling set makes the choice evident.

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

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

Annotations already indicate the tool is not read-only, not destructive, and can have open-world side effects. The description adds value beyond annotations by disclosing the cost (~$0.05), the character limit (1000), the model (ElevenLabs Turbo 2.5), and language support (32), giving the agent useful behavioral expectations for a paid, state-changing call.

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

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

The description is a single, tightly packed sentence that front-loads the core purpose and includes essential operational details (format, model, languages, limit, cost). No filler or 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?

For a relatively simple tool with an output schema and clear annotations, the description covers the essential decision-making information: purpose, output format, constraints, and cost. It does not explicitly state when not to use it, but the description is otherwise complete enough for an agent to select and invoke the tool 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?

The schema already covers both parameters (text and voice) with descriptions, achieving 100% schema coverage. The tool description adds context such as the character limit, which indirectly informs the text parameter, but it does not substantially enhance the schema's parameter documentation.

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

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

The description clearly states the tool's function with a specific verb and resource: 'Text-to-speech voiceover MP3'. It also provides distinctive details like the ElevenLabs model and language count, effectively distinguishing it from sibling tools such as ai_music and ai_image.

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-to-speech tasks but does not explicitly state when to use this tool over alternatives, nor does it mention any exclusions or alternate tool recommendations. The context from sibling names suggests differentiation, but the description itself lacks explicit guidance.

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

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

Annotations already indicate readOnlyHint=false and destructiveHint=false, so the description does not need to cover all safety aspects. The description adds value by disclosing the cost ('~$0.03'), which is a behavioral trait. However, it does not mention that the question is sent to an external service (Claude LLM) or any rate limits, but this is somewhat implicit and the annotation coverage sets a moderate bar.

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

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

The description is a single, compact sentence: 'Ask Claude LLM any question, concise answer. ~$0.03.' Every word earns its place, no redundancy or fluff. It immediately conveys the tool's function and key cost detail, making it highly scannable.

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

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

For a tool with one parameter and an output schema, the description adequately covers the basics: what it does and cost. It does not need to explain return values because an output schema exists. While it lacks deeper context (e.g., input length limits, privacy), the tool's simplicity and existing annotations make this acceptable, though not fully comprehensive.

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% description coverage for the single parameter 'q' ('Your question'), so the schema already fully documents the parameter. The description's 'any question' loosely overlaps with the schema but does not add new semantic detail beyond what is already provided. 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 states a clear purpose: 'Ask Claude LLM any question, concise answer.' It specifies the verb ('Ask'), the resource ('Claude LLM'), and the nature of the output ('concise answer'). However, it does not explicitly distinguish from the sibling tool 'ask_ai_pro', which likely offers a more advanced or expensive variant, so it falls short of full differentiation.

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

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

The description provides no explicit guidance on when to use this tool versus alternatives. It does not mention use cases, exclusions, or trade-offs compared to siblings like 'ask_ai_pro' or other AI tools. The cost hint (~$0.03) implies a budget-conscious use case, but this is not stated as a directive.

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 already indicating readOnlyHint=false and destructiveHint=false, the description adds valuable context: the cost (~$0.05) and the model strength. This goes beyond annotations to inform about cost and capability, though it does not disclose latency or response 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 a single, front-loaded sentence with no wasted words. It efficiently conveys the tool's core purpose and cost.

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 one-parameter tool with annotations and an output schema present, the description covers the key aspects. It could improve by explicitly comparing to ask_ai, but overall it is sufficiently complete for the tool's simplicity.

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 q described as 'Hard question'. The description additionally clarifies the expected question type (complex analysis, code, reasoning), which adds semantic meaning beyond the schema's minimal 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 the tool asks the strongest Claude Opus model for complex analysis, code, and reasoning. This distinguishes it from the sibling ask_ai and other media tools by specifying the premium model and task complexity.

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 complex tasks ('complex analysis, code, reasoning'), but does not explicitly mention alternatives like ask_ai or state when not to use this tool. Context is clear but lacks explicit exclusions compared to a 5.

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

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

Annotations already provide readOnly/destructive safety hints. The description adds valuable behavioral context beyond those: the response is 'concise' and the operation costs '~$0.03', which helps an agent anticipate output style and expense. 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 a single, front-loaded sentence with no filler. It packs the core purpose, model version, output character, and cost into minimal words, making it easy to parse quickly.

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 one-parameter query tool with an output schema, the description is sufficiently complete: it states what to ask, which model, the response style, and cost. No additional behavioral or return-value details are needed given the low complexity and existing schema/annotations.

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 single parameter 'q' is fully documented in the schema as 'Your question', so the schema carries the semantic weight. The description does not add additional meaning about parameter syntax, accepted formats, or length constraints beyond what the schema already provides.

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

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

The description uses a specific verb ('Ask') with a clearly identified resource ('Google Gemini 3.1 Pro') and defines the output style ('concise answer'). This differentiates it from sibling tools like ask_gpt and ask_grok by naming the exact model variant and adding a cost signal.

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 using this tool when you need Google Gemini, but it does not explicitly state when to prefer it over alternatives or provide exclusions. The context is clear from the model name, but no direct comparison or selection guidance is given.

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

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

The description adds a key behavioral detail beyond the annotations: the operation carries a monetary cost (~$0.03). It also clarifies that the response will be concise. It does not contradict the annotations (readOnlyHint false, openWorldHint true, destructiveHint false), though it could mention rate limits or side effects.

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

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

The description is a single, front-loaded sentence with no filler; every clause ('ask', model, concise answer, cost) 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?

For a one-parameter tool with an output schema present, the description is complete: it identifies the model, the input type, the response style, and the cost. There is no missing structural information that would prevent correct invocation.

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 100% schema description coverage and a single 'q' parameter already described as 'Your question', the tool description adds little new semantic detail beyond reinforcing that the question can be anything. 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 uses a specific verb ('Ask') with a clear resource ('OpenAI GPT-5.2') and scope ('any question'), and notes the response format ('concise answer'). The model name differentiates it from sibling ask_* tools, making the purpose unmistakable.

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

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

No guidance is provided on when to choose this over sibling tools like ask_ai, ask_gemini, or ask_grok; it only states generic 'any question' usage. Without exclusions or alternative references, an agent must guess based on the model name alone.

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

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

The description adds behavioral context beyond the annotations by noting the approximate cost (~$0.03), speed (fast), and tone (witty). It aligns with openWorldHint=true by implying the question is sent to xAI's external service. No contradictions with annotations exist.

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?

At just one sentence, the description is extremely concise and front-loaded, starting with the primary action 'Ask xAI Grok 4.6'. The rest of the sentence adds differentiating and cost info without fluff. Every word contributes value.

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 single-parameter tool with an output schema and annotations, this description is sufficient. The output format is presumably in the output schema, and the safety profile is covered by annotations. The cost and personality hints round out the contextual 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?

The schema already covers the only parameter 'q' with 'Your question' at 100% coverage, so the description doesn't need to add much. The description implicitly defines what the question is by stating 'Ask xAI Grok', but offers no additional constraints or syntax. This meets the baseline for schema-heavy parameter documentation.

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

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

The description clearly identifies the tool as an interface to xAI Grok 4.6, using 'Ask' as the verb and the model as the resource. The phrase 'alternative take' distinguishes it from sibling AI tools like ask_gpt and ask_gemini, making its niche obvious. It is specific and not a tautology.

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

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

It gives contextual guidance by highlighting 'fast, witty alternative take', implying use when a quick, personality-driven answer is desired. However, it does not explicitly mention when not to use it or name alternatives, so there is room for more guidance. This is a clear context but lacks exclusions.

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

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

The description adds "paid per call," which is a critical behavioral trait beyond the annotations (readOnlyHint=false, openWorldHint=true, destructiveHint=false). This cost warning helps the agent weigh usage. The phrase "call ANY endpoint" aligns with openWorldHint and implicitly warns of arbitrary side effects, though it doesn't detail specifics. 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 two sentences: the first states the core function and cost; the second provides a crucial prerequisite. Every clause earns its place, with no filler. It is front-loaded with the verb and resource, making it immediately scannable.

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

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

Given the tool's open-world nature and generic purpose, the description provides key context: it points to list_endpoints for discovery, mentions cost, and signals that any path is accepted. The presence of an output schema covers return values, so the description doesn't need to. It could add more about authentication or error handling, but the current guidance is sufficient for safe operation.

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 already covers 100% of parameters with descriptions: path and query. The description echoes this by saying "by path with custom query parameters," but adds no additional semantic nuance beyond what the schema provides. The baseline of 3 is appropriate since the schema carries the load.

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

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

The description clearly states the tool's action ("Call ANY Reelfy endpoint by path") and adds an important qualifier ("ANY") that distinguishes it from sibling tools like list_endpoints, which merely discover endpoints. It also mentions the ability to pass custom query parameters, making the purpose unmistakable.

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 usage guidance: "Use list_endpoints first to discover paths and parameters." This not only tells the agent when to use this tool but also establishes a prerequisite, reducing misuse. It implies call_endpoint is for executing endpoints after discovery, which distinguishes it from dedicated endpoint tools in the 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?

The description presents a read-only price lookup, but the annotations set readOnlyHint=false, implying the tool might have side effects. This directly contradicts the description's clear read-only nature. The description also does not disclose any other behavioral aspects (e.g., data freshness, rate limits).

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

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

The description is a single, information-dense sentence that includes the tool's purpose, key data fields, source, and cost. It is well-front-loaded and contains 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?

For a simple tool with one optional parameter and an output schema, the description covers the essential context: source, data type, and cost. It lacks caveats about external API behavior or limits, but the simplicity of the tool makes this acceptable.

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 covers 100% of parameters, with the 'ids' parameter fully documented as a comma-separated list with a default. The description adds no additional parameter-level detail beyond what the schema already provides, so the baseline score of 3 applies.

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

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

The description clearly states the tool provides live crypto prices with specific fields (USD, 24h change, market cap) and names the source (CoinGecko). It is specific about the resource but does not explicitly differentiate it from sibling tools like market_snapshot, which could also provide market data.

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

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

The description offers no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. While it is a simple tool, the lack of any usage context leaves the agent to infer when it is appropriate.

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

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

Annotations declare readOnlyHint=false, but the description describes an inherently read-only WHOIS/RDAP query, creating an annotation contradiction. The description adds only cost info, omitting caveats like data redaction or availability issues.

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 one sentence, front-loads the core purpose, lists concrete data points, and includes the cost. Every word earns its place; 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?

For a simple one-parameter tool with an output schema, the description covers the essential behavior and cost. However, the readOnly mismatch and lack of context about WHOIS availability/privacy redactions keep it from being fully 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 has 100% coverage of the single 'domain' parameter with an example, so the description adds no additional parameter meaning. Baseline 3 is appropriate since the structured data already fully documents the parameter.

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

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

The description clearly states it provides a WHOIS/RDAP dossier with specific fields (registrar, creation/expiry, status, nameservers), which distinguishes it from siblings like web_search or scrape_page. However, it lacks an explicit verb like 'retrieve' or 'lookup', so it's clear but not maximally 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 implies usage for domain registration lookups and mentions the cost, giving some context, but does not explicitly state when to use this versus alternatives, nor does it provide exclusions or prerequisites.

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

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

Annotation Contradiction: The description says 'List' and 'free, no payment' indicating a read-only, side-effect-free operation, but annotations set readOnlyHint=false, implying potential state changes. This is a serious inconsistency. The description does not add meaningful behavioral context beyond the annotations.

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

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

One sentence with zero fluff, front-loaded with the action and resource. Every word carries meaning, including the free/payment note.

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

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

The tool is simple, has an output schema, and the description covers purpose and cost. It is complete enough for an agent to select and invoke it correctly, though it could have briefly mentioned that it precedes calling call_endpoint.

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

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

The tool has 0 parameters, so the schema covers everything. The description does not need to explain parameter semantics, and the baseline of 4 applies. It adds no misleading parameter information.

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 the tool lists ALL Reelfy paid endpoints, includes live prices and example URLs, and is free/no-payment. This clearly distinguishes it from siblings like call_endpoint, which invokes endpoints rather than enumerating 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?

The description clearly communicates that this tool is the free way to see all paid endpoints with prices and URLs, making its use case obvious. However, it does not explicitly mention when not to use it or name the alternative (call_endpoint), though the context is enough to infer.

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 only indicate readOnlyHint=false, destructiveHint=false, and openWorldHint=true, leaving the description to carry some burden. It adds useful cost information ('~$0.02') and the aggregation of multiple data types, but does not disclose potential side effects or behavior when ids are omitted. No contradiction with annotations, but could offer more behavioral detail.

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

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

The description is a single, front-loaded sentence that immediately states the tool's purpose and contents, followed by cost information. Every word adds value, making it highly concise and appropriately structured.

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

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

Given the simple one-parameter schema with 100% coverage and the existence of an output schema, the description sufficiently covers the snapshot's content and cost. It lacks only explicit usage guidance relative to sibling tools, but overall it is complete for a tool of this complexity.

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

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

The input schema covers 100% of the parameter (ids) with a description, so the baseline is 3. The tool description does not add any additional semantic context for the parameter beyond what the schema already provides, but it does imply the parameter controls which coins are included by listing prices as part of the snapshot.

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 provides a crypto market snapshot with specific components (prices, fear/greed, trending, BTC fees), using a clear imperative style. It distinguishes itself from the sibling crypto_prices by offering a broader multi-part snapshot in one call.

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

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

The phrase 'in one call' implies this is for efficiently gathering a comprehensive market overview, but it does not explicitly state when to use this tool over alternatives like crypto_prices or web_search. The intended use case is inferred from the listed contents rather than explicitly guided.

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=false (write operation), openWorldHint=true (accesses external URLs), and destructiveHint=false. The description adds valuable context: the cost (~$0.10) and the output property (transparent result). It doesn't disclose potential failure modes, but with annotation coverage, this is sufficient.

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

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

The description is a single, front-loaded sentence that conveys the function, output, and cost without any filler. Every word contributes value, exemplifying ideal conciseness.

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

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

Given the tool's simplicity (one parameter, output schema available), the description is mostly complete. It could mention that the image must be publicly accessible, but that is already covered in the schema. The cost and transparent result are included, and the output schema handles return value details.

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

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

Schema description coverage is 100%: the 'image' parameter is described as 'Public URL of the image'. The description does not add additional meaning beyond that, so it relies on the schema. This meets the baseline for high 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?

The description uses a specific verb ('Remove') and resource ('image'), clearly stating the output is a transparent result. It distinguishes itself from sibling tools like ai_image or ai_vision by focusing on background removal, a unique 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?

The description provides clear context: use this tool when you need to remove an image background and get a transparent result. It doesn't explicitly name alternatives or exclusions, but none of the sibling tools offer this specific capability, so the usage intent is unambiguous.

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 provide readOnlyHint=false and openWorldHint=true, indicating live web interactions. The description adds value by disclosing the cost (~$0.10) and the specific steps (3 web searches + brief), which goes beyond annotation hints. It does not contradict annotations and provides meaningful 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?

The description is a single, information-dense sentence. It quickly conveys the core action, the output, and the cost, with no filler or redundant details. Every word 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?

The tool is simple (1 parameter) and has an output schema, so the description need not explain return values in detail. It covers the process, output type, and cost. Minor gaps include no mention of expected duration or potential failure modes, but overall it is sufficiently complete for the tool's complexity.

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

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

Schema coverage is 100% for the only parameter 'q', with description 'Research topic'. The tool description adds no additional detail about the parameter beyond what the schema already provides, so a 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 what the tool does: performs 3 live web searches and generates a Claude-written brief with citations and source links. It distinguishes itself from sibling tools like web_search (which just searches) by specifying the multi-step research and report generation process.

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 comprehensive research tasks but does not explicitly state when to use it over alternatives (e.g., web_search for quick lookups, ask_ai for direct questions). No exclusions or alternative tool names are mentioned, leaving the agent to infer appropriate usage from the description alone.

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

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

Annotations already provide readOnlyHint, openWorldHint, and destructiveHint. The description adds the cost (~$0.005) and the 'clean content' behavior, which is useful, but it does not disclose potential side effects, failure modes, or limitations (e.g., handling of JavaScript-heavy pages). It adds some context beyond annotations but not extensive.

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

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

The description is a single concise sentence that packs in the action, resource, outputs, and cost. No filler or redundancy. It is optimally sized for a simple tool.

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

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

The tool is simple with one parameter and an output schema exists, so the description need not explain return values. It adequately covers the core functionality, though it could mention what happens on errors or the meaning of 'clean' content. Overall, it's sufficient for a tool of this complexity.

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

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

Schema coverage is 100% for the single 'url' parameter, which has its own description ('Full page URL'). The tool description does not add any additional semantic detail about the parameter, so it receives the baseline score for 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?

The description uses a specific verb ('Fetch') and clearly states the resource (web page) and outputs (title, headings, text, links). This clearly distinguishes it from sibling tools like web_search (search) and call_endpoint (raw API calls), making its purpose immediately clear.

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

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

The description implies the use case: when you need clean content from a specific URL. However, it does not explicitly state when to prefer this over alternatives or mention any exclusions. The context is clear, just not fully explicit about 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 already indicate openWorldHint=true and destructiveHint=false, but the description adds valuable specifics: the tool performs a live search (not cached), returns a short AI-generated answer, and has an approximate cost of $0.005. These details go beyond the annotations and help the agent anticipate behavior and cost. No contradiction with annotations is 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 a single, compact sentence that front-loads the core purpose and includes essential extras (freshness, AI answer, cost) without any redundant words. Every element 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?

For a simple one-parameter tool with an output schema, the description covers purpose, result nature, and cost, which is sufficient for basic selection and invocation. It lacks any mention of result count or format limitations, but these are not critical given the output schema handles return details.

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% coverage for parameter q with the description 'Search query'. The tool description does not add any further meaning or usage details about the parameter, so it does not enhance the schema's clarity. A baseline score of 3 is appropriate given the 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?

The description clearly states 'Live web search' with the added detail of 'fresh results + short AI answer', which makes the tool's purpose immediately obvious and distinguishes it from siblings like ask_ai or research_report. Although the verb 'search' is implied rather than explicit, the resource (web) is clearly identified.

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

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

The phrase 'fresh results' implies this tool is for time-sensitive or up-to-date information, but there is no explicit guidance on when to use this tool versus alternatives, nor are any exclusions or preconditions mentioned. The usage context is therefore only implied, not stated.

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