Bright Data MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Every tool has a clearly distinct target and action, from smart_scrape/unlocker_scrape (with explicit cost/fallback differences) to the large web_data_* family, where each tool is bound to a specific platform and entity. Even with 57 tools, there is no real ambiguity because descriptions precisely delimit the use case.
Naming Consistency3/5
Naming is consistent within families: web_data_* for structured data, smart_* for cost-aware fetching, unlocker_* for paid requests, and web_scraper_* for datasets. However, the overall set mixes these prefixes with standalone names like serp_search, browser_scrape, parse_feed, check_robots, and replay_har, so no single predictable convention governs the whole server.
Tool Count2/5
At 57 tools, the server far exceeds the typical well-scoped 3-15 tool range. While each endpoint offers distinct value, the sheer volume makes selection harder and suggests the server is a sprawling catalog rather than a focused toolkit, potentially overwhelming agents.
Completeness5/5
The server covers the entire scraping lifecycle: free vs paid fetch, async jobs, batching, crawling, search, structured data for dozens of major sites, diff/extract, robots checking, and HAR replay. Paired trigger/get-result tools ensure no dead ends, and the domain appears fully addressed.
Average 3.3/5 across 57 of 57 tools scored. Lowest: 1.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 6 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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{
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  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description gives no behavioral information such as side effects, rate limits, authentication requirements, or return format. The one-line noun phrase does not disclose any operational traits, shifting the full burden onto the description, which it completely fails to meet.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (three words) but is under-specified rather than concise. It lacks essential information such as an action verb, usage context, or example. The fragment-like structure does not earn its place because it fails to communicate purpose or behavior, making it ineffective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
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 no output schema, the description is completely inadequate. It does not explain what structured data is returned, how the URL should be formatted, or what the expected behavior is. The description is nothing more than a label, leaving the agent without sufficient context to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already fully describes the single parameter 'url' with its type and description, achieving 100% schema coverage. Since the schema covers everything, the baseline is 3; the description adds no extra meaning beyond what is already in the schema, so the baseline is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Structured Instagram reel data' is a noun phrase that lacks an explicit action verb. It does not clearly state what the tool does (e.g., fetch, extract, or scrape), and it largely restates the tool name 'Instagram reels' without adding functional clarity. It is not a tautology, but it is vague and fails to differentiate from sibling tools beyond the name itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives such as web_data_instagram_posts or web_data_instagram_profiles. The description provides no context, exclusions, or mention of suitable use cases, leaving the agent without any criteria for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. The description says nothing about what the tool does beyond returning 'structured data'—no mention of read-only nature, rate limits, authentication requirements, or output specifics. This provides zero transparency into the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short fragment, but it is under-specified rather than appropriately concise. It omits essential information, so the brevity is not a virtue. The structure is just a noun phrase, not a clear instructional sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that the tool is a scraper-like utility for a specific website, the description is severely incomplete. It does not explain what fields are extracted, whether it expects a specific listing URL, or what the return format is. With no output schema and no behavioral notes, the agent lacks sufficient context to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with a single parameter 'url' clearly described as 'Facebook Marketplace listing URL'. The description adds nothing about the parameter, but the schema already provides adequate semantics, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Structured Facebook Marketplace listing data' is essentially a restatement of the title 'Facebook Marketplace listings' with the addition of 'Structured' and 'data'. It does not state a specific verb or action (e.g., 'Retrieve' or 'Scrape'), so the tool's purpose is implied rather than explicit. This is close to a tautology and does not distinguish itself from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With a large list of sibling web_data_* tools for different platforms, the lack of any context, prerequisites, or alternative mentions leaves the agent without direction on selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does not state whether the operation is read-only, whether authentication is needed, what the output format is, or any constraints like rate limits. The phrase 'structured data' hints at output but is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, but it is under-specified rather than appropriately concise. It reads as a fragment rather than a complete tool description and does not earn its place by conveying essential information about the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 no annotations and no output schema, the description still fails to explain what the tool returns or how it behaves. The text 'Structured Facebook post data' is not enough for an agent to understand the tool's capabilities or limitations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides full coverage for the single parameter 'url' with a description 'Facebook post URL' (100% coverage). The description adds no additional meaning beyond what the schema provides, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Structured Facebook post data' identifies the resource (Facebook posts) but lacks a verb indicating what the tool does (fetch, scrape, search, etc.). It adds the adjective 'structured' but does not distinguish it from sibling tools like web_data_facebook_marketplace or other web_data tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. It does not mention any context, prerequisites, or comparisons to sibling tools like smart_scrape or other social media scrapers.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only offers a generic statement about 'structured data' and does not mention whether the operation is read-only, whether the URL must be publicly accessible, rate limits, or what the response contains beyond being structured. This is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short but under-specified; it is a fragment rather than a complete sentence. While it is not verbose, it sacrifices meaning for brevity, making it less helpful than a slightly longer, more explicit description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is inadequate. It gives no indication of the return structure, possible errors, or other behavioral context. The tool's simplicity does not excuse such sparse information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully documents the single 'url' parameter with the description 'LinkedIn job listing URL'. Since schema coverage is 100%, the description adds no additional parameter semantics, but per the baseline for high coverage, a score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Structured LinkedIn job listing data' is a noun phrase that essentially restates the title and tool name without providing an action verb like 'retrieve' or 'scrape'. It vaguely indicates the resource type but fails to state what the tool actually does with the given URL.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool relative to other LinkedIn data tools (e.g., person profile, posts) or any prerequisites. The description only states what the data is, not when to invoke it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only says 'structured data' with no disclosure of behavior, output format, authentication needs, rate limits, or what happens on invalid URLs. This is essentially no transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (five words) but under-specified. It is not concise in an effective way—it omits essential information and reads more like a fragment than a functional description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema or annotations, the description must explain what 'structured data' means (fields, format, etc.), but it does not. The tool's purpose, return value, and behavior are all unclear, leaving the agent without sufficient context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (the single 'url' parameter is described as 'LinkedIn post URL'). The tool description adds no additional meaning beyond the schema, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Structured LinkedIn post/article data' is a noun phrase without a verb, so it does not clearly state what the tool does (fetch, scrape, retrieve, etc.). It vaguely indicates the resource (LinkedIn posts/articles) but does not distinguish from siblings like job listings or profiles.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. No context, prerequisites, or comparison with sibling tools like web_data_linkedin_person_profile or web_data_linkedin_company_profile is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure, but it offers nothing. It does not mention authentication needs, rate limits, pagination, response structure, whether replies are included, or any other behavioral trait. The word 'structured' vaguely hints at output format but without specifics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short and front-loaded, but its brevity comes at the cost of substance. While there is no wasted content, the single phrase barely expands on the title and does not earn its place by adding meaningful information. It is concise but under-specified.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the schema (one parameter), the tool is not complex, but the description still fails to provide essential context: what the output looks like, whether pagination is required, or what fields are returned. For a data-fetching tool, this absence of output details leaves significant ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter `url` is already fully described in the schema as 'Instagram post URL,' providing 100% coverage. The description adds no extra meaning about URL format, constraints, or how the parameter should be used, 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.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Structured Instagram comments for a post' identifies the resource (Instagram comments) and scope (a post), but lacks an explicit verb like 'retrieve' or 'scrape' that would clearly define the action. It is not a tautology, as it adds 'structured' and 'for a post,' but the purpose remains somewhat vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like web_data_tiktok_comments or web_data_instagram_posts. There are no explicit scenarios, exclusions, or conditions that help the agent decide between this and sibling tools. The only implied use is evident from the name and schema, not from the description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so the description carries the full burden of disclosing behavioral traits. It mentions the URL format requirement but does not disclose what happens with invalid URLs, whether the tool performs web scraping, any rate limits, or what the response looks like. This is a significant gap for a data retrieval tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short but the first sentence 'Structured Walmart product data' largely restates the title, wasting a sentence. The second sentence provides the /ip/ requirement. While there is no fluff, the redundancy and lack of substantive content make it less effective than a concise sentence that combines purpose and constraint.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 no output schema, but the description does not explain what 'structured data' includes or any behavioral expectations. Given missing annotations and output schema, the description should provide more detail about the data returned and any limitations. It is insufficient for an agent to know what to expect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already covers the only parameter with full detail ('Walmart product URL (must contain /ip/)'). The description repeats this requirement without adding new semantics, so it adds no value beyond the schema. Baseline 3 is appropriate given 100% schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Structured Walmart product data' indicates the tool provides data about a Walmart product, but it is vague and does not specify what 'structured data' entails or how it differs from sibling tools like web_data_walmart_seller or web_data_amazon_product. It adds the /ip/ URL requirement, which gives some specificity but not enough to distinguish it from similar tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description only states a technical precondition ('Requires a product URL containing /ip/'). It provides no guidance on when to prefer this tool over alternatives, such as web_data_walmart_seller for seller data or web_data_amazon_product for Amazon products. No exclusions or context for appropriate use are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden of behavioral disclosure. It only states that output is 'structured' and scoped to 'seller profile data', but does not mention how the tool fetches data, what authentication or access requirements exist, what failure modes occur, or what fields are included.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, short, front-loaded sentence with no filler words. It is concise and readable, though the brevity borders on under-specification; that concern is more relevant to completeness than to conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description has a higher burden to explain what the agent will receive or need to know. It only gives a minimal indication of 'structured data' without listing typical fields, return format, or any operational context. This is insufficient for a data extraction tool, even a simple one.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully covers the single 'url' parameter with a description 'Walmart seller URL', so schema coverage is 100%. The tool description adds the context of 'seller profile data' but does not add meaningful parameter-level details beyond the schema, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the resource (Walmart seller profile data) and implies data retrieval, but it is a noun phrase rather than a specific verb+resource statement. It does not explicitly say 'get' or 'extract', and while it distinguishes from the sibling Walmart product tool by mentioning 'seller', it does not clearly differentiate from other web_data tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives like smart_scrape or web_data_walmart_product. No conditions, exclusions, or recommended use cases are provided, leaving the agent to infer usage entirely from the tool name and title.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only says 'Structured', hinting at output format, but discloses nothing about pagination, rate limits, authentication, error behavior, or what happens if the URL is invalid. This is minimal 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one short sentence with no filler, and the key term 'Glassdoor employee reviews' appears early. It is very concise, though it sacrifices completeness for brevity. This is not under-specification to the point of being a tautology, but it is close to the edge.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has a simple input schema and no output schema, so the description must explain return values and behavior. It only says 'Structured Glassdoor employee reviews', lacking details like whether reviews include ratings, dates, or pagination. For a data-extraction tool, this is incomplete context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes the `url` parameter as 'Glassdoor company URL' with 100% coverage. The description's phrase 'for a company' adds little beyond that. Since the schema fully documents the parameter, a baseline of 3 is appropriate; no extra semantic value is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource as 'Structured Glassdoor employee reviews for a company', distinguishing it from sibling tool `web_data_glassdoor_company` which likely returns company profiles. However, it lacks an explicit verb like 'get' or 'fetch', making it more of a noun phrase than an action statement.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention `web_data_glassdoor_company` or other review-related tools, nor does it state any prerequisites or exclusions. The agent is left to infer usage from the name and one-line description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full burden of behavioral disclosure. It only lists output content, without mentioning whether it is read-only, how data is retrieved, rate limits, error behavior, or any 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that is concise and to the point. It could arguably include more detail, but it has no wasted words and is well-structured for a simple tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has low complexity (one parameter), but the description omits usage guidance and behavioral context. It lists key output fields but does not explain return format or limitations, leaving gaps that would matter to an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description covers 100% of the single parameter (package_name), and the description adds no extra meaning beyond what the schema already provides. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource as PyPI package data and enumerates the data components (latest version, README, dependencies, metadata). It distinguishes itself from sibling tools by naming PyPI specifically, though it lacks an explicit verb to state the action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to use this tool versus alternatives like web_data_npm_package or other data tools. There are no exclusions, prerequisites, or context to help an agent choose it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full transparency burden. It mentions 'structured data' but does not disclose what fields are returned, whether pagination exists, or any limitations. This lack of detail about return format or behavior is a significant gap for a data-fetching tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, extremely concise and without wasted words. It is appropriately sized for a simple one-parameter tool, though it borders on under-specification.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, yet the description does not explain what 'structured' data includes—such as comment text, author, likes, or pagination. This leaves agents uncertain about the return value, making the description incomplete for effective decision-making.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes the only parameter 'url' as 'TikTok video URL', and the description adds no additional meaning beyond 'for a video'. Since schema description coverage is 100%, the baseline of 3 applies without extra contribution from the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Structured TikTok comment data for a video' clearly indicates the tool returns comment data for a TikTok video, using a specific resource and scope. It distinguishes itself from siblings like web_data_tiktok_posts and web_data_tiktok_profiles, though it lacks an explicit verb like 'retrieve'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description only states what the tool does, with no mention of conditions, exclusions, or comparative tools, leaving the agent to infer usage solely from the tool name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full disclosure burden. It only lists output fields and does not disclose behavioral traits such as authentication requirements, rate limits, URL constraints, or error handling for invalid posts. This is minimal for a web scraping tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no redundancy, placing the key information at the start. It is efficient, but it omits some useful context that could be added without bloat, so it is not maximally informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple one-parameter schema and lack of annotations or output schema, the description provides a basic understanding of what the tool returns. However, it is incomplete because it does not mention edge cases, return format specifics, or when this tool is preferred over generic scrapers, which is important given the many sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes the url parameter as 'X/Twitter post URL', and with 100% schema coverage, the tool description adds no additional parameter semantics. It does not clarify URL format constraints or how the URL is used beyond what the schema states.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource (X/Twitter posts) and the output (structured data with text, likes, retweets, replies), making it distinct from sibling tools for other platforms. However, it lacks an explicit action verb like 'fetches' or 'retrieves', relying on the tool name and inferable meaning.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as smart_scrape or other web_data_* tools. The description does not mention any prerequisites, use cases, or exclusions, leaving the agent without clear direction on selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only states that the data is structured; it does not disclose pagination behavior, rate limits, authentication requirements, error cases, or whether all comments are returned. This is insufficient for a data retrieval tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is six words long with zero fluff, and it is front-loaded with the key concept ('structured YouTube comment data'). However, it is so terse that it sacrifices explanatory power, so it does not earn a 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema and no annotations, this description is too minimal. It does not describe the return format, whether comments are paginated, or any limitations. An agent would lack critical context for interpreting results or handling edge cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully documents the single parameter (url: YouTube video URL) with 100% schema description coverage. The description's mention of 'for a video' is consistent with the schema but adds no additional meaning beyond what is already present in the schema definition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as returning structured YouTube comment data for a video, distinguishing it from sibling tools like web_data_youtube_profiles and web_data_youtube_posts. However, it lacks an explicit verb such as 'retrieves' or 'fetches', relying instead on a noun phrase.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. The description simply states what the data is, without explaining the context or exclusions. No alternative tools are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only lists output fields and does not disclose how the tool operates, such as whether it fetches the URL, requires a valid Zillow listing, or has rate limits. This is a significant gap for a scraping tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the core output information without any filler. It is appropriately sized for a simple tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With only one parameter and no output schema or annotations, the description is minimally adequate but incomplete. It does not explain input requirements, potential failure modes, or the full structure of the returned data beyond a few fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides a description for the 'url' parameter with 100% coverage, so the baseline is 3. The description adds no additional meaning about the parameter or its expected format.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool outputs structured Zillow property data with specific fields (price, beds, baths, sqft, listing details), which distinguishes it from sibling tools. However, it lacks an explicit action verb like 'extracts' or 'retrieves', making it slightly less clear than ideal.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus the many sibling tools. It does not mention alternatives, exclusions, or any context for appropriate use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It lists the data fields returned but does not disclose any behaviors such as requiring authentication, handling invalid URLs, rate limits, or the output structure. The description is essentially a data label rather than a behavioral specification.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, efficiently front-loading the core purpose with no redundant words. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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, no output schema), the description provides the key data fields but lacks detail on the return structure or caveats. It is minimally complete for a straightforward data retrieval tool, but not enriched beyond the bare essentials.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already documents the 'url' parameter with 100% coverage. The description adds no additional parameter meaning beyond the schema, 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource (Apple App Store) and the type of data returned (rating, reviews, price, description), distinguishing it from siblings like web_data_google_play_store. However, it lacks a verb indicating an action, instead using a noun phrase, so it falls short of a fully specific verb+resource formulation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any conditions, exclusions, or alternative tools. The only implicit signal is the store name in the tool title/description, which is not explicit usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries full responsibility for behavioral disclosure. It mentions 'structured AI-generated insights,' hinting at the type of output, but it does not disclose important behavioral traits such as nondeterminism of AI responses, potential latency, data source limitations, or any 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that communicates the core action, target, and output type without fluff. Every word contributes meaningful information, and it is appropriately sized for a simple one-parameter tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description provides the basic purpose and expected deliverable. However, it lacks context about the return format, limitations, or distinctions from similar AI insight tools, leaving some gaps in the absence of annotations or output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has a single parameter 'query' with description 'Brand name or query topic,' and the tool description repeats the same phrase without adding new meaning. Schema coverage is 100%, so the schema fully documents the parameter, and the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action and resource: 'Query ChatGPT and get structured AI-generated insights about a brand or topic.' It identifies the tool's source (ChatGPT) and scope (brand or topic), which distinguishes it from most sibling web_data tools. However, it does not explicitly differentiate from the closely related siblings web_data_grok_ai_insights and web_data_perplexity_ai_insights.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool instead of alternatives such as web_data_grok_ai_insights or web_data_perplexity_ai_insights. The description only states what the tool does, not the context in which it should be preferred or any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden. It implies a read-only data retrieval operation but does not disclose any limitations, auth requirements, error behavior, or return format. For a scraping tool, this is a significant gap beyond the basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence listing core output fields with no filler. It is front-loaded and every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one parameter, no output schema), and the description gives a basic idea of output fields. However, it lacks details about expected URL format, output structure, or potential error conditions, making it minimally viable but not fully complete for an agent deciding to invoke it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage for the single 'url' parameter, and the description adds no extra meaning beyond what the schema already provides. The baseline of 3 applies since the schema fully documents the parameter; the description's mention of output fields does not clarify parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool returns structured eBay listing data with specific fields (price, condition, seller, bids), clearly identifying the resource and scope. It distinguishes from sibling tools like web_data_amazon_product by explicitly mentioning eBay, but lacks a strong action verb like 'retrieve' or 'get'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives. The description does not mention that it is for eBay product pages or contrast it with sibling tools such as web_data_walmart_product. Given the presence of many similar web_data_* siblings, this omission is notable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must carry the behavioral transparency burden. It only states that the data is 'structured' and lists some fields, but does not disclose how the tool behaves—whether it scrapes the page, requires special access, can fail on invalid URLs, or any limitations. This is a notable gap for a web data extraction tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, compact sentence that communicates the core domain and expected data fields without unnecessary words. It is front-loaded with the resource name and immediately informs the user of key content, earning full marks for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple one-parameter schema and lack of output schema, the description partially compensates by listing expected data fields (rating, downloads, reviews, description). However, it omits behavioral context such as how to invoke it, what constitutes a valid URL, and any edge cases or limitations. It is minimally viable but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% description coverage: the single parameter `url` is described as 'Google Play Store app URL'. The tool description adds no additional parameter-level semantics, staying at the baseline. The parenthetical fields describe output rather than input, so parameter understanding relies entirely on the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource (Google Play app data) and highlights key data fields (rating, downloads, reviews, description). It lacks an explicit verb like 'fetches' or 'scrapes', but the context and sibling names make the retrieval purpose clear. It distinguishes itself from siblings like web_data_apple_app_store by naming Google Play specifically.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives such as web_data_apple_app_store or general scraping tools. The only contextual signal is the tool name itself, which implies Google Play URLs but does not explicitly state selection criteria or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral disclosure. It only states the output data fields and does not indicate whether the operation is read-only, if there are rate limits, authentication requirements, or other behavioral constraints. This is a significant gap for a data-fetching tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one concise sentence that immediately conveys the tool's purpose. It is front-loaded with the key phrase 'Structured Instagram post data' and includes the essential fields without any fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool, the description provides a reasonable high-level summary of the output. However, since there is no output schema and no annotations, it does not fully specify the return structure or any limitations (e.g., public posts only). It is adequate but leaves several gaps that a more complete description could fill.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes the single 'url' parameter with 100% coverage, so the description need not repeat it. The description does not add any extra meaning about the parameter, such as URL format requirements or restrictions, leaving it at the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource as Instagram posts and specifies the data fields (caption, likes, comments, media). It distinguishes from sibling tools like web_data_instagram_reels or web_data_instagram_profiles by explicitly saying 'posts'. However, it lacks an explicit verb like 'fetch' or 'retrieve', which keeps it from being perfectly unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. No mention of use cases, exclusions, or relationships to sibling tools such as web_data_instagram_reels or web_data_instagram_comments.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must carry the burden of behavioral disclosure. It only states that data is 'structured' and lists fields; it does not explain how the data is obtained, whether it requires authentication, what happens with invalid URLs, or any rate limits or limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one short sentence that gets straight to the point, listing the key data fields. It is appropriately sized and front-loaded with the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with a single URL parameter and no output schema, the description provides the essential data fields but omits any behavioral context such as whether it performs a live scrape, any usage restrictions, or how the response is structured. Given the simplicity, it is minimally adequate but not thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully documents the URL parameter (100% coverage), achieving the baseline score of 3. The description does not add any additional meaning to the parameter, but the schema already suffices for a simple URL.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource (LinkedIn company profile) and the main fields returned (industry, size, description, specialties). It distinguishes itself from siblings like person_profile or job_listings by specifying 'company profile'. However, it lacks an explicit verb (e.g., 'retrieves'), relying on the noun phrase.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not mention when to use this tool compared to alternatives such as web_data_linkedin_person_profile or web_data_glassdoor_company. It provides no exclusions or context for selection beyond the implicit 'company profile' in the name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only lists the data fields (headline, experience, education, skills) and implies structured output, but does not disclose whether the tool performs a live scrape, handles private profiles, the format of the response, potential errors, or any access requirements. This is insufficient for a tool that may encounter varied LinkedIn profile states.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that is front-loaded with the key term 'Structured LinkedIn person profile data.' It lists the main fields concisely, contains no fluff, and every word adds value. It is an exemplar of efficient, lean tool description for a simple data-fetching tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no output schema, no annotations), the description provides the core information: what data is returned. However, it lacks context about potential pitfalls such as LinkedIn's login walls, rate limits, or how to handle invalid URLs. It is minimally viable but leaves gaps that could affect correct invocation in edge cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes the single parameter 'url' as 'LinkedIn person profile URL' with a uri format, giving 100% schema coverage. The description adds no extra parameter-level semantics (e.g., URL format requirements, public-profile constraints). Thus, it does not enhance what the schema already provides, but it also does not need to—the schema is clear. Baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states that the tool provides 'Structured LinkedIn person profile data' and lists specific fields (headline, experience, education, skills). This clearly indicates the tool's purpose and distinguishes it from sibling tools like web_data_linkedin_company_profile and web_data_linkedin_posts. However, it lacks an explicit verb such as 'fetches' or 'returns,' making it slightly less directive than a full action-oriented description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide any guidance on when to use this tool versus alternatives. It only describes the output content. The sibling tool names imply usage (e.g., person profile vs. company profile), but the description itself offers no explicit when-to-use or when-not-to-use instructions, leaving the agent to infer context solely from the tool name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden, but it only says 'query' and 'get insights', revealing no behavioral traits like rate limits, auth requirements, or output structure. It implies a read operation but doesn't disclose any constraints 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with the key action 'Query Perplexity', no filler. Appropriate length for a one-parameter tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-param query tool, the description gives source and purpose, but with no output schema or annotations it doesn't explain what 'structured insights' looks like or any limitations. It lacks usage differentiation but is adequate for basic selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema describes query as 'Brand name or query topic' and description repeats that concept. With 100% schema coverage, the description adds no extra parameter semantics beyond what schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description uses a specific verb 'Query' and names the resource 'Perplexity', with output 'structured AI-generated insights' about a brand or topic. This distinguishes it from sibling AI insight tools like ChatGPT/Grok. However, the nature of 'insights' is somewhat underspecified.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to prefer this over sibling tools such as web_data_chatgpt_ai_insights or web_data_grok_ai_insights. The description only states what it does, not when to use it or what alternatives exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so the description carries the full burden. It lists output fields but does not disclose behavioral aspects such as whether the tool performs a read-only fetch, error handling, rate limits, or any potential side effects. This lack of behavioral context is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence with no redundant words. It front-loads the core purpose and lists key output fields in parentheses, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter, the description conveys the core purpose and expected output fields. However, it lacks information about return format nuances, possible failure modes, or usage conditions, which would be helpful given no output schema and no annotations. It is minimally complete but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The sole parameter 'url' is fully described in the schema (Reuters article URL), and schema coverage is 100%. The description adds no extra parameter details beyond what the schema 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource (Reuters news articles) and the output (structured data with headline, body, author, publication date), making the tool's purpose unambiguous. However, it lacks an explicit verb like 'retrieve' or 'scrape', so it doesn't fully meet the 5-point criterion of 'specific verb + resource + scope'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or differentiate it from other web_data_* tools, so an agent receives no usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does not state whether the tool is read-only, what happens on invalid URLs, whether it handles pagination, or any rate limits. It only lists output fields, which is insufficient for the agent to anticipate side effects or error behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that directly states the resource and data fields. There is no wasted language or redundancy. It is appropriately sized for a simple data retrieval tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description adequately identifies the output but falls short on broader context. It does not specify return format, error handling, or usage scenarios. Given the lack of annotations and output schema, the description should provide more behavioral and usage context to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes the single `url` parameter as 'TikTok profile URL' with format URI, resulting in 100% schema description coverage. The tool description adds no additional meaning about the parameter (e.g., expected format, examples, or constraints), so it does not exceed the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource (TikTok profile) and the specific data fields returned (bio, followers, following, video count). It distinguishes from sibling tools like web_data_tiktok_posts or web_data_instagram_profiles by naming the platform and profile scope. However, it lacks an explicit action verb (e.g., 'gets' or 'retrieves'), relying on the title and context to imply data retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. It only states the output, leaving the agent to infer usage from the name and siblings. There is no mention of when a profile URL is needed or how this differs from web_data_tiktok_posts/comments.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only says 'Structured data', hinting at a clean output, but does not disclose error conditions, rate limits, authentication needs, or what happens for invalid/unavailable videos. This is a minimal disclosure for a data retrieval tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that includes the resource type and key data fields. It is front-loaded with the key term 'YouTube video data' and has no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 no output schema, the description provides a basic example of fields but is not fully complete. It could mention handling of live streams, age-restricted content, or pointers to sibling tools for other YouTube data types. The lack of an output schema means the description should more thoroughly describe the return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema is fully documented (100% coverage) with the url parameter described as 'YouTube video URL'. The description adds context about the type of data returned (views, likes, etc.) but does not add further meaning about the parameter itself. The schema already covers the semantics, 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Structured YouTube video data' and lists specific fields (views, likes, description, tags), which clearly identifies the tool's purpose as retrieving metadata for a YouTube video. It distinguishes from sibling tools like youtube_profiles and youtube_comments by focusing on 'video data', though it lacks an explicit verb like 'Get' or 'Fetch'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus alternatives. It does not mention that for profile data or comments one should use other tools, nor does it provide any context about prerequisites or scenarios. Usage is only implied by the resource type in the name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the type of data returned (subscribers, description, videos) but does not mention any limitations, rate limits, authentication needs, or whether the operation is read-only. There is no output schema to clarify return structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the key information: 'Structured YouTube channel data'. It lists a few example fields in parentheses, earning its place without unnecessary fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one parameter, no output schema, and no annotations, the description provides the basic purpose but leaves ambiguity around what 'videos' means (e.g., a list of video metadata vs. count) and lacks any usage context or limitations. It is adequate for a simple tool but misses opportunities to clarify expected output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage: the single 'url' parameter is described as 'YouTube channel URL'. The description adds no additional meaning beyond this, merely reiterating that the tool returns channel data. Since the schema already documents the parameter sufficiently, 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The tool name and description clearly identify the resource as YouTube channel data, listing example fields (subscribers, description, videos) that distinguish it from sibling tools like web_data_youtube_posts and web_data_youtube_comments. However, the description lacks an explicit verb like 'Retrieve' or 'Get', making the action slightly implicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention scenarios where channel profile data is needed or contrast with sibling tools like smart_scrape or web_data_youtube_posts. The differentiation relies solely on the tool name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'Returns first page,' which is a useful limitation, but it does not indicate whether the tool is read-only, what fields the structured results include, how results are ordered, or any Amazon-specific restrictions such as rate limits. This is a significant gap for a live web scraping tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that immediately states the core purpose and the key behavioral limitation (first page). It is concise, easy to parse, and every word contributes to the meaning without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotation, the description should compensate by explaining what a 'structured result' contains (e.g., product title, URL, price, rating). It only states 'Returns first page,' which leaves the agent without critical information about the response format and breadth of data. For a web scraper tool, this is incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents both parameters (keyword and domain) with defaults and descriptions. The description adds no additional meaning or context beyond what the schema 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides 'Structured Amazon search results for a keyword' with the scoped behavior of returning only the first page. This distinguishes it from the sibling tools web_data_amazon_product (specific product details) and web_data_amazon_product_reviews (reviews).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool versus alternatives like web_data_amazon_product or general search tools such as serp_search. It offers no context about suitable use cases or exclusions, leaving the agent to infer usage from the name and schema.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the output fields (title, body, upvotes, awards, subreddit), giving the agent a reasonable expectation of the return payload. However, it does not describe error behavior, rate limits, authentication needs, or any side effects, leaving gaps in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that quickly communicates the tool's core output. It is front-loaded and free of fluff, earning a good conciseness score. However, it could include a bit more context without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with only one parameter and no output schema, the description is minimally adequate. It states the purpose and basic output fields, but lacks usage context, alternatives, or constraints. The overall simplicity of the tool makes this acceptable, though not exemplary.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 100% of parameters (one 'url' field described as 'Reddit post URL'). The description does not add any additional meaning beyond the schema, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource (Reddit posts) and the data type returned (structured post data with specific fields). It distinguishes from sibling tools like web_data_reddit_comments by focusing on posts rather than comments. Lacks an explicit action verb, but the intent is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives. The description does not mention that web_data_reddit_comments exists for comment data, nor any exclusions or prerequisites. Usage is implied only by the tool name and resource type.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It mentions the output is 'structured data', hinting at a normalized format, but does not disclose behavior like handling invalid URLs, rate limits, or whether it scrapes live data. For a read-only lookup, this is acceptable but not rich. The lack of any caveats is a minor gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that names the resource and key data points. It is efficient and easy to scan, though it is terse and could have added usage context without bloating.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one parameter, no output schema), so the description is mostly adequate. It lists common data categories but does not guarantee the full return structure or mention any limitations. For a tool with no output schema, more detail would be beneficial, but the minimalism is not critically incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides 100% coverage for the single parameter (url) with a clear description. The text adds nothing about parameters themselves, but it implicitly relates the URL to the output data. Since schema coverage is high, baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool provides structured ZoomInfo company data, listing key fields (employees, revenue, technologies, contacts). This is clear and specific enough to infer it retrieves company profiles. It does not explicitly differentiate from siblings like LinkedIn or Crunchbase company profiles, but the name and data categories provide reasonable context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives. The description does not mention prerequisites, when it is appropriate (e.g., need ZoomInfo-specific data), or when to choose other company-data tools. The agent is left without explicit usage criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavior. It states the data is structured and the URL requirement, but provides no details about the content of the reviews (e.g., ratings, dates, metadata), pagination, or potential blocking/rate limits. This is a minimal disclosure for a read tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences that immediately state the tool's purpose and the key input constraint. Every word earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool, the description covers the basic purpose and input requirement. However, since there is no output schema, the agent gets no indication of what fields are included in the 'structured review data,' such as reviewer name, rating, date, or text, leaving some ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already covers the single parameter 'url' with a description identical to the tool description. The description adds no new meaning beyond reinforcing the /dp/ requirement, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as returning structured Amazon review data for a product, distinguishing it from sibling tools like web_data_amazon_product which handle product details. However, it lacks an explicit action verb (e.g., 'retrieves'), making it slightly less direct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies a required input condition (URL must contain /dp/), which is useful guidance. However, it does not explicitly state when to choose this tool over alternatives such as web_data_amazon_product or web_data_amazon_product_search, leaving the differentiation to the name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It does not mention whether the operation is read-only, requires authentication, or has rate limits. The word 'Structured' hints at output format but offers no transparency about side effects or access requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no redundant words. Every word adds meaning, making it concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description lists the expected data types (stars, forks, contributors, file content), which provides some completeness. However, it does not specify the exact response structure, error behavior, or whether file content is raw or structured, leaving gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema fully describes the only parameter `url` as 'GitHub repository or file URL', and the tool description repeats this without adding new meaning. With 100% schema coverage, the baseline is 3, and the description provides no additional parameter context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as providing structured GitHub repository or file data, listing specific data types (stars, forks, contributors, file content). It is distinct from sibling tools that target other platforms, though it lacks an explicit action verb like 'fetch' or 'retrieve'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for GitHub URLs and data, but it does not explicitly state when to use this tool over alternatives such as smart_scrape or other web_data tools. No exclusions or alternative recommendations are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the type of data returned but does not disclose prerequisites, output format details, error behavior, or limitations. For a data-fetching tool this is a notable gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that directly states what the tool returns. No filler or redundant information, and it is easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With one simple parameter and no output schema, the description provides a reasonable summary of the returned data, but it lacks usage guidance and any behavioral caveats. It is minimally adequate for a low-complexity tool but leaves room for improvement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single url parameter, and the schema already describes it as 'Glassdoor company URL'. The description does not add additional parameter meaning beyond the schema, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly indicates the tool returns a structured Glassdoor company profile with specific data points (rating, CEO approval, culture scores). It distinguishes from the sibling glassdoor_reviews tool by focusing on company profile data rather than reviews, though it lacks an explicit verb like 'get' or 'retrieve'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for company profile data via the parenthetical content, but it does not explicitly state when to use this tool over alternatives. The sibling glassdoor_reviews is not mentioned, so the guidance is only implicit based on the tool name and content.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'structured' output but does not disclose authentication needs, rate limits, pagination, or the depth/structure of the returned comment thread.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the core purpose. It is efficiently worded with no unnecessary filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 no output schema. The description is minimally adequate but leaves unclear what 'structured' means and what the response format will be, especially given the many sibling data tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes the url parameter clearly, and coverage is 100%, so the description does not need to add parameter detail. The description adds no extra semantics to the parameter beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides a structured Reddit comment thread for a post. It uses a specific resource ('comment thread') and distinguishes itself from sibling tools like web_data_reddit_posts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives or on exclusions. The intended use case is only implied by the tool name and description, with no explicit mention of when to prefer this over other comment-related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden of behavioral disclosure. It mentions 'Returns clean JSON' but does not explain caching behavior, free_only vs forced Bright Data options, error handling, or rate limits. The parameters use_cache, free_only, and force_bright_data suggest complex behavior that remains undisclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief and well-structured: it states what the tool does, lists outputs, and gives example use cases in two sentences. Every word contributes value, and no unnecessary repetition or padding exists.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description adequately outlines the output data types, which is helpful given no output schema. However, it lacks parameter behavior details, explicit alternative guidance, and any mention of error scenarios or edge cases. For a tool with 4 parameters and a large sibling set, more context is needed for full completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 25% (only use_cache has a description). The tool description does not explain free_only, force_bright_data, or provide additional meaning for url. It lists extractable data types but fails to compensate for the low schema coverage, leaving parameter semantics unclear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('extract') and resource ('structured data from a page'), explicitly listing JSON-LD, Open Graph, Twitter card meta, meta description/keywords, and title. This clearly distinguishes it from sibling tools like smart_scrape or web_scraper_trigger, which focus on raw HTML scraping.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives use cases ('useful for getting product info, article metadata, or any schema.org markup') which implies when to use it, but it does not explicitly contrast with alternatives or state when not to use it. With many closely related sibling tools, explicit exclusion guidance would be more helpful.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool returns structured data with specific fields, which is honest about its read-only nature, but it omits potential rate limits, authentication needs, or error behaviors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the key output fields. There is no filler or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema, the description gives sufficient context about the expected output (bio, followers, following, post count). It could mention return format or limitations, but the simplicity of the tool makes this adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already covers 100% of the parameter, with 'url' described as 'Instagram profile URL'. The tool description adds no extra meaning to the parameter beyond confirming its purpose, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as providing structured Instagram profile data, listing key fields (bio, followers, following, post count). This distinguishes it from sibling tools like Instagram posts/reels/comments, though it lacks an explicit verb such as 'Fetch' or 'Get'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It does not mention when to prefer this over Instagram posts/reels tools or other profile scrapers, nor any prerequisites like public profile visibility.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, and the description only lists output metrics without disclosing behaviors such as whether it performs a read-only fetch, any rate limits, or error conditions. It adds minimal transparency about the operation itself.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The single sentence is front-loaded with key information and contains no unnecessary words. It is appropriately sized for a simple data retrieval tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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 no output schema, the description provides the essential return value context. It lists the main metrics, which is sufficient for basic invocation, though it does not specify the exact output structure (e.g., object vs array) or provide usage examples.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 100% of parameters with 'url' described as 'TikTok video URL'. The description does not add further semantic detail about the parameter, 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns structured TikTok video data with specific metrics (views, likes, shares, comments). The verb is implied but the resource and scope are explicit, distinguishing it from sibling tools like web_data_tiktok_profiles and web_data_tiktok_comments.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description does not mention any exclusions or alternative tools, leaving the agent to infer usage solely from the tool name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It openly describes that the tool fetches the URL, caches versions, returns both versions, and that the first call stores a baseline. This goes beyond a simple verb phrase and gives insight into side effects (caching) and statefulness. It does not mention rate limits or authentication, but for a diff tool this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences with efficient, front-loaded content: the action, the return values, and the caching behavior. Every sentence earns its place, with no fluff or repetition. It is well-structured and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite a clear core explanation, the tool has 4 parameters with zero documentation in both schema and description, and no output schema or annotations. The description covers the high-level logic but omits parameter-specific guidance, which is essential for correct invocation. An agent would be left guessing what 'free_only' or 'force_bright_data' mean. This makes the description incomplete 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.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, and the description does not explain any of the four parameters (url, free_only, data_format, force_bright_data). Only 'url' is implied by 'Fetch a URL,' but the other parameters' purposes and effects remain completely undocumented. The description adds no semantic value beyond the schema types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Fetch a URL and compare it to the previously cached version.' This distinguishes it from sibling tools like smart_scrape or smart_crawl by emphasizing the change-detection aspect. It also specifies the return values, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the caching behavior ('First call always stores a baseline; subsequent calls detect changes'), which implies when to use the tool (for tracking page changes over time). However, it does not explicitly contrast it with alternatives or state when not to use it, leaving usage guidance implicit rather than direct.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description is the primary source of behavioral transparency. It discloses that the tool is asynchronous and returns a response_id that must be used later, which are key non-synchronous behaviors. However, it does not mention other important behaviors such as potential costs, rate limits, or any side effects. Given the lack of annotations, this is adequate but not rich, hence a 3.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is exceptionally concise: two sentences that front-load the action ('Start an asynchronous Web Unlocker request') and follow with the critical output information. There is no filler, and every sentence adds value by explaining the async nature and the subsequent retrieval step.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While the description clearly outlines the async lifecycle, it is incomplete for selection and invocation. The lack of annotation coverage and output schema, combined with zero parameter semantics, leaves significant gaps. The tool has four parameters, and the description fails to explain any of them, making it difficult for an agent to construct a correct request beyond the obvious 'url'. The description covers the high-level flow but not the details necessary for full contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate by explaining parameter meanings. It does not mention any of the four parameters (url, zone, country, data_format) or provide additional context beyond the schema's basic types. The agent would have no semantic guidance on what zone or country represent or how data_format affects output, making parameter understanding heavily reliant on the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with 'Start an asynchronous Web Unlocker request' which clearly states the tool's action and resource. It differentiates from siblings by specifying 'asynchronous' and 'slow/heavy pages', and explicitly mentions returning a response_id for later retrieval via unlocker_get_async_result, distinguishing it from synchronous counterparts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear usage context: 'for slow/heavy pages', implying it is the appropriate choice for such cases. It also instructs the user to later pass the returned response_id to unlocker_get_async_result, outlining the complete workflow. However, it does not explicitly name alternatives like unlocker_scrape or state when not to use this tool, so it falls just 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It only mentions that the data is 'structured' and lists example fields, but does not explain potential behaviors like required URL format, error handling, rate limits, or whether the output is a single listing or multiple. The transparency is minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the core purpose and includes concrete example data fields. Every word is meaningful, with no redundant or irrelevant content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with a single URL parameter, the description gives a basic expectation of output content but does not clarify whether 'hotel listings' means a single hotel or a list, nor does it discuss edge cases or supported URL variations. It is minimally viable but lacks contextual depth.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema fully describes the only parameter ('url' with format 'uri' and description 'Booking.com hotel URL'), so schema coverage is 100%. The description adds no additional parameter-level detail beyond what the schema already provides, meeting the baseline but not exceeding it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns structured Booking.com hotel data with specific fields (price, rating, amenities, location), distinguishing it from other platform-specific tools. The name and title reinforce the resource, 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.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for Booking.com hotel data but does not explicitly state when to choose this tool over alternatives or provide any exclusions. No alternative tools are mentioned, so the guidance is left to inference from the tool name and category.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the output is structured and hints at the data fields included. With no annotations provided, the description carries a heavier burden, but it does not mention limitations (e.g., data freshness, missing fields, errors) or output format beyond 'structured'. This is a minimal but not comprehensive disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that efficiently states the tool's purpose and key data fields. Every word earns its place, with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool, the description gives a reasonable overview of return content, but it does not specify whether the listed fields are exhaustive, how the data is returned (e.g., JSON structure), or any caveats (e.g., rate limits, URL validation). The lack of an output schema increases the need for more detail, which is only partially met.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter 'url' with a clear description ('Crunchbase company URL') and format 'uri', achieving 100% coverage. The description adds no additional parameter semantics, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as providing Crunchbase company data and enumerates specific data types (funding rounds, investors, founded, HQ). It is distinct from sibling tools by its explicit reference to Crunchbase and company focus, though it lacks an explicit action verb like 'get' or 'retrieve'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage context is implied by the name and description: to obtain structured Crunchbase company data. However, there is no explicit guidance on when to use this tool versus alternatives (e.g., other company-data tools), nor any exclusions or alternative tool recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral expectations. It only states the output categories and the URL format requirement, with no mention of potential blocking, latency, or error conditions. For a web scraping tool, this is insufficient disclosure of how the tool behaves in practice.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences, front-loading the key data fields and placing the requirement last. Every word adds value; no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of a one-parameter product data tool, the description covers the essential purpose, output fields, and URL constraint. It does not specify error handling or return format, but that is partially mitigated by the listed fields. For the tool's complexity, it is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already fully documents the single 'url' parameter with a description matching the tool description. The description adds no new parameter details, so it earns the baseline score of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the tool as providing 'Structured Amazon product data' and lists specific fields (title, price, ratings, images, specs). However, it lacks an explicit action verb like 'fetch' or 'retrieve', making it less directive than ideal. The mention of /dp/ distinguishes it from related search and review tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear prerequisite: 'Requires a product URL containing /dp/.' This indicates the intended input context. It does not explicitly compare to alternatives like the sibling review or search tools, but the product-specific focus is apparent from the name and fields. There are no explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the kind of data returned (structured fields), which adds value beyond the schema. However, it does not mention error handling, rate limits, data freshness, or any behavior for invalid package names. For a simple retrieval tool, the disclosure is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one concise parenthetical clarification appended to the title. It uses a clear noun phrase and lists key data fields without waste. Every word adds meaning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 parameter, no output schema), the description adequately conveys what data the caller will receive. It covers the important output aspects (fields). It does not discuss result format or error cases, but for this level of complexity, the description is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter package_name is fully described in the schema (100% coverage), so the schema carries the load. The description does not add extra parameter syntax or examples, but none are needed. The baseline of 3 is appropriate since the schema fully documents the parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool's purpose: it returns structured npm package data, specifically listing latest version, README, dependencies, and weekly downloads. While it lacks an explicit verb like 'get' or 'fetch', the resource and scope are unambiguous. It distinguishes itself from siblings like pypi_package by naming npm.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage context is implied by the tool name and description: it is for retrieving npm package data. However, there is no explicit guidance on when to use this tool versus alternatives (e.g., web_data_pypi_package) or any exclusions. The description does not state prerequisites or mention that the package must exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the burden. It discloses the parallel execution, 'free-first' routing, and the return object shape. However, it omits details about failure handling, rate limiting, or cost implications beyond the 'paid' field, and it relies on referencing smart_scrape for routing details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loaded with the core action, includes the return format, and gives usage guidance. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a batch tool with 7 parameters, no output schema, and no annotations, the description is incomplete. It explains the return format and usage context but leaves parameter semantics and edge-case behavior undocumented, which is needed 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.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is low (29%), and the description does not explain the non-URL parameters like free_only, concurrency, data_format, or rate_limit_ms. It hints at free-first routing and concurrency, but the agent cannot accurately set parameters without additional documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: fetching an array of URLs in parallel, with the same routing as smart_scrape. It explicitly distinguishes itself from calling smart_scrape in a loop, making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit guidance: use this instead of calling smart_scrape in a loop for better performance. It references the sibling smart_scrape for routing behavior but does not mention exclusions or alternative tools like smart_crawl for broader crawling tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses one key behavioral trait (returns PENDING if still processing). It does not explain other likely behaviors such as error conditions, result retention, or success response format, leaving the behavioral disclosure incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences with front-loaded purpose and no unnecessary words. Every clause adds value, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description offers a functional overview and the polling behavior, but with no output schema and no annotations, it should describe what a successful result looks like and how long to wait. It is minimally adequate for a simple poll tool but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is only 50%: response_id has a description but zone does not. The tool description does not mention either parameter, failing to compensate for the coverage gap. Parameter semantics are entirely left to the schema, leaving zone's purpose unexplained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves the result of an async unlock job, using a specific verb and resource. It distinguishes from sibling unlocker_scrape_async by focusing on result retrieval and mentions the PENDING status, which clarifies its polling role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use after an async unlock job and instructs polling again if PENDING, providing clear context. However, it does not explicitly name the preceding tool or list alternative tools, so it lacks exclusionary guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It does disclose the 7-day time window and wildcard behavior, which is useful. However, it doesn't describe the return format (e.g., a JSON object with rate fields), error handling, or any rate limits. This is adequate for a simple read-only query but leaves some behaviors implicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, with the core function in the first sentence and a helpful wildcard tip in the second. No wasted words, and the structure is front-loaded for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple: one parameter, no output schema, and no annotations. The description covers the essential context: what the tool returns (success-rate stats), the time range (past 7 days), and domain syntax. It is complete enough for an agent to select and invoke it correctly, though it could be improved by noting the response structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage for the single 'domain' parameter, and the description essentially repeats the schema's example ('example.com or example.*') without adding new semantics or clarifying constraints. The baseline is 3 because the schema already documents the parameter; the description adds no further value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Return Web Unlocker success-rate statistics (past 7 days) for a domain.' The specific verb 'Return' and the resource 'success-rate statistics' make it unambiguous, and it distinguishes itself from sibling tools like unlocker_scrape by focusing on stats rather than fetching content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving success-rate metadata, and it provides a concrete example of wildcard syntax ('example.*'). However, it does not explicitly mention when to prefer this over alternatives, nor does it state any exclusions (e.g., 'for actual scraping use unlocker_scrape'). The guidance is more about value format than tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that if the job is still running, it returns status, which is important behavioral information. But it doesn't describe what happens on completion beyond 'download the data', nor does it mention error handling, timeouts, or how to interpret status codes. This is adequate but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences, front-loaded with the primary action ('Check progress and download data') and omits unnecessary details. Every phrase earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple 2-parameter schema and no output schema, the description covers the basic flow: check progress, download when ready, return status if still running. However, it doesn't explain the structure of the response or data, potential failure modes, or how to identify 'ready'. It's minimally complete but lacks depth for a fully self-contained description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It explains that snapshot_id is returned by web_scraper_trigger, adding provenance. However, it says nothing about the 'format' parameter (json, ndjson, jsonl, csv) or its default, leaving users to infer from the schema. The description adds only limited value over the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states what the tool does: 'Check progress and, when ready, download the data for a snapshot_id'. The verb is specific, and the resource is a snapshot_id tied to web_scraper_trigger, which distinguishes it from sibling tools that scrape specific websites or trigger new scrapes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says the snapshot_id comes from web_scraper_trigger, implying this tool is used after triggering a job. This provides clear context for when to call it. However, it doesn't state when not to use it or mention alternative result-fetching tools, so it lacks explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It reveals auto-detection behavior, return type, and cost implications. However, it omits details on error handling, redirects, payload size limits, or what 'behind a wall' means, leaving gaps for a tool that performs network fetches.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three dense sentences deliver high value without fluff. Each sentence provides essential information: input formats, auto-detection, output structure, and cost model. Excellent front-loading of the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple parse tool with only 2 parameters and no output schema, the description covers the main behaviors, including format handling, output shape, and cost nuance. The only notable gap is the undocumented free_only parameter and edge-case behaviors, but overall it is sufficiently complete 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.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is only 50% — free_only lacks a schema description. The description does not explain free_only's meaning beyond implying a free-fetch default, and it does not discuss parameter syntax or interplay. This is insufficient to compensate for the schema's missing coverage on free_only.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Fetch and parse an RSS 2.0 feed, Atom 1.0 feed, or XML sitemap', naming specific formats and output type. This distinguishes parse_feed from sibling scraping tools, which target specific websites or general web scraping.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear context for when to use the tool (for feeds/sitemaps) and highlights a key differentiator: 'Free direct fetch — no Bright Data credit unless the feed is behind a wall.' However, it does not explicitly name alternative tools or state when NOT to use it, so it doesn't reach a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the same-host default and url_filter behavior, but omits other important traits like rate limiting, robots.txt handling, or the HTML/markdown output choice. It offers some transparency but not comprehensive coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the primary behavior, and every word earns its place. No unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex 9-parameter tool with no annotations and no output schema, the description remains minimal. It covers the core crawl behavior but omits practical details like rate limiting, return format guidance, and the meaning of undocumented parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning for url_filter and implies depth/page limits, but the schema already describes 67% of parameters. It doesn't explain free_only or force_bright_data, which are undocumented in the schema, so it only partially compensates for the coverage gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool starts at a URL, follows links up to a depth/page limit, and returns content from each page. This distinguishes it from sibling single-page scrapers like smart_scrape and site-specific web_data tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear context by noting the same-hostname default and the url_filter option to restrict paths, implying multi-page crawling use cases. However, it doesn't explicitly name alternatives or state when not to use the tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations provided, so the description is the sole source of behavioral disclosure. The description states the tool 'queries' Grok and returns structured insights, implying a read-only operation, but it does not disclose potential rate limits, authentication requirements, or the exact structure/format of the output. It adds minimal behavioral context beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that gets straight to the point with the verb 'Query' leading. It avoids unnecessary detail and is easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one parameter, no output schema), but the description leaves some gaps: 'structured AI-generated insights' is vague about what the output looks like. Given no annotations and no output schema, a bit more detail about the return format or use cases would enhance completeness. Still, the core function is understandable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes the only parameter ('query') as 'Brand name or query topic' with 100% coverage. The description's phrase 'about a brand or topic' essentially restates the schema, adding no additional meaning or usage details. The baseline of 3 is appropriate since the schema fully documents the parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool's function with a specific verb ('Query') and resource ('Grok'), and states it produces structured AI-generated insights for a brand or topic. This distinguishes it from sibling tools like web_data_chatgpt_ai_insights and web_data_perplexity_ai_insights by naming the specific AI model.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on what to use the tool for (querying Grok about a brand/topic), but it does not explicitly mention alternatives or when not to use it. Since the name and description make the Grok-specific nature clear, it serves as adequate guidance but lacks explicit exclusions or alternative tool references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that robots.txt is fetched and parsed, the check is for the wildcard user-agent, return values are the verdict and matching rule, and robots.txt is cached for 24 hours. This provides useful behavioral nuance beyond a simple API call.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each earning its place: action, return value, caching behavior. No redundancy or fluff. Information is front-loaded with the core purpose in the first sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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 no output schema, the description adequately covers purpose, behavior, and output. It omits edge-case handling (e.g., missing robots.txt, errors), but the core functionality is fully described and 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.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (single 'url' parameter with clear description). The description adds that the URL is the target to check, not the robots.txt URL itself, which reinforces the schema but doesn't introduce new semantics. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: fetch and parse robots.txt, then check URL permission for the wildcard user-agent. It specifies the action (check), the resource (robots.txt), and the output (verdict and matching rule). This distinguishes it from sibling scraping and extraction tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage as a pre-check for URL permission against robots.txt but does not explicitly state when to use it versus alternatives. No exclusions or alternative tool mentions are provided, leaving it to the agent to infer context from sibling tool names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It reveals the fallback logic (free fetch with paid fallback on block), engine-specific pricing behavior, and output format flexibility (JSON vs raw HTML/Markdown). This is solid transparency, though it omits details on rate limits, authentication, or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core verb ('Run a web search'), and packs essential details about engine behavior and output modes without any repetition or fluff. Every word adds value, making it an exemplary concise description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 11 parameters and no output schema, the description covers the critical operational context: engine selection, cost implications, and output format. It does not detail all parameters or result structures, but the schema covers parameter semantics, and the description gives enough context for effective invocation. It is 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.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is high (82%), so the parameters are already well-described in the schema. The description adds marginal semantic value by explaining the parse parameter's effect and the engine-specific cost implications, but it does not deeply elaborate on any parameter beyond what the schema already provides. This aligns with the baseline 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Run a web search') and explicitly distinguishes it from sibling site-specific tools by covering multiple general search engines (Google, Bing, Yandex, DuckDuckGo). It also clarifies the difference between free and paid execution paths, leaving no ambiguity about its scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when the tool uses free vs. paid backend ('tries a FREE direct fetch first' for DuckDuckGo, 'always uses the paid SERP API' for Google/Bing/Yandex) and how the parse parameter affects output. However, it doesn't explicitly state when to prefer this tool over sibling web_data_* tools, relying on the name and context to imply general search use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses the asynchronous trigger nature, the snapshot_id return contract, and the dataset_id prerequisite/fallback. It does not mention costs, rate limits, or failure modes, but covers the essential behavioral contract for a trigger tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three tightly written sentences front-load the core verb and resource, then add the return/polling contract and the dataset_id requirement. No filler or repetition of schema details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is a simple async trigger with a required urls array and several optional parameters, and the description explains the most important missing piece: what the caller receives and how to continue via web_scraper_get_results. It could add guidance for extra_inputs or when to choose a specialized web_data_* tool, but the schema plus description is largely sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is ~80%, so the baseline is 3. The description adds meaning beyond the schema for dataset_id (argument vs BRIGHTDATA_DATASET_ID fallback) and the overall return contract, but does not elaborate on extra_inputs, include_errors, or custom_output_fields beyond what the schema already states.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Trigger a Bright Data dataset (Web Scraper / Crawl API) collection over one or more URLs,' which names a specific action and resource. It immediately distinguishes itself from the sibling web_scraper_get_results by stating it returns a snapshot_id to poll with that tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides clear usage context: the tool triggers a collection and returns a snapshot_id to poll with web_scraper_get_results, plus a note on dataset_id via argument or BRIGHTDATA_DATASET_ID. It does not spell out when to prefer this over the many web_data_* siblings, so it stops short of full exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the burden and does a good job: it discloses that the tool runs on Bright Data's remote infrastructure, requires auth, supports automatic CAPTCHA solving and sticky sessions, and can return HTML, text, and/or screenshots. It does not mention rate limits, costs, or side effects on live sites, but these are less critical given the explicit interaction features.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two dense but well-structured sentences cover the tool's purpose, capabilities, infrastructure, and authentication requirement. There is no fluff, and every clause adds useful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with no annotations and no output schema, the description covers the essential behavioral surface: target use case, interaction types, output formats, infrastructure, and auth. It omits details on error handling and rate limits, but the schema fills in defaults for many parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 58%, and the description compensates by mapping capabilities to parameters: 'automatic CAPTCHA solving' (solve_captcha/captcha_timeout), 'ad blocking' (block_ads), 'device emulation' (device), 'sticky sessions' (session_id), 'clicks/typing/scrolling' (actions), and output types (return_html/return_text/screenshot). It does not explain wait_until/nav_timeout, but the schema provides enums and defaults for those.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Drive' and identifies the precise resource: a 'real, remote cloud browser (Puppeteer over CDP).' It explicitly scopes the tool to JavaScript-heavy sites and multi-step flows, and enumerates unique capabilities (CAPTCHA solving, ad blocking, sticky sessions) that distinguish it from sibling scraping tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It clearly identifies target use cases: JavaScript-heavy sites and multi-step flows. It also notes the prerequisite that BRIGHTDATA_BROWSER_AUTH must be set, effectively telling the agent when it can be used. However, it does not explicitly name alternatives or state when not to use it, 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing behavior. It reveals that dynamic values are detected and substituted automatically, static assets are skipped by default, and dry_run prevents actual requests. This covers the most important behavioral traits, though it doesn't explicitly state that live requests will be made during a real run.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with the primary action, followed by a concise explanation of the auto-correlation mechanism and then parameter usage notes. Every sentence earns its place with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 params, nested object, no output schema), the description covers the essential behavioral and usage aspects: the purpose, the auto-substitution mechanism, default asset skipping, and parameter-specific roles. It doesn't cover failure modes or redirects, but the core functionality is well-specified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema descriptions already cover all parameters (100% coverage), so baseline is 3. The description adds practical guidance beyond the schema: 'substitutions' for credentials/API keys, 'dry_run' for previewing. This enhances semantic understanding beyond the parameter descriptions alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Replays an HTTP Archive (.har) file exported from browser DevTools.' It also explains the core value-add of auto-correlating dynamic values, clearly distinguishing it from the scraping-focused sibling tools. The title reinforces the purpose with 'CSRF/token auto-correlation.'
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context: it is intended for login flows, form submissions, and signed API calls, and explains when to use 'substitutions' (for values never in responses) and 'dry_run' (to preview). It doesn't explicitly name alternative tools, but no sibling tool overlaps with this replay functionality, so the context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It transparently discloses the key behavioral trait that it always spends Bright Data credit (a cost implication) and implies it can bypass blocks. It doesn't cover every edge case like error handling or rate limits, but the most critical behavioral disclosure is present and accurate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three concise sentences with no fluff. It opens with the most important warning (cost), then provides the alternative, then lists specific use cases. Every sentence earns its place and the structure is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 8 parameters and no output schema, the description provides strong decision-making context: cost implications and when to use vs. alternatives. It doesn't explicitly describe the return format beyond the data_format enum, but the schema covers that. Given the absence of annotations, it is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers all 8 parameters with descriptions (100% coverage), so the baseline is 3. The description adds marginal value by mentioning screenshot output as a reason to use the tool, which maps to the data_format parameter, but it does not add meaning to the other parameters beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states what the tool does: it scrapes any URL using Bright Data's Web Unlocker, as indicated by the title and the statement that it always spends Bright Data credit. It also distinguishes itself from the sibling smart_scrape by positioning itself as the paid fallback for blocked sites, making the resource and scope explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use guidance: prefer smart_scrape for read-only fetches, and use unlocker_scrape directly only for screenshots, unconditional Bright Data forcing, or when smart_scrape confirms a hard block. It also names the alternative tool, making the decision process clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description clearly implies a read-only operation by stating it 'Show[s]' data. It adds useful behavioral context: the list is in-memory, reflecting the current routing state, and mentions the reason for the skip-list (free tier was blocked). This goes beyond a simple restatement of the title.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the key action and resource, and every clause provides value. There is no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that the tool has no parameters, no output schema, and no annotations, the description provides sufficient detail: what it shows, why it exists, and when it's useful. Nothing critical is missing for the agent to decide to invoke this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so parameter semantics are not applicable. The baseline for no parameters is 4, and no further explanation is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Show') and a clear resource ('in-memory list of domains currently being sent straight to Bright Data'). It also provides the rationale ('free tier was blocked recently'), which helps differentiate it from the many sibling scraping tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'Useful for debugging cost' gives clear context for when to use this tool. It doesn't explicitly mention alternatives or exclusions, but the tool's niche purpose is distinct from the other scraping and data tools, making the guidance adequate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully bears the burden of disclosing behavioral traits. It reveals the free-first/fallback-to-paid logic, the in-memory skip-list for hard domains, the types of anti-bot walls detected, and that results report which tier was used and whether credit was spent. This is rich, non-obvious behavior that goes well beyond the schema and helps the agent anticipate side effects like spending.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is information-dense and well-structured. It opens with the primary positioning ('PREFERRED scraping tool'), then explains the cost optimization strategy, detection criteria, skip-list behavior, cost outcome, and usage alternatives. Every sentence contributes distinct value: strategy, fallback conditions, memory optimization, pricing, output reporting, and alternative tool guidance. No filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is complex (16 params, no output schema, no annotations), yet the description covers the essential context: cost model, fallback triggers, the skip-list, and result reporting. It also provides comparison to siblings. While it doesn't detail the full return structure or error handling, the schema thoroughly documents parameters, and the description compensates for missing annotations. A slight gap is the lack of mention of what happens on total failure, but overall it is quite complete for the complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides 100% description coverage for all 16 parameters, so the baseline is 3. The description does not directly elaborate on individual parameters but does provide context for the overall cost-aware behavior that some parameters (e.g., free_only, force_bright_data) relate to. It adds no parameter-specific meaning beyond what the schema offers, hence a solid 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies smart_scrape as a scraping tool that fetches a page with a cost-aware strategy, using the cheapest method first and falling back to a paid option only when necessary. It explicitly names sibling tools (unlocker_scrape, browser_scrape) and positions this tool as the preferred read-only fetch tool, distinguishing it from alternatives with different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit usage guidance: it states this is the preferred scraping tool, recommends using it 'instead of unlocker_scrape for read-only fetches,' and reserves browser_scrape for 'real JS interaction.' It also explains the cost-saving behavior, making clear when the paid tier is triggered, which helps the agent decide when to invoke it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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