Bright Data Web MCP

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, with clear separation between general scraping (extract, scrape_as_html, scrape_as_markdown, search_engine), browser interaction (scraping_browser_*), and structured data retrieval (web_data_*). However, some overlap exists between extract and scrape_as_markdown, as both can produce markdown, and among web_data_* tools for similar platforms (e.g., Instagram posts vs. profiles), but descriptions help clarify use cases.
Naming Consistency5/5
Tool names follow highly consistent patterns: snake_case throughout, with clear prefixes like 'scraping_browser_' for browser tools and 'web_data_' for structured data tools. The naming is predictable and organized, making it easy to identify tool categories and purposes at a glance.
Tool Count2/5
With 60 tools, the count is excessive for a single server, far beyond the typical well-scoped range of 3-15 tools. This large number can overwhelm agents and increase complexity, despite the server's broad web data domain, suggesting it could be better split into multiple focused servers.
Completeness5/5
The tool set provides comprehensive coverage for web scraping and data extraction, including general scraping, browser automation, and structured data from numerous platforms (e.g., Amazon, Google, social media). It supports a wide range of use cases with no obvious gaps, offering both raw and processed data retrieval methods.
Average 3.4/5 across 60 of 60 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

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 tool can be a cache lookup and more reliable than scraping, which adds some context about performance and data source. However, it doesn't disclose critical behavioral traits such as whether it's read-only, potential rate limits, authentication needs, error handling, or what 'structured data' entails in terms of format. For a tool with no annotations, this leaves significant gaps.
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 brief with two sentences that are front-loaded with the main purpose. There's no unnecessary verbosity, and each sentence adds value (purpose and reliability context). However, it could be slightly more structured by explicitly separating usage notes, but overall it's efficient.
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 tool has no annotations, no output schema, and low parameter coverage (0%), the description is incomplete. It covers the basic purpose and a reliability note but misses key contextual details: it doesn't explain the return format (what 'structured data' means), error conditions, or how to interpret results. For a data retrieval tool with siblings offering similar functions, this lacks sufficient guidance for effective use.
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?
The input schema has 1 parameter (url) with 0% description coverage, meaning the schema provides no semantic information about the parameter. The description doesn't mention the 'url' parameter at all, failing to explain what URL is expected (e.g., a Crunchbase company page URL) or any format specifics beyond the schema's URI format. With low schema coverage, the description doesn't compensate, leaving the parameter poorly documented.
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 states the tool reads structured Crunchbase company data, which is a clear verb+resource combination. However, it doesn't specifically distinguish this tool from sibling tools like 'web_data_linkedin_company_profile' or 'web_data_zoominfo_company_profile' beyond mentioning Crunchbase as the source. The purpose is understandable but lacks sibling differentiation.
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 mentions this can be a cache lookup and more reliable than scraping, which provides some implied context about when to use it (for reliability). However, it doesn't explicitly state when to use this tool versus alternatives like 'scrape_as_html' or 'scrape_as_markdown' for Crunchbase data, nor does it provide clear exclusions or prerequisites. The guidance is minimal and incomplete.
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 full burden. It mentions 'Tell the user about tool usage,' which suggests a read-only, informational output, but fails to disclose behavioral traits like what data is included (e.g., metrics, timestamps), format of output, or any side effects. This is inadequate for a tool with zero annotation coverage.
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, straightforward sentence with no wasted words, making it appropriately concise. However, it's under-specified rather than efficiently informative, slightly reducing its effectiveness, but it's not overly verbose or poorly structured.
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 tool has no annotations, no output schema, and 0 parameters, the description is incomplete. It lacks details on what 'tool usage' entails, the output format, or how it integrates with the session context. For a tool in a server full of web scraping siblings, this leaves significant gaps in understanding its role and behavior.
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 0 parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description doesn't add parameter semantics, but with no parameters, the baseline is 4—it doesn't need to compensate, and the schema suffices.
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 'Tell the user about the tool usage during this session' is vague and tautological—it essentially restates the tool name 'session_stats' without specifying what 'tool usage' means (e.g., counts, durations, errors) or what 'this session' refers to. It doesn't clearly distinguish from sibling tools, which are primarily web scraping/data extraction focused, making this tool's purpose ambiguous in 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 explicit guidance is provided on when to use this tool versus alternatives. The description implies it's for session-related information, but it doesn't specify scenarios (e.g., monitoring, debugging, reporting) or prerequisites. Given the sibling tools are for web operations, this lack of context leaves the agent unsure about its application.
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?
No annotations are provided, so the description carries the full burden. It discloses that the tool performs a 'read' operation and mentions cache lookup for reliability, which adds useful behavioral context. However, it doesn't cover critical aspects like rate limits, authentication needs, data freshness, or error handling, leaving significant gaps for a web data 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 brief with two sentences that are front-loaded: the first states the core purpose, and the second adds behavioral context. There's no wasted text, but it could be more structured by explicitly separating purpose from guidelines.
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 complexity of a web data tool with 3 undocumented parameters, no annotations, and no output schema, the description is incomplete. It lacks details on parameter usage, output format, error cases, and how it differs from sibling tools, making it inadequate for reliable agent invocation.
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?
The schema description coverage is 0%, meaning none of the three required parameters (url, first_name, last_name) are documented in the schema. The description provides no information about these parameters—their purpose, format, or how they interact (e.g., whether url is a LinkedIn search URL or profile page). This fails to compensate for the lack of schema documentation.
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 states the tool 'read[s] structured linkedin people search data', which provides a clear verb ('read') and resource ('linkedin people search data'). However, it doesn't distinguish this tool from sibling tools like 'web_data_linkedin_person_profile' or 'web_data_linkedin_company_profile', leaving the specific scope of 'people search' versus 'person profile' ambiguous.
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 mentions that 'This can be a cache lookup, so it can be more reliable than scraping', which implies usage when reliability is prioritized over real-time data. However, it lacks explicit guidance on when to use this tool versus alternatives like 'scrape_as_html' or 'web_data_linkedin_person_profile', and doesn't specify prerequisites 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions advanced content extraction and the ability to bypass bot detection or CAPTCHA, which adds some context about capabilities. However, it fails to address critical behavioral aspects such as rate limits, error handling, authentication needs, or whether the operation is read-only or has side effects. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
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 concise and front-loaded, stating the core functionality in the first sentence. The second sentence adds useful context about bypassing protections. There's no wasted text, and both sentences contribute meaningfully, though it could be slightly more structured by explicitly separating purpose from capabilities.
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 tool's complexity (involving web scraping with advanced features), no annotations, no output schema, and low parameter coverage, the description is incomplete. It lacks details on output format beyond 'HTML,' error cases, performance considerations, and how it differs from sibling tools. For a tool with such potential variability and no structured support, the description should provide more comprehensive guidance to ensure proper usage.
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 1 parameter ('url') with 0% description coverage, meaning the schema provides no semantic details. The description adds minimal value by implying the URL is for a webpage to scrape, but it doesn't elaborate on format constraints, validation, or what 'advanced options' entail beyond the single parameter. With low schema coverage, the description compensates inadequately, resulting in a baseline score due to the lack of detailed parameter information.
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's purpose: 'Scrape a single webpage URL with advanced options for content extraction and get back the results in HTML.' It specifies the verb (scrape), resource (webpage URL), and output format (HTML). However, it doesn't explicitly differentiate from siblings like 'scrape_as_markdown' or 'scraping_browser_get_html', which offer similar functionality with different output formats or methods.
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 minimal guidance on when to use this tool. It mentions 'advanced options for content extraction' and the ability to 'unlock any webpage even if it uses bot detection or CAPTCHA,' which hints at use cases involving complex or protected sites. However, it lacks explicit comparisons to alternatives (e.g., when to choose this over 'scrape_as_markdown' or browser-based tools) or clear exclusions, leaving the agent with limited decision-making 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. While 'navigate' implies a state-changing operation, the description doesn't reveal important behavioral traits: whether this requires an existing session, what happens on navigation failure, if it waits for page load, or any rate limits/authentication needs. It provides minimal 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, efficient sentence that directly states the tool's purpose with zero wasted words. It's appropriately sized and front-loaded with the essential information.
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 navigation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't address critical context: what constitutes a 'scraping browser session', whether navigation is synchronous/asynchronous, what happens on success/failure, or what (if anything) is returned. The description leaves too many operational questions unanswered.
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% with the single 'url' parameter fully documented in the schema. The description adds no additional parameter semantics beyond what the schema already provides (e.g., URL format requirements, validation rules, or examples). The baseline score of 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('navigate') and target ('a scraping browser session to a new URL'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'scraping_browser_go_back' or 'scraping_browser_go_forward', which are also navigation-related operations.
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 prerequisites (e.g., requires an active scraping browser session), when not to use it, or how it differs from other navigation tools in the sibling list like 'go_back' or 'go_forward'.
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 states the action but fails to describe key behavioral traits such as output format (e.g., image type, size), whether it requires a page to be loaded, potential errors (e.g., if no page is open), or performance implications. This leaves significant gaps in understanding how the tool behaves.
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, clear sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and efficiently communicates the core functionality, making it easy for an agent to quickly understand what the tool does.
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 complexity of a screenshot tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., output format, error conditions), usage context relative to siblings, and does not compensate for the absence of structured data. This makes it insufficient for an agent to fully understand the tool's operation.
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% description coverage, with the parameter 'full_page' well-documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, such as explaining the trade-offs of 'full_page' beyond size (e.g., memory usage, time). Given the high schema coverage, 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 states the action ('Take a screenshot') and the target ('of the current page'), which is specific and unambiguous. However, it does not explicitly differentiate from sibling tools like 'scrape_as_html' or 'scraping_browser_get_text', which also capture page content but in different formats. This omission prevents a perfect score.
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 a screenshot is preferable over other content extraction methods (e.g., HTML or text), nor does it reference sibling tools for context. This lack of usage context leaves the agent without direction.
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 full burden. It states the action but doesn't disclose behavioral traits like what happens if the selector doesn't match any element (error? no-op?), whether scrolling is smooth or instant, if it waits for element visibility, or any side effects. The description is minimal and lacks needed operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with zero waste. It's appropriately sized for a simple tool and front-loaded with the core action, 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a browser interaction tool. It doesn't cover error conditions, return values (e.g., success status, new viewport position), or integration with other tools in the scraping context. For a tool that modifies browser state, more behavioral context is needed.
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% with the parameter 'selector' well-documented in the schema. The description adds no additional parameter semantics beyond implying the selector targets an element to scroll to, which is already clear from the schema. Baseline 3 is appropriate when schema does the heavy lifting.
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 action ('Scroll to') and target ('a specific element on the page'), which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'scraping_browser_scroll' (which likely scrolls by amount rather than to element), leaving some ambiguity in sibling 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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a page to be loaded), when-not scenarios (e.g., if element doesn't exist), or explicit alternatives among the many sibling browser interaction 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?
With no annotations provided, the description carries full burden for behavioral disclosure but offers minimal information. It mentions typing but doesn't cover important aspects like whether this requires a loaded page, what happens if the selector doesn't exist, whether typing simulates human behavior, or what side effects might occur. The description is insufficient for a mutation tool with zero annotation 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 extremely concise at just four words, with zero wasted language. It's front-loaded with the core action and gets straight to the point without unnecessary elaboration.
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 mutation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what happens after typing, what errors might occur, or what the tool returns. Given the complexity of browser interaction and the lack of structured safety information, more behavioral context is needed.
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 coverage is 100%, providing complete documentation for all three parameters (selector, text, submit). The description adds no additional parameter semantics beyond what's already in the schema, so it meets the baseline of 3 where the schema does the heavy lifting.
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 action ('Type text') and target ('into an element'), providing a specific verb+resource combination. However, it doesn't differentiate itself from potential sibling tools like 'scraping_browser_click' or 'scraping_browser_navigate' that might also interact with page elements, missing full sibling distinction.
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 many sibling tools available for web interaction and data extraction, there's no indication of appropriate contexts, prerequisites, or exclusions for this specific typing functionality.
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 states the tool waits for an element to be visible, but doesn't explain what happens after waiting (e.g., returns success/failure, throws an error on timeout, or proceeds silently). It also omits details like whether it's blocking, if it affects page state, or any performance implications. For a tool with no annotations, this 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence: 'Wait for an element to be visible on the page'. It's front-loaded with the core action and resource, with zero wasted words. This makes it easy to parse and understand quickly, earning a high score for 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?
Given the complexity of browser interaction tools, the description is incomplete. With no annotations and no output schema, it fails to explain behavioral aspects (e.g., what the tool returns or how errors are handled) or usage context. While the schema covers parameters well, the overall description lacks the depth needed for an agent to use this tool effectively in a scraping workflow.
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% description coverage, with clear documentation for both parameters ('selector' and 'timeout'). The description doesn't add any extra meaning beyond the schema, such as examples of CSS selectors or advice on timeout values. Since the schema does the heavy lifting, a baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
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's purpose: 'Wait for an element to be visible on the page'. It specifies the action ('wait for') and the resource ('an element'), making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'scraping_browser_navigate' or 'scraping_browser_click', which are also browser interaction tools but serve 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 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 prerequisites (e.g., requiring a page to be loaded first), exclusions, or compare it to similar tools like 'scraping_browser_get_html' which might also involve waiting. This lack of context makes it harder for an agent to decide when this tool is appropriate.
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 full burden. It mentions scraping and return format (SERP results in markdown), but lacks critical behavioral details: whether this requires authentication, rate limits, potential blocking risks, pagination behavior beyond the cursor parameter, or error handling. For a scraping tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.
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?
Perfectly concise and front-loaded: two sentences that directly state what the tool does and what it returns. Every word earns its place with zero redundancy or unnecessary elaboration.
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 complexity of web scraping (which involves network calls, potential failures, and structured data extraction), no annotations, and no output schema, the description is incomplete. It doesn't cover error conditions, rate limits, authentication needs, or detailed output structure beyond 'markdown'. For a tool with 3 parameters and significant behavioral implications, more context is needed.
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 33% (only cursor has a description). The description adds minimal parameter context: it implies 'engine' selects between Google/Bing/Yandex (though the enum already shows this) and 'query' is for search terms. However, it doesn't explain parameter interactions, format requirements for query, or how cursor works with the returned results. With low schema coverage, the description provides some but insufficient compensation.
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's purpose: 'Scrape search results from Google, Bing or Yandex' specifies the verb (scrape) and resource (search results from specific engines). It distinguishes from many siblings that scrape specific websites (e.g., web_data_amazon_product) but doesn't explicitly differentiate from generic scraping tools like scrape_as_html or scrape_as_markdown.
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 use this tool versus alternatives. It doesn't mention when to choose search_engine over other scraping tools like scrape_as_html for the same search engines, or when to use it versus specialized web_data tools for specific platforms. The description provides no context about use cases 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 provided, the description carries the full burden of behavioral disclosure. It mentions the tool 'can be a cache lookup' and 'can be more reliable than scraping,' which adds useful context about performance and reliability. However, it doesn't disclose critical behavioral traits such as rate limits, authentication needs, error conditions, or what 'structured data' specifically entails (e.g., JSON format, specific fields).
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 extremely concise with just two sentences that are front-loaded and waste-free. The first sentence states the core purpose, and the second adds valuable context about cache lookup and reliability. Every word earns its place, making it easy to parse quickly.
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 complexity of web data extraction (1 parameter, no annotations, no output schema), the description is incomplete. It lacks details on what 'structured data' means in the output, error handling, rate limits, or authentication requirements. While it hints at reliability benefits, it doesn't provide enough information for an agent to confidently invoke this tool without additional context or trial-and-error.
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?
The input schema has 1 parameter with 0% description coverage, and the tool description provides no information about the 'url' parameter. It doesn't explain what kind of LinkedIn profile URL is expected (e.g., public profile URL, specific format), any validation rules, or examples. The description fails to compensate for the low schema coverage, leaving the parameter semantics unclear.
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's purpose: 'Quickly read structured linkedin people profile data.' It specifies the verb ('read'), resource ('linkedin people profile data'), and key characteristic ('structured'). However, it doesn't explicitly differentiate from sibling tools like 'web_data_linkedin_people_search' or 'scrape_as_html' beyond mentioning cache vs scraping reliability.
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 provides some implied usage guidance by mentioning 'This can be a cache lookup, so it can be more reliable than scraping,' which suggests this tool might be preferred over scraping tools for reliability. However, it doesn't explicitly state when to use this tool versus alternatives like 'scrape_as_html' or 'web_data_linkedin_people_search,' nor does it mention any prerequisites 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 provided, the description carries the full burden of behavioral disclosure. It states the action but lacks details on permissions, rate limits, side effects, or the format of the returned text. This is a significant gap for a tool that interacts with a browser, as it doesn't clarify if it requires an active session or has any constraints.
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, efficient sentence that directly states the tool's function without any unnecessary words. It is front-loaded and wastes no space, making it highly concise and well-structured for quick understanding.
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 complexity of browser interaction and the lack of annotations and output schema, the description is incomplete. It doesn't explain the return format, error conditions, or dependencies on other tools like 'scraping_browser_navigate'. For a tool in a scraping context, more context is needed to ensure proper usage.
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 0 parameters with 100% schema description coverage, so the schema already fully documents the inputs. The description doesn't need to add parameter details, and it correctly avoids redundancy. A baseline of 4 is appropriate since no parameters are present, and the description doesn't mislead about inputs.
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 verb 'Get' and the resource 'text content of the current page', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'scraping_browser_get_html' or 'extract', which might also retrieve text content, so it misses full sibling differentiation.
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 such as 'scraping_browser_get_html' or 'scrape_as_markdown'. There is no mention of context, prerequisites, or exclusions, leaving the agent to infer usage based on the name alone.
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 burden but only states the basic action. It lacks details on behavioral traits such as whether it requires an active browser session, what happens if no forward history exists (e.g., error or no-op), or if it affects page state (e.g., reloading). This is inadequate for a tool with potential 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, efficient sentence with no wasted words. It's front-loaded and directly conveys the core action, making it easy to parse quickly.
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 complexity of a browser interaction tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'next page' means in context (e.g., browser history), potential errors, or return values, leaving critical gaps for an agent to use it correctly.
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 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description doesn't add param info, which is fine, but it also doesn't compensate for any gaps since there are none. Baseline is 4 for zero parameters.
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 'Go forward to the next page' clearly states the action (go forward) and target (next page) with a specific verb. It distinguishes from siblings like 'scraping_browser_go_back' by direction, but doesn't fully differentiate from navigation tools like 'scraping_browser_navigate' in terms of scope or mechanism.
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 doesn't mention prerequisites (e.g., requires a browsing session with forward history), exclusions, or comparisons to siblings like 'scraping_browser_navigate' for general navigation.
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 full burden. It mentions scrolling behavior but lacks details on whether it waits for page load, handles infinite scroll, or has any side effects. This is a significant gap for a browser interaction 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, clear sentence with no wasted words. It's front-loaded and efficiently communicates the core functionality.
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 browser interaction tool with no annotations and no output schema, the description is insufficient. It doesn't explain what happens after scrolling (e.g., does it return success/failure, wait for content, or handle errors), leaving critical behavioral context missing.
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 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, earning a baseline high score.
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 action ('scroll to the bottom') and target ('current page'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling 'scraping_browser_scroll_to', which might offer more flexible scrolling options.
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 'scraping_browser_scroll_to' or other navigation tools. The description only states what it does, not when it's appropriate.
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 that this is a read operation ('read structured... data'), mentions reliability aspects ('can be more reliable than scraping'), and hints at caching behavior ('can be a cache lookup'). However, it lacks details on rate limits, authentication needs, error conditions, or what 'structured data' entails in the output. This provides basic context but leaves significant behavioral gaps.
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 brief and front-loaded with the core purpose. All three sentences add value: the first states what it does, the second lists requirements, and the third provides behavioral context. There's no wasted text, though it could be slightly more structured for clarity.
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 no annotations, 0% schema coverage, no output schema, and 3 parameters, the description is incomplete. It covers the basic purpose and hints at caching behavior but misses critical details: parameter semantics (especially 'pages_to_search'), output format, error handling, and explicit differentiation from siblings. For a tool with this complexity, more context is needed.
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 0%, so the description must compensate. It mentions 'keyword' and 'url' parameters in the requirement statement, but doesn't explain 'pages_to_search' at all. The description adds minimal semantic value beyond naming two parameters, failing to clarify format expectations (e.g., URL must be an Amazon domain) or the purpose of 'pages_to_search'. This is inadequate given the coverage gap.
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's purpose: 'Quickly read structured amazon product search data.' It specifies the verb ('read'), resource ('amazon product search data'), and scope ('structured'). However, it doesn't explicitly differentiate from sibling tools like 'web_data_amazon_product' or 'search_engine', which would be needed for a score of 5.
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 provides some usage context by stating 'Requires a valid search keyword and amazon domain URL' and mentioning it 'can be a cache lookup, so it can be more reliable than scraping.' This implies when to use it (for Amazon product searches with caching benefits) but doesn't explicitly compare to alternatives like 'scrape_as_html' or 'search_engine' from the sibling list, nor does it specify when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 discloses that this is a read operation ('read'), involves cache lookup for reliability, and requires a URL. However, it lacks details on rate limits, authentication needs, error handling, or what 'structured' data entails. The mention of cache behavior adds some value, but more behavioral context would be helpful for a tool with no annotations.
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 concise with three sentences that are front-loaded: the first states the core purpose, the second specifies the key requirement, and the third adds behavioral context. There's no wasted text, but it could be slightly more structured (e.g., bullet points) for clarity.
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 no annotations, 0% schema coverage, no output schema, and 2 parameters, the description is incomplete. It covers the purpose and URL requirement but misses details on the 'days_limit' parameter, output format, error cases, and how it differs from sibling tools. For a data-fetching tool with undocumented parameters, this leaves too many gaps for effective use.
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 0%, so the description must compensate. It mentions the 'url' parameter ('Requires a valid Google maps URL') but doesn't explain the 'days_limit' parameter at all. With 2 parameters and no schema descriptions, the description only covers one parameter partially, leaving significant gaps in understanding what inputs are needed and why.
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's purpose: 'Quickly read structured Google maps reviews data.' It specifies the verb ('read'), resource ('Google maps reviews data'), and key characteristics ('structured', 'quickly'). However, it doesn't explicitly differentiate from sibling tools like 'scrape_as_html' or 'web_data_facebook_company_reviews' that might also handle reviews from other platforms.
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 provides some usage context: 'Requires a valid Google maps URL' and mentions it 'can be more reliable than scraping,' which implies an alternative approach. However, it doesn't explicitly state when to use this tool versus siblings like 'scrape_as_html' or other web_data_* tools for reviews, nor does it mention any exclusions or prerequisites beyond the URL requirement.
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 of behavioral disclosure. It adds useful context: the tool 'can be a cache lookup' and 'can be more reliable than scraping,' which hints at performance and reliability traits. However, it doesn't cover critical aspects like rate limits, authentication needs, error handling, or data freshness, leaving gaps in transparency for a web data 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 appropriately sized with three concise sentences that are front-loaded: the first states the purpose, the second specifies a requirement, and the third adds behavioral context. There's no wasted text, and each sentence adds value, though it could be slightly more structured for clarity.
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 tool's complexity (web data retrieval with 2 parameters), no annotations, no output schema, and 0% schema coverage, the description is incomplete. It lacks details on return values, error cases, limitations (e.g., comment count max), and how it differs from scraping siblings. For a data-fetching tool, this leaves significant gaps in understanding.
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 0%, so the description must compensate for undocumented parameters. It only mentions the 'url' parameter ('Requires a valid youtube video URL') and implies data retrieval but doesn't explain 'num_of_comments' or its default value. With 2 parameters and minimal semantic detail, it fails to adequately supplement the schema, resulting in a low score.
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's purpose: 'Quickly read structured youtube comments data.' It specifies the verb ('read'), resource ('youtube comments data'), and quality ('structured'). However, it doesn't explicitly distinguish from sibling tools like 'web_data_youtube_videos' or other comment-related tools, which prevents a perfect score.
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 provides some usage context: 'Requires a valid youtube video URL' and mentions it 'can be more reliable than scraping,' which implies it's a better alternative to scraping tools. However, it doesn't explicitly state when to use this versus other sibling tools (e.g., 'scrape_as_html' for YouTube) or detail specific scenarios, leaving usage somewhat implied rather than explicit.
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 full burden. It states the action but doesn't disclose behavioral traits like whether this requires an active browser session, what happens if there's no history, error conditions, or side effects. The description is minimal and lacks operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with zero waste. It's appropriately sized for a simple navigation command and front-loaded with the essential action.
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 (0 parameters, no output schema, no annotations), the description is minimally adequate but lacks completeness. It doesn't address behavioral context like error handling or session requirements, which would be helpful for an agent invoking 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 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description doesn't add parameter semantics (none exist), which is appropriate. Baseline is 4 for zero-parameter tools.
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 action ('Go back') and target ('to the previous page'), providing a specific verb+resource combination. It distinguishes this from forward navigation (scraping_browser_go_forward) but doesn't explicitly differentiate from other navigation tools like scraping_browser_navigate.
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 context (after navigating to a page, to return to the previous one) but doesn't provide explicit guidance on when to use this versus alternatives like scraping_browser_navigate or when not to use it (e.g., when there's no browser history). No prerequisites or sibling tool comparisons are mentioned.
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 of behavioral disclosure. It adds useful context: it's a read operation ('read'), involves a cache lookup (implying potential latency/availability trade-offs), and is 'more reliable than scraping' (suggesting higher accuracy or stability). However, it doesn't cover details like rate limits, authentication needs, error handling, or what 'structured data' entails.
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 appropriately sized with three concise sentences that front-load the core purpose. Each sentence adds value: the first states what it does, the second specifies the input requirement, and the third explains a key behavioral trait. No wasted words, though it could be slightly more 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?
Given the tool's moderate complexity (read operation with caching), no annotations, no output schema, and low schema coverage, the description is somewhat complete but has gaps. It covers the purpose, input requirement, and a reliability advantage, but lacks details on output format, error cases, or performance characteristics, which would help an agent use it effectively.
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 1 parameter with 0% description coverage. The description adds meaning by specifying the parameter must be 'a valid ebay product URL,' which clarifies the expected format and domain beyond the schema's generic 'uri' format. This compensates partially but doesn't detail URL validation rules or examples.
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's purpose: 'Quickly read structured ebay product data.' It specifies the verb ('read'), resource ('ebay product data'), and quality ('structured'). However, it doesn't explicitly differentiate from sibling tools like 'web_data_amazon_product' or 'scrape_as_html' beyond mentioning it's 'more reliable than 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 provides some usage guidance: 'Requires a valid ebay product URL' and 'This can be a cache lookup, so it can be more reliable than scraping.' This implies when to use it (for eBay products, preferring reliability over raw scraping) but doesn't explicitly state when to choose it over alternatives like 'scrape_as_html' or 'web_data_amazon_product' for similar 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 provided, the description carries the full burden. It discloses that this is a read operation ('read'), mentions a cache lookup mechanism for reliability, and hints at structured data output. However, it lacks details on rate limits, authentication needs, error handling, or what 'structured' means exactly. It adds value but leaves behavioral gaps for a web data 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 concise with three sentences that each add value: stating the purpose, listing requirements, and explaining the cache advantage. It's front-loaded with the core function. There's no wasted text, though it could be slightly more structured (e.g., bullet points for parameters).
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 2 parameters with 0% schema coverage, no annotations, and no output schema, the description does a fair job. It covers the tool's purpose, basic requirements, and a reliability note. However, for a web data tool with siblings like scraping alternatives, it lacks details on output format, error cases, or performance expectations, making it minimally adequate but not 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?
Schema description coverage is 0%, so the schema provides no parameter details. The description adds meaning by explaining that 'url' must be a 'valid Facebook company URL' and 'num_of_reviews' specifies the 'number of reviews' to fetch. This clarifies the purpose of both parameters beyond their names, but doesn't provide format examples (e.g., URL patterns) or constraints (e.g., max reviews). It partially compensates for the low 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 clearly states the tool's purpose: 'Quickly read structured Facebook company reviews data.' It specifies the verb ('read') and resource ('Facebook company reviews data'), and distinguishes it from generic scraping tools by mentioning structured data and cache lookup. However, it doesn't explicitly differentiate from sibling tools like 'web_data_facebook_posts' or 'web_data_facebook_events' beyond the 'reviews' focus.
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 provides some usage context: 'Requires a valid Facebook company URL and number of reviews' and compares it to scraping ('more reliable than scraping'). It implies when to use this tool (for Facebook company reviews) but doesn't explicitly state when not to use it or name alternatives among siblings (e.g., vs. 'scrape_as_html' for unstructured data). The guidance is helpful but not comprehensive.
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 key behavioral traits: it's a read operation ('read'), uses caching ('cache lookup'), and offers reliability benefits ('more reliable than scraping'). However, it lacks details on error handling, rate limits, authentication needs, or what 'structured data' entails. It adds value but leaves gaps for a tool with no annotation coverage.
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 concise and well-structured in three sentences: purpose, requirement, and benefit. Each sentence adds value without redundancy. It could be slightly more front-loaded by integrating the requirement into the purpose, but overall it's efficient with zero waste.
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 no annotations, 0% schema coverage, and no output schema, the description is moderately complete. It covers the core purpose, input requirement, and a key behavioral trait (caching/reliability). However, for a tool that reads data, it lacks details on output format, error cases, or performance characteristics, which are important given the complexity implied by 'structured data' and caching.
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 coverage is 0%, with one parameter ('url') documented only by type. The description adds semantic context: 'Requires a valid github repository file URL,' specifying the URL must be for a GitHub repository file. This clarifies beyond the schema's generic 'uri' format. However, it doesn't detail URL format examples or constraints, leaving some ambiguity.
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's purpose: 'Quickly read structured github repository data.' It specifies the verb ('read'), resource ('github repository data'), and quality ('structured'). However, it doesn't explicitly differentiate from sibling tools like 'scrape_as_markdown' or 'scrape_as_html' that might also read GitHub content, though the mention of 'cache lookup' hints at a distinction.
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 provides some usage context: 'Requires a valid github repository file URL' and implies when to use it ('can be more reliable than scraping'). However, it doesn't explicitly state when to choose this tool over alternatives like 'scrape_as_markdown' for GitHub files or other web_data_* tools for different sources. The guidance is implied but not comprehensive.
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 key behavioral traits: it's a read operation ('read'), uses caching ('cache lookup'), and offers reliability benefits over scraping. However, it doesn't cover potential limitations like rate limits, authentication needs, error handling, or what 'structured data' entails in the output. The description adds value but leaves gaps in 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 appropriately sized with three concise sentences. It's front-loaded with the core purpose, followed by requirements and behavioral context. There's minimal waste, though the second sentence could be integrated more smoothly. Overall, it's efficient and well-structured for its length.
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 complexity (data extraction from a specific platform), no annotations, no output schema, and low parameter schema coverage, the description is moderately complete. It covers purpose, input requirements, and a key behavioral advantage. However, it lacks details on output format, error cases, caching specifics, and comparison to siblings, leaving room for improvement in contextual coverage.
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 1 parameter with 0% description coverage, so the description must compensate. It specifies that the 'url' parameter must be 'a valid Google play store app URL,' adding crucial semantic context beyond the schema's URI format. However, it doesn't detail URL format examples or validation rules. Given the low schema coverage, this provides basic but incomplete parameter guidance.
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's purpose: 'Quickly read structured Google play store data.' It specifies the verb ('read') and resource ('Google play store data'), and distinguishes it from generic scraping tools by mentioning structured data. However, it doesn't explicitly differentiate from sibling tools like 'web_data_apple_app_store' beyond the platform specificity.
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 provides some usage context: 'Requires a valid Google play store app URL' and 'This can be a cache lookup, so it can be more reliable than scraping.' This implies when to use it (for Google Play Store data with URLs) and hints at advantages over scraping. However, it lacks explicit guidance on when to choose this tool versus alternatives like 'scrape_as_html' or other web_data_* siblings, beyond the reliability claim.
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?
No annotations are provided, so the description carries the full burden. It discloses that this is a read operation ('read'), mentions reliability ('more reliable than scraping'), and hints at caching behavior ('can be a cache lookup'). However, it lacks details on rate limits, authentication needs, error conditions, or what 'structured data' entails. The disclosure is partial but adds some value 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with three sentences that each add value: stating the purpose, specifying the URL requirement, and explaining reliability. It's front-loaded with the core purpose. There's no wasted text, though it could be slightly more structured for clarity.
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 complexity (read operation with caching), no annotations, no output schema, and low schema coverage, the description is somewhat complete but has gaps. It covers the basic purpose, parameter semantics, and behavioral hints, but lacks details on output format, error handling, or explicit differentiation from siblings. It's adequate but not fully comprehensive for informed tool 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?
The schema has 1 parameter with 0% description coverage. The description adds meaning by specifying that the 'url' must be 'a valid Instagram URL,' which clarifies the parameter's purpose beyond the schema's generic 'uri' format. However, it doesn't detail URL format examples or constraints, leaving some ambiguity. With low schema coverage, the description compensates partially but not fully.
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's purpose: 'Quickly read structured Instagram comments data.' It specifies the verb ('read'), resource ('Instagram comments data'), and key characteristic ('structured'). However, it doesn't explicitly differentiate from sibling tools like 'web_data_instagram_posts' or 'web_data_instagram_profiles' beyond the comments focus.
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 provides some usage context: 'Requires a valid Instagram URL' and mentions it 'can be more reliable than scraping,' which implies an alternative to scraping tools. However, it doesn't explicitly state when to use this tool versus alternatives like 'scrape_as_html' or other web_data_* tools, nor does it provide exclusions or prerequisites beyond the URL requirement.
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?
No annotations are provided, so the description carries the full burden. It discloses that the tool reads data (implied read-only), requires a valid URL, and may use caching for reliability. However, it doesn't cover other behavioral aspects like rate limits, error handling, authentication needs, or what 'structured data' entails in the output. The description adds some value but leaves significant gaps.
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 concise with three sentences that each add value: stating the purpose, input requirement, and a behavioral trait (caching). It's front-loaded with the core purpose. There's minimal waste, though it could be slightly more structured (e.g., bullet points).
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 no annotations, no output schema, and low schema coverage (0%), the description provides basic context: purpose, input requirement, and a reliability hint. However, for a tool that reads data from a specific source, it lacks details on output format, error cases, or how it differs from other data-fetching siblings. It's minimally adequate but has clear gaps in completeness.
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 0%, with one parameter ('url') documented only by type and format in the schema. The description adds meaning by specifying 'Requires a valid yahoo finance business URL,' clarifying the expected content of the URL parameter. This compensates partially for the low schema coverage, but doesn't provide examples or further details on URL format or constraints.
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's purpose: 'Quickly read structured yahoo finance business data.' It specifies the verb ('read'), resource ('yahoo finance business data'), and quality ('structured'). However, it doesn't explicitly distinguish this from sibling tools like 'extract' or other web_data_* tools that might also read structured data from different sources.
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 provides some usage context: 'Requires a valid yahoo finance business URL' and 'This can be a cache lookup, so it can be more reliable than scraping.' This implies when to use it (for Yahoo Finance business data) and hints at an advantage over scraping tools. However, it doesn't explicitly state when to choose this over other sibling tools like 'scrape_as_html' or 'web_data_*' tools for other platforms.
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 key behavioral traits: advanced content extraction, ability to handle bot detection/CAPTCHA, and Markdown output format. However, it lacks details on rate limits, error handling, authentication needs, or what 'advanced options' entail, which are important 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences that efficiently convey purpose and key capabilities without waste. It's front-loaded with the main function and follows with the unlocking feature. A minor improvement could be integrating the Markdown output more seamlessly, but overall it's 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?
Given no annotations, 1 parameter with 0% schema coverage, and no output schema, the description is moderately complete. It covers the core purpose and some behavioral aspects but lacks details on parameters, error cases, or output structure. For a tool with potential complexity (handling bot detection), more context would be helpful.
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 input schema has 1 parameter (url) with 0% description coverage, so the description must compensate. It clarifies that the url is for a 'single webpage' and implies it's for scraping, adding meaningful context beyond the schema's URI format. However, it doesn't detail URL constraints or what 'advanced options' might be, keeping it from a perfect score.
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 scrapes a single webpage URL with advanced content extraction and returns results in Markdown. It specifies the verb (scrape), resource (webpage), and output format (Markdown), which distinguishes it from HTML-based scraping siblings. However, it doesn't explicitly differentiate from all siblings like 'extract' or domain-specific 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 for webpages with bot detection or CAPTCHA ('unlock any webpage'), suggesting when this tool might be preferred over simpler scraping tools. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling tools, leaving some ambiguity.
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 need for a known selector, which is useful context, but fails to describe critical behavioral traits such as what happens after clicking (e.g., page navigation, element state changes), error handling (e.g., if selector is invalid), or performance implications (e.g., delays). For a mutation tool with zero annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with two sentences that directly convey the action and usage guidance without any wasted words. Every sentence earns its place by providing essential information efficiently.
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 complexity (a mutation action in a browser context) and the absence of annotations and output schema, the description is incomplete. It covers the basic purpose and usage but lacks details on behavioral outcomes, error cases, and return values. This is adequate as a minimum viable description but has clear gaps for effective tool invocation.
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% description coverage, with the 'selector' parameter fully documented in the schema. The description adds minimal value beyond the schema by implying the selector must be known, but does not provide additional semantics like format examples or constraints. Given the high schema coverage, 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 states the action ('Click on an element') and the resource ('an element'), making the purpose specific and understandable. However, it does not explicitly distinguish this tool from potential sibling tools like 'scraping_browser_navigate' or 'scraping_browser_type', which are also interaction tools but for different actions, so it lacks sibling differentiation.
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 when to use this tool ('unless you know the element selector') and suggests alternatives ('you can use other tools to find those'), such as 'scraping_browser_get_html' or 'scraping_browser_links' for selector discovery. It does not explicitly state when not to use it or list all alternatives, but the guidance is sufficient for effective usage.
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 a recommendation for validation but lacks details on critical behaviors: it doesn't specify if this is a read-only operation, what the output format is (e.g., list of objects with text and selectors), potential errors, or performance implications. The description adds minimal context beyond the basic action, leaving gaps for an AI agent.
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 concise and front-loaded, with the core purpose stated first in one sentence and a usage tip in the second. Both sentences earn their place by providing essential information without waste. It could be slightly improved by integrating the guidance more seamlessly, but it's efficient overall.
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 complexity (a scraping operation with no parameters but potential behavioral nuances), the description is minimally adequate. It lacks an output schema, so the description doesn't explain return values, and with no annotations, it misses behavioral details. It covers the basic purpose and a usage tip but leaves out specifics on output format or error handling, making it incomplete for optimal agent use.
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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate. A baseline of 4 is applied since it doesn't need to compensate for any schema gaps, and it avoids redundancy.
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's purpose: 'Get all links on the current page, text and selectors.' It specifies the verb ('Get'), resource ('links'), and scope ('on the current page'), making the action clear. However, it doesn't explicitly differentiate from sibling tools like 'scraping_browser_get_html' or 'scraping_browser_get_text', which could also retrieve page content, so it falls short of a perfect score.
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 guidance by recommending to 'call the links tool to check that your click target is valid,' implying this tool is for link discovery before interactions like clicking. It suggests a workflow but doesn't explicitly state when not to use it or name alternatives among siblings, such as 'scraping_browser_click' for direct actions, so it's not fully comprehensive.
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 of behavioral disclosure. It adds useful context: the requirement for a valid URL, the cache lookup behavior, and reliability advantage over scraping. However, it doesn't cover critical aspects like error handling, rate limits, authentication needs, or what 'structured data' entails in the output, leaving gaps for a mutation/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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with three sentences that are front-loaded with the core purpose. Each sentence adds value: purpose, parameter requirement, and behavioral insight. It avoids redundancy, though minor trimming (e.g., 'Quickly' is subjective) could make it a 5.
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 complexity (data extraction), no annotations, no output schema, and low schema coverage, the description is moderately complete. It covers purpose, parameter semantics, and a key behavioral trait (cache reliability), but lacks details on output structure, error cases, or integration with siblings, making it adequate but with clear gaps.
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 description adds meaningful semantics for the single parameter: 'Requires a valid booking hotel listing URL.' This clarifies that the 'url' parameter must be a booking hotel listing URL, not just any URI. With 0% schema description coverage and only 1 parameter, this compensation is effective, though it could specify URL format examples for a 5.
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's purpose: 'Quickly read structured booking hotel listings data.' It specifies the verb ('read'), resource ('structured booking hotel listings data'), and scope ('booking hotel listings'). However, it doesn't explicitly differentiate from siblings like 'web_data_zillow_properties_listing' or general scraping tools, which would require a 5.
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 provides some usage context: 'Requires a valid booking hotel listing URL' and 'This can be a cache lookup, so it can be more reliable than scraping.' This implies when to use it (for booking hotel URLs) and hints at an advantage over scraping tools, but it doesn't explicitly name alternatives like 'scrape_as_html' or specify when-not-to-use scenarios, falling short of a 4 or 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 provided, the description carries the full burden. It discloses that it's a read operation ('read'), requires a valid URL, and mentions caching behavior ('cache lookup') and reliability advantage over scraping. However, it lacks details on rate limits, error handling, authentication needs, or what 'structured data' specifically includes.
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 brief and front-loaded with the core purpose. All three sentences add value: stating the action, parameter requirement, and behavioral trait. It avoids redundancy but could be slightly more structured (e.g., separating requirements from benefits).
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 no annotations, no output schema, and low schema coverage, the description provides basic purpose and parameter guidance but lacks details on output format, error cases, or performance characteristics. It's minimally viable for a simple read tool but leaves gaps in behavioral context.
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 has 1 parameter with 0% description coverage. The description adds meaningful context: 'Requires a valid etsy product URL.' This clarifies the 'url' parameter's purpose and format beyond the schema's basic 'uri' format. Since there's only one parameter, the baseline is high, and the description adequately compensates for the low 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 clearly states the tool's purpose: 'Quickly read structured etsy product data.' It specifies the verb ('read'), resource ('etsy product data'), and key characteristic ('structured'). However, it doesn't explicitly differentiate from sibling tools like 'web_data_ebay_product' or 'scrape_as_html' beyond mentioning 'more reliable than 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 provides some usage context: 'Requires a valid etsy product URL' and 'This can be a cache lookup, so it can be more reliable than scraping.' This implies when to use it (for Etsy products, preferring reliability) but doesn't explicitly state when to choose alternatives like 'scrape_as_html' or other web_data_* tools for different sites.
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 that this is a read operation ('read'), mentions reliability aspects ('cache lookup', 'more reliable than scraping'), and notes a prerequisite ('Requires a valid... URL'). However, it lacks details on error handling, rate limits, authentication needs, or what 'structured data' specifically includes.
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 extremely concise with three short sentences that are front-loaded with the core purpose. Every sentence adds value: the first states what it does, the second specifies the input requirement, and the third explains a key behavioral trait. Zero 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 no annotations, 1 parameter, no output schema, and low schema coverage, the description provides basic purpose and usage context but is incomplete. It doesn't explain what 'structured data' returns, error conditions, or how the cache lookup works. Given the complexity (reading web data) and lack of structured fields, more behavioral details would be helpful.
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 1 parameter with 0% description coverage. The description adds meaning by specifying that the 'url' parameter must be 'a valid Facebook marketplace listing URL,' which clarifies the expected content beyond just a URI format. This compensates somewhat for the low schema coverage, but doesn't provide examples or format details.
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's purpose: 'Quickly read structured Facebook marketplace listing data.' It specifies the verb ('read'), resource ('Facebook marketplace listing data'), and quality ('structured'). However, it doesn't explicitly differentiate from sibling tools like 'scrape_as_html' or 'scrape_as_markdown' beyond mentioning it's 'more reliable than 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 provides some usage context: 'Requires a valid Facebook marketplace listing URL' and 'This can be a cache lookup, so it can be more reliable than scraping.' This implies when to use it (for Facebook marketplace listings, preferring reliability) but doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling scraping tools.
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 of behavioral disclosure. It mentions the cache lookup capability and reliability advantage over scraping, which are valuable behavioral insights. However, it doesn't cover important aspects like rate limits, authentication requirements, error conditions, or what 'structured data' specifically means in the response format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just two sentences that each add distinct value. The first sentence establishes the core purpose, and the second provides important behavioral context about cache reliability. There's zero wasted language, and both sentences earn their 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 no annotations, no output schema, and 0% schema description coverage, the description provides adequate basic purpose and usage context but leaves significant gaps. For a data extraction tool with many sibling alternatives, it should ideally explain more about the structure of returned data, error handling, and parameter requirements to be truly complete.
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?
The schema has 0% description coverage for its single parameter (url), and the tool description provides no information about what format the URL should be, whether it needs to be a specific LinkedIn company profile URL pattern, or any constraints. With low schema coverage and no parameter guidance in the description, this represents a significant gap.
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's purpose: 'Quickly read structured linkedin company profile data' specifies the verb (read), resource (LinkedIn company profile data), and key characteristic (structured). It distinguishes from generic scraping tools by mentioning structured data extraction, though it doesn't explicitly differentiate from all sibling tools like 'web_data_linkedin_person_profile' or 'web_data_crunchbase_company'.
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 for when to use this tool: 'This can be a cache lookup, so it can be more reliable than scraping' suggests it should be preferred over scraping tools when reliability is important. It implicitly contrasts with scraping siblings but doesn't explicitly name alternatives or provide when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 of behavioral disclosure. It adds valuable context about the cache mechanism and reliability advantage over scraping, which helps the agent understand performance characteristics. However, it doesn't disclose important behavioral traits like rate limits, authentication requirements, error conditions, or what 'structured data' specifically means in the return format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise with just two sentences that each earn their place. The first sentence establishes the core purpose, the second adds crucial behavioral context about cache reliability. No wasted words, well-structured with the most important information first.
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 moderate complexity (single parameter but no output schema and no annotations), the description is adequate but incomplete. It covers the purpose and reliability advantage well, but fails to document the parameter semantics and lacks details about the return format. For a data extraction tool with no output schema, more information about what 'structured data' means would be helpful.
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?
The schema description coverage is 0% (no parameter descriptions in schema), and the tool has 1 parameter. The description provides no information about the 'url' parameter - what format it expects, what types of LinkedIn URLs are valid, or any constraints. The description doesn't compensate for the complete lack of parameter documentation in 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 states the tool's purpose: 'Quickly read structured linkedin posts data' specifies the verb (read), resource (linkedin posts data), and key attribute (structured). It distinguishes from siblings like 'scrape_as_html' by emphasizing structured data extraction rather than raw scraping. However, it doesn't explicitly differentiate from other LinkedIn-specific tools like 'web_data_linkedin_company_profile'.
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 for when to use this tool: 'This can be a cache lookup, so it can be more reliable than scraping' implies it should be preferred over scraping tools when reliability is important. It distinguishes from scraping siblings by highlighting the cache advantage. However, it doesn't explicitly state when NOT to use it or name specific alternatives.
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 that this is a read operation ('read'), involves a cache lookup (implying potential performance benefits and data freshness considerations), and compares reliability to scraping. However, it lacks details on error handling, rate limits, authentication needs, or output format, which are important for a web data 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 brief and front-loaded, with three concise sentences that each add value: stating the purpose, input requirement, and reliability benefit. There's no wasted text, though it could be slightly more structured (e.g., bullet points) for clarity.
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 moderate complexity (web data reading with caching), no annotations, no output schema, and low schema coverage, the description is somewhat incomplete. It covers the core purpose and input but lacks details on behavioral traits (e.g., caching behavior, error cases) and output format, which are important for effective use.
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 has 1 parameter with 0% description coverage, so the description must compensate. It specifies that the 'url' parameter must be 'a valid reuter news report URL,' adding crucial semantic context beyond the schema's generic URI format. This clearly defines the expected input, though it doesn't detail URL structure or validation rules.
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's purpose: 'Quickly read structured reuter news data' specifies the verb ('read'), resource ('reuter news data'), and key characteristic ('structured'). It distinguishes from siblings by focusing on Reuters news specifically, though it doesn't explicitly contrast with other web_data_* tools for different sources.
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 provides some usage context: 'Requires a valid reuter news report URL' and mentions it 'can be more reliable than scraping,' which implicitly suggests using this instead of scraping tools for Reuters content. However, it doesn't explicitly state when to use this versus other Reuters-specific tools (none listed) or general scraping alternatives, leaving some ambiguity.
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 of behavioral disclosure. It adds useful context: 'Quickly' suggests performance characteristics, 'structured' describes output format, 'cache lookup' explains the data source method, and 'more reliable than scraping' compares reliability. However, it doesn't cover important aspects like rate limits, authentication needs, error conditions, or what 'structured data' specifically entails (e.g., JSON format, fields included).
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 extremely concise and well-structured: three short sentences that each add distinct value (purpose, requirement, behavioral context). It's front-loaded with the core purpose, has zero wasted words, and efficiently communicates key information without 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?
Given the tool's moderate complexity (data retrieval with reliability considerations), no annotations, no output schema, and minimal parameter documentation, the description is somewhat complete but has gaps. It covers the core purpose, basic requirement, and key behavioral advantage (cache reliability), but lacks details about output format, error handling, limitations, and comprehensive parameter guidance that would be helpful for an AI 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 input schema has 1 parameter with 0% description coverage (no schema descriptions). The description adds some semantic value: 'Requires a valid Tiktok video URL' clarifies that the 'url' parameter must be a Tiktok video URL (not just any URI). However, it doesn't specify URL format requirements (e.g., must include video ID), validation rules, or examples. Given the low schema coverage, the description provides basic but incomplete parameter guidance.
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's purpose: 'Quickly read structured Tiktok comments data' specifies the verb (read), resource (Tiktok comments data), and key characteristic (structured). It distinguishes from siblings like 'web_data_tiktok_posts' by focusing on comments, but doesn't explicitly differentiate from other comment-reading tools like 'web_data_instagram_comments' or 'web_data_youtube_comments' beyond the Tiktok platform.
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 provides some usage context: 'Requires a valid Tiktok video URL' gives a prerequisite, and 'This can be a cache lookup, so it can be more reliable than scraping' implies when to prefer this tool over scraping alternatives. However, it doesn't explicitly name when to use this vs. specific sibling tools (like 'scrape_as_html' or 'web_data_tiktok_posts'), nor does it provide clear exclusions or detailed alternative 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 provided, the description carries the full burden. It discloses that this is a read operation ('read'), mentions reliability aspects ('can be more reliable than scraping'), and hints at caching behavior ('This can be a cache lookup'). However, it lacks details on rate limits, authentication needs, error conditions, or response format, leaving gaps for a mutation-free 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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by prerequisites and behavioral notes. Each sentence adds value without redundancy, making it efficient and well-structured for quick comprehension.
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 moderate complexity (read-only, single parameter), no annotations, and no output schema, the description is partially complete. It covers the purpose, URL requirement, and reliability advantage, but lacks details on output structure, error handling, or performance characteristics, which would be helpful for an AI agent to use it effectively.
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 coverage is 0%, so the description must compensate. It adds meaning by specifying the 'url' parameter must be 'a valid product URL with /ip/ in it', which clarifies the expected format beyond the schema's 'uri' format. However, with only one parameter, the baseline is 4, but it doesn't fully explain semantics like what constitutes a 'valid' URL or handling of malformed inputs, so it scores slightly lower.
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's purpose: 'Quickly read structured walmart product data.' It specifies the verb ('read'), resource ('walmart product data'), and characteristic ('structured'). However, it doesn't explicitly differentiate from sibling tools like 'web_data_walmart_seller' or 'web_data_amazon_product', which would be needed for a perfect score.
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 provides some usage context: 'Requires a valid product URL with /ip/ in it.' This implies when to use it (for Walmart product URLs) but doesn't explicitly state when not to use it or mention alternatives like 'scrape_as_html' or 'web_data_walmart_seller'. The guidance is implied rather than comprehensive.
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 of behavioral disclosure. It adds useful context: the requirement for a valid URL, the cache lookup behavior (implying potential speed/reliability benefits), and the comparison to scraping. However, it doesn't cover key behavioral aspects like error handling, rate limits, authentication needs, or what 'structured data' specifically includes (e.g., metadata fields).
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 highly concise and front-loaded: the first sentence states the core purpose, followed by prerequisites and behavioral context. Every sentence adds value without redundancy, 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?
Given the tool's moderate complexity (single parameter, no output schema, no annotations), the description is somewhat complete but has gaps. It covers the purpose, input requirement, and a key behavioral trait (cache lookup), but lacks details on output structure, error cases, or how it differs from sibling tools like 'scrape_as_html' for YouTube data.
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 1 parameter with 0% description coverage. The description adds semantic context by specifying that the 'url' parameter must be 'a valid YouTube video URL,' which clarifies the expected format beyond the schema's generic 'uri' format. This compensates partially for the low schema coverage, but doesn't provide examples or detailed constraints.
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's purpose: 'Quickly read structured YouTube videos data.' It specifies the verb ('read') and resource ('structured YouTube videos data'), making the action clear. However, it doesn't explicitly differentiate from sibling tools like 'web_data_youtube_comments' or 'web_data_youtube_profiles' beyond the 'videos' focus.
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 provides some usage context: 'Requires a valid YouTube video URL' and 'This can be a cache lookup, so it can be more reliable than scraping.' This implies when to use it (for structured video data with URL) and hints at an advantage over scraping tools. However, it doesn't explicitly state when to choose this over alternatives like 'scrape_as_html' or other web_data tools, nor does it mention 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 that this is a read operation ('read structured zara product data'), mentions reliability due to potential caching, and specifies the URL requirement. However, it lacks details on rate limits, authentication needs, error handling, or what 'structured data' entails (e.g., format, fields).
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 front-loaded with the core purpose in the first sentence, followed by prerequisites and behavioral context in two additional sentences. Each sentence adds essential information without redundancy, making it efficient and well-structured for quick comprehension.
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 moderate complexity (reading web data), no annotations, no output schema, and low schema coverage, the description is adequate but incomplete. It covers the basic purpose, input requirement, and a key behavioral trait (caching reliability), but lacks details on output format, error cases, or performance characteristics, leaving gaps for an AI 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 input schema has 1 parameter with 0% description coverage. The description adds value by specifying that the 'url' must be 'a valid zara product URL,' clarifying its purpose beyond the schema's generic URI format. However, it doesn't provide examples, constraints (e.g., URL patterns), or further semantics, so it partially compensates for the low 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 clearly states the tool's purpose: 'Quickly read structured zara product data.' It specifies the verb ('read') and resource ('structured zara product data'), making it easy to understand. However, it doesn't explicitly differentiate from siblings like 'web_data_amazon_product' or 'scrape_as_html' beyond mentioning it's 'more reliable than 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 provides some usage context: 'Requires a valid zara product URL' and 'This can be a cache lookup, so it can be more reliable than scraping.' This implies when to use it (for Zara products, preferring reliability) but doesn't explicitly state when not to use it or name alternatives among siblings like 'scrape_as_html' for non-Zara URLs or when caching isn't needed.
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 of behavioral disclosure. It adds useful context: the cache lookup behavior and reliability advantage over scraping. However, it doesn't cover other important aspects like rate limits, authentication needs, error conditions, or what 'structured data' specifically means in the output. For a tool with no annotations, this leaves significant gaps in behavioral understanding.
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 extremely concise and front-loaded: three sentences with zero waste. The first sentence states the core purpose, the second specifies the key parameter requirement, and the third adds important behavioral context. Every sentence earns its place without 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?
Given the tool's complexity (reading structured data from Amazon), no annotations, no output schema, and 0% schema description coverage, the description is minimally adequate. It covers the basic purpose, parameter requirement, and cache advantage, but lacks details on output format, error handling, limitations, and how it differs from sibling tools. This leaves the agent with significant gaps in understanding how to use it effectively.
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 has 1 parameter with 0% description coverage. The description adds meaningful semantics: it specifies the parameter must be 'a valid product URL with /dp/ in it,' which provides crucial format requirements not in the schema. This compensates well for the low schema coverage, though it doesn't detail other constraints like URL validation rules or examples.
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's purpose: 'Quickly read structured amazon product review data.' It specifies the verb ('read'), resource ('amazon product review data'), and key characteristic ('structured'). However, it doesn't explicitly differentiate from sibling tools like 'web_data_amazon_product' or 'scrape_as_html', which would be needed for a score of 5.
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 provides some usage context: 'Requires a valid product URL with /dp/ in it' and 'This can be a cache lookup, so it can be more reliable than scraping.' This implies when to use it (for Amazon product reviews with specific URLs) and hints at advantages over scraping alternatives. However, it doesn't explicitly name when NOT to use it or directly compare to specific sibling tools like 'scrape_as_html' or 'web_data_amazon_product', keeping it at an implied rather than explicit level.
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 that this is a read operation ('read'), mentions reliability aspects ('can be more reliable than scraping'), and hints at caching behavior ('This can be a cache lookup'). However, it lacks details on rate limits, authentication needs, error handling, or what 'structured data' specifically entails in the output.
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 highly concise and front-loaded: the first sentence states the core purpose, followed by key requirements and behavioral notes. Every sentence adds value without redundancy, 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?
Given no annotations, no output schema, and low schema coverage, the description provides basic purpose and parameter guidance but lacks details on output format, error cases, or performance characteristics. It's minimally viable for a simple read tool but leaves gaps in behavioral context that could aid agent decision-making.
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 input schema has 1 parameter with 0% description coverage. The description adds meaningful context: it specifies that the 'url' parameter must be 'a valid apple app store app URL,' clarifying the expected format and domain. This compensates well for the lack of schema descriptions, though it doesn't detail URL validation rules or examples.
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's purpose: 'Quickly read structured apple app store data.' It specifies the verb ('read'), resource ('apple app store data'), and key characteristic ('structured'). However, it doesn't explicitly differentiate from sibling tools like 'web_data_google_play_store' beyond mentioning the Apple App Store focus.
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 provides some usage context: 'Requires a valid apple app store app URL' and mentions it 'can be more reliable than scraping,' which implies it's an alternative to scraping tools. However, it doesn't explicitly state when to use this versus other web_data_* siblings or scraping tools, nor does it provide clear exclusions or prerequisites beyond the URL requirement.
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 full burden. It discloses key behavioral traits: the cache lookup mechanism and reliability advantage over scraping. However, it doesn't mention rate limits, authentication requirements, error conditions, or what 'structured data' specifically includes. The description adds value but leaves significant behavioral aspects unspecified.
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 perfectly concise: three short sentences with zero wasted words. Each sentence adds distinct value (purpose, parameter requirement, behavioral advantage). It's front-loaded with the core purpose and appropriately sized for a single-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?
Given no annotations, 0% schema description coverage, and no output schema, the description provides adequate basics but has gaps. It covers purpose, parameter semantics, and a key behavioral advantage. However, for a data retrieval tool, it doesn't describe the return format, error handling, or data structure details. The context is minimally complete but could be more comprehensive.
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 description coverage is 0% (no parameter descriptions in schema), but the tool has only 1 parameter. The description adds crucial semantics: 'Requires a valid Facebook event URL' clarifies that the 'url' parameter must be a Facebook event URL specifically, not just any URI. This compensates well for the schema's lack of description, though it could specify URL format expectations more precisely.
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's purpose: 'Quickly read structured Facebook events data' specifies the verb ('read'), resource ('Facebook events data'), and key characteristic ('structured'). It distinguishes from generic scraping tools by mentioning structured data extraction. However, it doesn't explicitly differentiate from all sibling web_data_* tools beyond the Facebook focus.
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 provides some usage context: 'Requires a valid Facebook event URL' gives a prerequisite, and 'This can be a cache lookup, so it can be more reliable than scraping' implies when to prefer this tool over scraping alternatives. However, it doesn't explicitly name when to use this vs. other Facebook-related tools or general scraping siblings, nor does it provide exclusion criteria.
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 that the tool reads data (implied non-destructive), mentions reliability via cache lookup, and hints at performance ('quickly'). However, it lacks details on rate limits, authentication needs, error handling, or what 'structured data' entails. The disclosure is partial but adds some behavioral context 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 front-loaded with the core purpose, followed by key requirements and a reliability note. All three sentences earn their place by adding distinct value: purpose, input requirement, and behavioral insight. It's efficient with zero 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?
Given the tool's moderate complexity (reading web data), no annotations, no output schema, and low schema coverage, the description is minimally adequate. It covers the purpose and parameter semantics but lacks details on output format, error cases, or deeper behavioral traits. It meets basic needs but leaves gaps for an agent to use it effectively in varied contexts.
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 description adds meaningful context for the single parameter: 'Requires a valid Facebook post URL.' This clarifies the 'url' parameter's purpose and format beyond the schema's basic type/format. With 0% schema description coverage and only one parameter, this compensates adequately, though it could specify URL examples or constraints.
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's purpose: 'Quickly read structured Facebook post data.' It specifies the verb ('read'), resource ('Facebook post data'), and key characteristic ('structured'). However, it doesn't explicitly differentiate from siblings like 'web_data_facebook_events' or 'web_data_facebook_marketplace_listings' beyond the 'post' focus.
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 provides some usage context: 'Requires a valid Facebook post URL' and mentions it 'can be more reliable than scraping,' which implies an alternative to scraping tools. However, it doesn't explicitly state when to use this versus siblings like 'scrape_as_html' or 'extract,' nor does it provide clear exclusions or prerequisites beyond the URL requirement.
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 that this is a read operation ('read structured Google shopping data'), mentions reliability advantages ('more reliable than scraping'), and reveals caching behavior ('can be a cache lookup'). However, it doesn't cover error conditions, rate limits, authentication needs, or what 'structured data' specifically entails.
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 extremely concise with three short sentences that each add value: states the core purpose, specifies the required input, and provides behavioral context about caching and reliability. No wasted words, and the most critical information (what it does and what it needs) comes first.
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 single-parameter read tool with no annotations and no output schema, the description provides adequate but minimal coverage. It explains the purpose, input requirement, and some behavioral traits (caching, reliability), but doesn't describe the output format or structure of the returned data, which would be helpful given the lack of output schema.
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?
With 0% schema description coverage for the single parameter 'url', the description compensates well by specifying 'Requires a valid Google shopping product URL.' This adds crucial semantic context about what kind of URL is expected (Google Shopping product URLs specifically), which isn't captured in the schema's generic URI format. For a single parameter tool, this provides adequate guidance.
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's purpose: 'Quickly read structured Google shopping data' specifies the verb ('read') and resource ('Google shopping data'). It distinguishes from many sibling tools by focusing on Google Shopping specifically, though it doesn't explicitly differentiate from other web_data_* tools that target different platforms.
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 provides some usage context: 'Requires a valid Google shopping product URL' and mentions it 'can be more reliable than scraping.' This implies when to use it (for Google Shopping URLs) and hints at advantages over scraping tools, but doesn't explicitly state when to choose this over alternatives like 'scrape_as_html' or other web_data_* tools targeting different platforms.
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 that the tool performs a read operation ('read'), requires a URL input, and may use caching for reliability. However, it lacks details on rate limits, error handling, authentication needs, or what 'structured data' entails. The description adds value but leaves behavioral gaps.
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 highly concise and well-structured: three short sentences that are front-loaded with the core purpose, followed by requirements and behavioral context. Every sentence adds value without redundancy, 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?
Given no annotations, no output schema, and low schema coverage, the description provides basic context but is incomplete. It covers the purpose, input requirement, and a reliability hint, but lacks details on output format, error cases, or integration with sibling tools. For a data-fetching tool, more completeness is needed.
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 input schema has 1 parameter with 0% description coverage. The description adds meaningful semantics: it specifies that the 'url' parameter must be 'a valid Instagram URL.' This compensates well for the schema's lack of documentation, though it doesn't detail URL format examples or constraints.
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's purpose: 'Quickly read structured Instagram profile data.' It specifies the verb ('read'), resource ('Instagram profile data'), and key characteristic ('structured'). However, it doesn't explicitly differentiate from sibling tools like 'web_data_instagram_posts' or 'web_data_instagram_comments' beyond the 'profile' focus.
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 provides some usage context: 'Requires a valid Instagram URL' and 'This can be a cache lookup, so it can be more reliable than scraping.' This implies when to use it (for profile data via URL, preferring reliability) but doesn't explicitly compare to alternatives like 'scrape_as_html' or other web_data_* tools. The guidance is helpful but not comprehensive.
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 full burden. It discloses that this is a read operation ('read'), requires URL input, and mentions cache lookup behavior for reliability. However, it doesn't cover rate limits, authentication needs, error conditions, or what 'structured data' specifically includes, leaving behavioral gaps.
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 concise sentences with zero waste: first states purpose, second specifies requirement, third explains behavioral advantage. Each sentence earns its place by adding distinct value. The description is appropriately sized and front-loaded with the core function.
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 no annotations, 0% schema coverage, and no output schema, the description does reasonably well for a single-parameter tool. It covers purpose, input requirement, and reliability advantage, but lacks details on output format, error handling, or specific limitations compared to siblings. For a data retrieval tool, more output context would be helpful.
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 description coverage is 0%, but the description compensates well: it specifies the single parameter 'url' must be 'a valid Instagram URL' and implies it should point to a reel. This adds crucial semantic context beyond the schema's basic URI format validation, though it doesn't detail URL format examples.
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's purpose: 'Quickly read structured Instagram reel data' specifies the verb ('read'), resource ('Instagram reel data'), and quality ('structured'). It distinguishes from general scraping tools but doesn't explicitly differentiate from sibling Instagram tools like 'web_data_instagram_posts' or 'web_data_instagram_profiles'.
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 provides some usage context: 'Requires a valid Instagram URL' and mentions it's 'more reliable than scraping' due to cache lookup. However, it doesn't explicitly state when to use this vs. other Instagram data tools or general scraping alternatives, leaving the agent to infer based on the 'reel data' focus.
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 key behavioral traits: it's a read operation ('read'), requires a specific input ('valid reddit post URL'), and has reliability characteristics ('cache lookup, more reliable than scraping'). However, it doesn't cover rate limits, error conditions, authentication needs, or what 'structured data' specifically includes.
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 extremely concise with three short sentences that each add value: states the purpose, specifies the requirement, and explains the reliability advantage. It's front-loaded with the core function and wastes no words. Every sentence earns its place by providing distinct, useful information.
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 moderate complexity (single parameter, no output schema, no annotations), the description is adequate but has gaps. It covers the basic purpose, input requirement, and reliability aspect, but doesn't explain what 'structured reddit posts data' includes (fields, format) or potential limitations. For a data retrieval tool, more detail about the output would be helpful since there's no output schema.
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 has 1 parameter with 0% description coverage. The description adds meaningful semantics: it specifies that the 'url' parameter must be 'a valid reddit post URL.' This clarifies the expected format and content beyond the schema's basic 'uri' format. Since there's only one parameter and the description fully explains it, this compensates well for the low 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 clearly states the tool's purpose: 'Quickly read structured reddit posts data.' It specifies the verb ('read') and resource ('structured reddit posts data'), and distinguishes it from scraping tools by mentioning it's a cache lookup. However, it doesn't explicitly differentiate from other web_data_* tools that also read structured data from different platforms.
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 provides some usage context: 'Requires a valid reddit post URL' and 'This can be a cache lookup, so it can be more reliable than scraping.' This implies when to use it (for Reddit posts, preferring reliability over fresh scraping) and suggests alternatives (scraping tools). However, it doesn't explicitly state when NOT to use it or name specific alternative tools among siblings.
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 of behavioral disclosure. It adds useful context: 'This can be a cache lookup, so it can be more reliable than scraping,' which hints at performance and reliability traits. However, it lacks details on error handling, rate limits, authentication needs, or what 'structured data' entails, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and well-structured: three short sentences that are front-loaded with the core purpose, followed by prerequisites and behavioral context. Every sentence adds value without redundancy, 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?
Given the tool's complexity (data retrieval with potential caching), no annotations, no output schema, and low schema coverage, the description is minimally adequate. It covers the basic purpose and a key behavioral trait but lacks details on output format, error cases, or deeper usage scenarios, leaving room for improvement in completeness.
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 input schema has 1 parameter with 0% description coverage, so the description must compensate. It specifies 'Requires a valid Tiktok post URL,' which clarifies the 'url' parameter's purpose and format. This adds meaningful semantics beyond the bare schema, though it doesn't detail URL validation rules or examples, preventing a perfect score.
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's purpose: 'Quickly read structured Tiktok post data.' It specifies the verb ('read'), resource ('Tiktok post data'), and quality ('structured'). However, it doesn't explicitly differentiate from sibling tools like 'web_data_tiktok_comments' or 'web_data_tiktok_profiles' beyond the 'post' focus, which prevents a perfect score.
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 provides some usage context: 'Requires a valid Tiktok post URL' and mentions it 'can be more reliable than scraping,' which implies it's a preferred method over scraping tools. However, it doesn't explicitly state when to use this versus alternatives like 'scrape_as_html' or other web_data_* tools, leaving the guidance somewhat implied rather than explicit.
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 key behavioral traits: it's a read operation ('read'), uses a cache ('cache lookup'), and offers reliability benefits ('more reliable than scraping'). However, it doesn't mention potential limitations like rate limits, authentication needs, or error conditions, which are important for a web data 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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by prerequisites and behavioral context. Every sentence adds value without redundancy, making it efficient 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?
Given the tool's complexity (web data extraction), lack of annotations, and no output schema, the description is moderately complete. It covers purpose, prerequisites, and reliability but lacks details on output format, error handling, or performance characteristics, which are important for an AI agent to use it effectively.
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 input schema has 1 parameter with 0% description coverage, so the description must compensate. It adds meaning by specifying that the 'url' parameter must be 'a valid Tiktok profile URL,' which clarifies the expected format beyond the schema's generic 'uri' format. This is helpful, but it doesn't provide examples or further constraints.
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's purpose: 'Quickly read structured Tiktok profiles data.' It specifies the verb ('read'), resource ('structured Tiktok profiles data'), and scope ('quickly'). However, it doesn't explicitly differentiate from sibling tools like 'web_data_tiktok_posts' or 'web_data_tiktok_comments', which prevents a perfect score.
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 provides some usage context: 'Requires a valid Tiktok profile URL' and mentions it 'can be more reliable than scraping,' which implies it's a preferred alternative to scraping tools. However, it doesn't explicitly state when to use this tool versus specific siblings like 'scrape_as_html' or 'web_data_tiktok_posts,' leaving some ambiguity.
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 of behavioral disclosure. It adds useful context: the cache lookup capability and reliability advantage over scraping. However, it doesn't disclose important behavioral traits like rate limits, authentication needs, error conditions, or what 'structured data' specifically means in the response format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and well-structured: three short sentences that each add distinct value (purpose, requirement, advantage). No wasted words, and the most critical information ('Quickly read structured Tiktok shop data') is front-loaded.
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 moderate complexity (single parameter but no output schema), the description is adequate but has gaps. It covers the basic purpose and parameter semantics well, but without annotations or output schema, it should ideally describe the return format more explicitly. The cache behavior mention is helpful but doesn't fully compensate for missing structured response details.
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 has 0% description coverage for its single parameter 'url'. The description compensates by specifying it must be 'a valid Tiktok shop product URL,' adding crucial semantic context about the expected URL type. This goes beyond the schema's basic URI format validation, though it doesn't provide examples or format specifics.
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's purpose: 'Quickly read structured Tiktok shop data' specifies the verb (read), resource (Tiktok shop data), and scope (structured). It distinguishes from siblings like 'scrape_as_html' by emphasizing structured data extraction rather than raw scraping. However, it doesn't explicitly differentiate from other web_data_* tools beyond the Tiktok shop focus.
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 provides some usage context: 'Requires a valid Tiktok shop product URL' and mentions it 'can be more reliable than scraping.' This implies when to use it (for Tiktok shop product data) and suggests an advantage over scraping tools. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling tools.
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 key behavioral traits: it's a read operation ('read'), requires a specific input ('valid walmart seller URL'), and may use caching for reliability. However, it lacks details on error handling, rate limits, authentication needs, or output format. The description adds value but leaves gaps for a tool with no structured annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded: three short sentences with zero waste. The first sentence states the core purpose, the second specifies the required input, and the third adds valuable behavioral context. Every sentence earns its place, 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?
Given the tool's complexity (data extraction with caching), no annotations, no output schema, and 0% schema description coverage, the description is moderately complete. It covers purpose, input requirements, and a key behavioral trait (caching), but lacks details on output structure, error cases, or performance characteristics. It's adequate but has clear gaps for informed tool selection.
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 input schema has 1 parameter with 0% description coverage, but the description compensates well: it specifies that the 'url' must be a 'valid walmart seller URL,' adding crucial semantic context beyond the schema's generic 'uri' format. This clarifies the parameter's purpose and constraints, making it highly informative despite the schema's lack of descriptions.
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's purpose: 'Quickly read structured walmart seller data.' It specifies the verb ('read'), resource ('walmart seller data'), and scope ('structured'). However, it doesn't explicitly differentiate from siblings like 'web_data_walmart_product' or 'scrape_as_html', which could handle similar data extraction tasks.
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 provides some usage context: 'Requires a valid walmart seller URL' and 'This can be a cache lookup, so it can be more reliable than scraping.' This implies when to use it (for Walmart seller URLs, preferring reliability) but doesn't explicitly state when not to use it or name alternatives among the many sibling scraping tools. The guidance is helpful but not comprehensive.
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?
No annotations are provided, so the description carries the full burden. It discloses that it 'can be a cache lookup' and 'more reliable than scraping,' which adds useful behavioral context about performance and reliability. However, it lacks details on error handling, rate limits, authentication needs, or what 'structured data' entails, leaving gaps for a tool with no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: three concise sentences with zero waste. The first sentence states the purpose, the second specifies the parameter requirement, and the third adds behavioral context, all efficiently 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?
Given the tool's moderate complexity (single parameter, no annotations, no output schema), the description is partially complete. It covers the purpose, parameter semantics, and some behavioral traits, but lacks details on output format, error cases, or explicit differentiation from siblings, leaving room for improvement.
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 description adds meaning beyond the input schema: it specifies that the 'url' parameter must be 'a valid X post URL,' clarifying the expected content. With 0% schema description coverage and only 1 parameter, this compensates well, though it doesn't detail URL format constraints beyond 'valid.'
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's purpose: 'Quickly read structured X post data.' It specifies the verb ('read') and resource ('structured X post data'), making it distinct from generic scraping tools. However, it doesn't explicitly differentiate from sibling tools like 'web_data_facebook_posts' or 'web_data_linkedin_posts' beyond the 'X' platform specificity.
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 provides some usage context: 'Requires a valid X post URL' and 'This can be a cache lookup, so it can be more reliable than scraping.' This implies when to use it (for X posts, preferring reliability) but doesn't explicitly state when not to use it or name alternatives among the many sibling scraping tools, leaving the agent to infer.
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 of behavioral disclosure. It mentions 'cache lookup' and 'more reliable than scraping,' which adds useful context about performance and reliability. However, it doesn't cover other important behaviors like rate limits, authentication needs, error handling, or what 'structured data' specifically means in the return format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and well-structured: three short sentences that each add distinct value (purpose, parameter requirement, behavioral advantage). There's no wasted language, and key information is front-loaded.
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 moderate complexity (reading structured data from a specific website), no annotations, no output schema, and 0% schema description coverage, the description is minimally adequate. It covers the basic purpose and parameter but lacks details about return format, error cases, or how it differs from other web data tools. The mention of 'cache lookup' adds some helpful context.
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 description adds meaningful context for the single parameter: 'Requires a valid zillow properties listing URL.' This clarifies that the 'url' parameter must be a Zillow properties listing URL, not just any URI. With 0% schema description coverage and only one parameter, this compensates adequately, though it doesn't specify URL format examples or validation details.
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's purpose: 'Quickly read structured zillow properties listing data.' It specifies the verb ('read'), resource ('zillow properties listing data'), and key characteristic ('structured'). However, it doesn't explicitly differentiate from sibling tools like 'scrape_as_html' or 'web_data_amazon_product' beyond mentioning 'more reliable than 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 provides some usage context: 'Requires a valid zillow properties listing URL' and 'This can be a cache lookup, so it can be more reliable than scraping.' This implies when to use it (for Zillow property listings, preferring cache over scraping) but doesn't explicitly state when not to use it or name specific alternatives among the many sibling tools.
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 of behavioral disclosure. It adds useful context: the tool reads data (implying read-only), mentions cache lookup for reliability, and contrasts with scraping. However, it doesn't disclose potential limitations like rate limits, authentication needs, data freshness, or error handling. The description provides some behavioral insight but lacks completeness for a tool with no annotations.
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 highly concise and well-structured: three short sentences that each add value. The first states the core purpose, the second specifies the input requirement, and the third provides behavioral context. There is no wasted language, and information is front-loaded appropriately.
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 moderate complexity (reading structured web data), no annotations, no output schema, and low schema coverage, the description is minimally adequate. It covers the purpose, input requirement, and a key behavioral trait (cache reliability), but lacks details on output format, error cases, or integration with siblings. For a tool with no structured support, it meets basic needs but leaves gaps.
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 input schema has 1 parameter with 0% description coverage, but the description compensates by specifying that the 'url' must be 'a valid ZoomInfo company URL.' This adds crucial semantic context beyond the schema's URI format, clarifying the expected content of the parameter. Since there's only one parameter, the description effectively covers its purpose, warranting a score above 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 states the tool's purpose: 'Quickly read structured ZoomInfo company profile data.' It specifies the verb ('read') and resource ('ZoomInfo company profile data'), and distinguishes it from generic scraping tools by mentioning structured data and cache lookup. However, it doesn't explicitly differentiate from sibling tools like 'web_data_crunchbase_company' beyond the ZoomInfo focus.
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 provides some usage context: 'Requires a valid ZoomInfo company URL' and 'This can be a cache lookup, so it can be more reliable than scraping.' This implies when to use it (for ZoomInfo URLs, preferring reliability over scraping) but doesn't explicitly state when not to use it or name alternatives among siblings like 'scrape_as_html' for non-ZoomInfo sites. The guidance is implied rather than explicit.
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 of behavioral disclosure. It mentions that 'full_page' can be large, which is useful context about performance implications. However, it lacks details on error handling, response format, or prerequisites (e.g., requires a page to be loaded). This leaves gaps in understanding the tool's behavior beyond basic usage.
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 front-loaded with the core purpose in the first sentence, followed by a concise usage guideline. Every sentence earns its place without redundancy, making it efficient and easy to parse. It's appropriately sized for a simple tool with one parameter.
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 is somewhat complete but has gaps. It explains the purpose and parameter usage well, but without annotations or output schema, it misses details like return format or error conditions. This makes it adequate but not fully comprehensive for agent use.
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%, so the input schema already documents the 'full_page' parameter thoroughly. The description adds minimal value by reiterating the advice to avoid it unless necessary due to size, but doesn't provide additional semantics beyond what's in the schema. This meets the baseline 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 clearly states the tool's purpose: 'Get the HTML content of the current page.' It specifies the verb ('Get') and resource ('HTML content of the current page'), making the action clear. However, it doesn't explicitly differentiate from sibling tools like 'scrape_as_html' or 'scraping_browser_get_text', which likely serve similar purposes, preventing a perfect score.
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 guidance: 'Avoid using the full_page option unless it is important to see things like script tags since this can be large.' This advises when to use or avoid the parameter, offering practical context. However, it doesn't mention when to use this tool versus alternatives like 'scrape_as_html' or 'scraping_browser_get_text', which could help differentiate among siblings.
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 of behavioral disclosure. It adds useful context: the cache lookup behavior ('This can be a cache lookup') and reliability comparison ('more reliable than scraping'). However, it doesn't disclose critical traits like rate limits, authentication needs, error conditions, or what 'structured data' entails in the output. For a tool with zero annotation coverage, this leaves significant gaps.
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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by specific requirements and behavioral context. Every sentence earns its place with no wasted words, making it highly efficient for an AI agent to parse quickly.
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 moderate complexity (1 parameter, no output schema, no annotations), the description is partially complete. It covers the basic purpose, parameter constraint, and cache behavior, but lacks details on output format, error handling, or performance characteristics. Without annotations or an output schema, the description should do more to compensate, especially for a data-fetching tool.
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 coverage is 0%, so the description must compensate. It adds meaning for the single parameter by specifying 'Requires a valid product URL with /dp/ in it,' which clarifies the URL format requirement beyond the schema's generic 'uri' format. However, it doesn't explain other semantic aspects like URL validation rules or examples, so it only partially compensates for the lack of schema descriptions.
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's purpose: 'Quickly read structured amazon product data.' It specifies the verb ('read') and resource ('amazon product data'), and distinguishes it from general scraping tools by mentioning structured data and cache lookup. However, it doesn't explicitly differentiate from its direct sibling 'web_data_amazon_product_reviews' or 'web_data_amazon_product_search', which prevents a perfect score.
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 for when to use this tool: 'Requires a valid product URL with /dp/ in it.' It implies an alternative by stating 'it can be more reliable than scraping,' suggesting it's preferable to scraping tools when possible. However, it doesn't explicitly name when NOT to use it or specify exact alternatives among the many sibling scraping tools, which limits the score.
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 of behavioral disclosure. It mentions the tool is a 'cache lookup' and 'more reliable than scraping,' which adds useful context about performance and reliability. However, it doesn't disclose other important behavioral traits like error handling, rate limits, authentication needs, or what 'structured data' specifically includes. The description doesn't contradict any annotations since none exist.
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 extremely concise and well-structured across three sentences. Each sentence adds distinct value: the first states the core purpose, the second specifies the required parameter, and the third provides comparative reliability context. There is zero wasted verbiage, and information is front-loaded appropriately.
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 moderate complexity (data retrieval from a specific e-commerce site), no annotations, no output schema, and minimal parameter documentation, the description is adequate but incomplete. It covers the basic purpose, parameter requirement, and reliability advantage, but lacks details on output format, error conditions, or behavioral constraints that would help an agent use it effectively.
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 1 parameter with 0% description coverage, so the schema provides no semantic information. The description adds value by specifying that the 'url' parameter must be 'a valid bestbuy product URL,' which clarifies the expected format and domain. However, it doesn't provide further details like URL validation rules, examples, or constraints beyond 'valid.'
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's purpose: 'Quickly read structured bestbuy product data.' It specifies the verb ('read'), resource ('bestbuy product data'), and key attributes ('structured', 'quickly'). However, it doesn't explicitly differentiate from sibling tools like 'web_data_amazon_product' or 'web_data_walmart_product' beyond mentioning Best Buy specifically.
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 for when to use this tool: 'Requires a valid bestbuy product URL.' It also offers comparative guidance: 'This can be a cache lookup, so it can be more reliable than scraping,' which implicitly positions it against scraping tools like 'scrape_as_html' or 'scrape_as_markdown.' However, it doesn't explicitly state when NOT to use it or name specific alternative tools.
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 of behavioral disclosure. It reveals important operational traits: the cache lookup capability and reliability advantage over scraping. However, it doesn't disclose potential limitations like rate limits, authentication requirements, error conditions, or what 'structured data' specifically entails. The description adds value but leaves significant behavioral aspects unspecified.
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 extremely efficient with three sentences that each serve distinct purposes: stating the core functionality, specifying the required input, and providing comparative advantages. There's zero wasted language, and the information is front-loaded with the primary purpose. This represents optimal conciseness for a tool with a single parameter and clear differentiation from alternatives.
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 moderate complexity (data extraction from a specific website), no annotations, no output schema, and 0% schema description coverage, the description provides a reasonable foundation but has gaps. It covers the basic purpose, input requirement, and reliability advantage, but doesn't explain the output format, error handling, or detailed behavioral constraints. For a data extraction tool without structured output documentation, more completeness would be beneficial.
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 0% description coverage, so the description must compensate. It specifies that the single 'url' parameter must be 'a valid homedepot product URL,' which adds crucial semantic context beyond the schema's URI format. However, it doesn't elaborate on URL format requirements, validation rules, or example patterns. With one parameter and some added meaning, this meets the baseline for adequate parameter guidance.
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's purpose: 'Quickly read structured homedepot product data' specifies the verb ('read'), resource ('homedepot product data'), and key characteristic ('structured'). It distinguishes from generic scraping tools by mentioning structured data extraction, though it doesn't explicitly differentiate from all sibling web_data_* tools. The description avoids tautology by not just restating the tool name.
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 for when to use this tool: 'Requires a valid homedepot product URL' establishes the prerequisite. It also offers comparative guidance: 'This can be a cache lookup, so it can be more reliable than scraping' suggests this tool as a preferred alternative to scraping methods. However, it doesn't explicitly name specific sibling tools to avoid or provide detailed exclusion criteria.
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 of behavioral disclosure. It mentions the cache lookup capability and reliability advantage over scraping, which are valuable behavioral insights. However, it doesn't address important aspects like rate limits, authentication requirements, error conditions, or what 'structured data' specifically means in terms of output format.
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 extremely concise with just two sentences that each earn their place. The first sentence establishes the core purpose, and the second adds crucial behavioral context about cache reliability. There's zero wasted language, and the information is front-loaded effectively.
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 moderate complexity (structured data extraction from LinkedIn), no annotations, no output schema, and 0% schema description coverage, the description is incomplete. While it establishes purpose and reliability advantages, it lacks details about parameter usage, output format, error handling, and limitations. For a web data extraction tool with many scraping alternatives, more guidance would be helpful.
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 coverage is 0%, so the description must compensate for the undocumented parameter. The description doesn't mention the 'url' parameter at all, nor does it explain what kind of LinkedIn URLs are supported (e.g., job search results, specific job postings, company job pages). With 1 parameter and 0% schema coverage, the baseline would be lower, but the tool's purpose is clear enough to warrant a 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 states the tool's purpose: 'Quickly read structured linkedin job listings data' specifies the verb ('read'), resource ('linkedin job listings data'), and key characteristic ('structured'). It distinguishes from siblings like 'scrape_as_html' by emphasizing structured data extraction rather than raw scraping. However, it doesn't explicitly differentiate from other LinkedIn tools like 'web_data_linkedin_company_profile'.
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 for when to use this tool: 'This can be a cache lookup, so it can be more reliable than scraping' suggests it should be preferred over scraping tools when reliability is important. It implicitly contrasts with scraping siblings but doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling tools.
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 of behavioral disclosure. It states the tool 'reads' data (implying a read-only operation) and mentions it 'can be a cache lookup' with 'more reliable than scraping' characteristics. However, it doesn't address important behavioral aspects like rate limits, authentication needs, error conditions, or what 'structured data' specifically means in the return format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with three sentences that each serve a distinct purpose: stating the core function, specifying the input requirement, and providing reliability context. There's zero wasted language, and the information is front-loaded with the primary purpose first.
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 single-parameter read tool with no annotations and no output schema, the description provides adequate but incomplete context. It covers the basic purpose, input requirement, and reliability advantage, but lacks details about the return format, error handling, or performance characteristics. Given the simplicity of the tool (one parameter, read operation), this is minimally sufficient but leaves 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 0% description coverage for its single parameter 'url'. The description adds meaningful context: 'Requires a valid youtube profile URL,' specifying the parameter's purpose and format requirement. However, it doesn't provide examples of valid URL formats or clarify what constitutes a 'youtube profile URL' versus other YouTube URLs. With only one parameter and some added semantics, this meets 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 states the tool's purpose: 'Quickly read structured youtube profiles data.' It specifies the verb ('read'), resource ('youtube profiles data'), and key characteristic ('structured'). However, it doesn't explicitly differentiate from sibling tools like 'web_data_youtube_videos' or 'web_data_youtube_comments' beyond the resource type.
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 for when to use this tool: 'Requires a valid youtube profile URL.' It also offers comparative guidance: 'This can be a cache lookup, so it can be more reliable than scraping,' which implicitly suggests using this over scraping tools for reliability. However, it doesn't explicitly name alternative tools or specify when NOT to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that this is a read operation ('read'), mentions the cache lookup behavior which affects freshness vs reliability trade-offs, and implies it's more reliable than scraping. However, it doesn't cover rate limits, authentication needs, error conditions, or what 'structured data' specifically includes.
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 concise sentences with zero waste. First states core purpose, second establishes prerequisite, third provides important behavioral context about cache vs scraping. Each sentence earns its place and the description is appropriately sized for a single-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 read tool with no annotations, no output schema, and 0% schema coverage, the description does reasonably well covering purpose, prerequisites, and reliability advantage. However, it lacks details about return format, error handling, rate limits, or what 'structured data' specifically contains - gaps that become more significant without output schema or annotations.
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?
With 0% schema description coverage for the single 'url' parameter, the description compensates well by specifying 'Requires a valid Instagram URL' - this adds crucial semantic context about what kind of URL is expected. While it doesn't provide format examples or validation rules, it gives meaningful guidance beyond the bare 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 states the tool's purpose: 'Quickly read structured Instagram post data' specifies the verb ('read'), resource ('Instagram post data'), and key characteristic ('structured'). It distinguishes from siblings like 'web_data_instagram_comments' or 'web_data_instagram_profiles' by focusing on posts, but doesn't explicitly contrast with 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 description provides clear context for when to use this tool: 'Requires a valid Instagram URL' establishes a prerequisite, and 'This can be a cache lookup, so it can be more reliable than scraping' suggests it's preferable to scraping alternatives when reliability is important. However, it doesn't explicitly name when NOT to use it or specify exact alternatives among the many scraping siblings.
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 of behavioral disclosure. It describes the two-step process (scrape as markdown, then AI conversion) and mentions capabilities like handling bot detection, which adds useful context. However, it lacks details on potential limitations, rate limits, error handling, or output structure, leaving gaps for a tool with no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in three sentences: the core functionality, the technical process, and a key capability. Each sentence adds distinct value without redundancy, making it front-loaded 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?
Given the tool's complexity (AI-powered extraction with bot bypass), no annotations, no output schema, and 50% schema coverage, the description is incomplete. It covers the high-level process and a unique feature but misses details on output format, error cases, or performance constraints, which are critical for effective use.
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 description coverage is 50% (only the extraction_prompt parameter has a description). The description compensates by explaining the overall process and implying the url parameter's role, but doesn't add specific details about parameter usage beyond what's in the schema. With 2 parameters and partial coverage, this is above the baseline of 3 for adequate but not comprehensive support.
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 specific verbs ('scrape', 'extract') and resources ('webpage', 'structured data as JSON'), and distinguishes it from siblings by mentioning the AI-powered conversion to JSON format, unlike simpler scraping tools like scrape_as_html or scrape_as_markdown.
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 for when to use this tool ('to unlock any webpage even if it uses bot detection or CAPTCHA'), suggesting it's for challenging sites. However, it doesn't explicitly state when not to use it or name specific alternatives among the many sibling tools, such as simpler scraping options for straightforward pages.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![brightdata-mcp-heroku MCP server](https://glama.ai/mcp/servers/dsouza-anush/brightdata-mcp-heroku/badges/card.svg)](https://glama.ai/mcp/servers/dsouza-anush/brightdata-mcp-heroku)

Score Badge

Copy to your README.md:

[![brightdata-mcp-heroku MCP server](https://glama.ai/mcp/servers/dsouza-anush/brightdata-mcp-heroku/badges/score.svg)](https://glama.ai/mcp/servers/dsouza-anush/brightdata-mcp-heroku)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/dsouza-anush/brightdata-mcp-heroku'

If you have feedback or need assistance with the MCP directory API, please join our Discord server