MCP Web Scrape

Server Quality Checklist

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Latest release: v1.0.7
Disambiguation3/5
Most tools have distinct purposes, but there is notable overlap in some areas, such as analyze_performance and analyze_page_speed both focusing on performance metrics, and extract_structured_data and extract_schema_markup both handling structured data extraction. The descriptions help differentiate them, but agents might still face confusion in selecting the right tool for specific tasks.
Naming Consistency5/5
Tool names follow a highly consistent verb_noun pattern throughout, such as analyze_competitors, extract_content, and validate_html. There are no deviations in naming conventions, making the set predictable and easy to navigate for agents.
Tool Count2/5
With 48 tools, the count is excessive for a web scraping server, leading to potential bloat and complexity. A more focused set of 10-20 tools could cover the domain effectively without overwhelming agents, as many tools could be consolidated or omitted.
Completeness5/5
The tool set provides comprehensive coverage for web scraping and analysis, including extraction, analysis, monitoring, validation, and reporting. There are no obvious gaps; it supports full workflows from data retrieval to insights, ensuring agents can handle diverse tasks without dead ends.
Average 2.9/5 across 48 of 48 tools scored.
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 is failing
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?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It doesn't disclose whether this is a read-only analysis, if it requires authentication, rate limits, what the output format might be, or if it performs any destructive actions. 'Analyze' implies a read operation, but specifics are missing.
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, efficient sentence with no wasted words. However, it's front-loaded with minimal detail, which could be seen as under-specified rather than optimally concise for a tool with 3 parameters and no annotations.
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 tool that analyzes traffic patterns. It doesn't explain what 'analyze' entails, what kind of results to expect, or how it differs from sibling tools. For a 3-parameter tool with behavioral uncertainty, 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 100%, so parameters are well-documented in the schema itself. The description adds no additional meaning about parameters beyond implying 'traffic patterns' relate to the URL and metrics, which is already clear from schema descriptions. Baseline 3 is appropriate as the schema does the heavy lifting.
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 'Analyze traffic patterns and user behavior indicators' states a general purpose but lacks specificity about what resource it analyzes (URLs) and how it distinguishes from siblings like 'analyze_competitors' or 'analyze_performance'. It mentions 'traffic patterns' but doesn't clarify if this is web analytics, network traffic, or something else.
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 'analyze_performance' or 'monitor_changes'. The description doesn't mention prerequisites, exclusions, or comparative contexts, leaving the agent to guess based on tool names 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 provided, the description carries full burden for behavioral disclosure. It mentions 'combining multiple analysis tools' which hints at aggregation behavior, but doesn't disclose critical details like whether this is a read-only operation, what permissions might be needed, whether it makes external requests, rate limits, or what the output looks like. For a tool with 4 parameters and no annotations, this is inadequate transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently states the core function. It's appropriately sized for the tool's complexity and doesn't waste words. However, it could be more front-loaded with specific value since it's somewhat generic.
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 4 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what 'comprehensive reports' contain, how analysis tools are combined, what the output looks like, or any behavioral constraints. For a tool that presumably aggregates multiple analyses, more context about scope and limitations 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%, so the schema already documents all parameters thoroughly with descriptions, enums, defaults, and requirements. The description adds no additional parameter semantics beyond what's in the schema. According to scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in description.
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 'generate[s] comprehensive reports combining multiple analysis tools', which provides a general purpose but lacks specificity about what resources it acts on or what analysis tools it combines. It doesn't clearly distinguish from siblings like 'analyze_performance' or 'generate_sitemap' that might also produce reports. The description is somewhat vague rather than tautological.
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 the many sibling analysis tools. There's no mention of alternatives, prerequisites, or specific contexts where this comprehensive report generation is preferred over individual analysis tools. The agent must infer usage from the tool name and parameters 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?
No annotations are provided, so the description carries full burden. It states the tool generates a summary but doesn't disclose behavioral traits like whether it's deterministic, supports multiple languages, handles long inputs, or has rate limits. For a generative tool with no annotations, this is a significant gap 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 a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, clearly stating the core function 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 no annotations, no output schema, and 3 parameters with full schema coverage, the description is minimally adequate but lacks context on output format, error handling, or limitations. It doesn't explain what the summary looks like or any constraints, leaving gaps for a generative 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?
Schema description coverage is 100%, so the schema already documents all parameters (content, maxLength, format) with descriptions and defaults. The description adds no additional meaning beyond what the schema provides, such as explaining how 'maxLength' is measured or when to choose 'bullets' vs. 'paragraph'. 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.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Generate a summary of already extracted content' states the verb (generate) and resource (summary), but it's vague about scope and doesn't differentiate from siblings like 'extract_content' or 'analyze_readability'. It doesn't specify what type of summary (e.g., abstractive vs. extractive) or target content domains.
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 vs. alternatives. It mentions 'already extracted content' but doesn't clarify prerequisites or compare to siblings like 'analyze_readability' for content quality or 'extract_keywords' for key points. Usage context is implied but not 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 mentions analysis for 'SEO and content insights' but lacks details on behavioral traits such as rate limits, authentication needs, whether it performs web scraping or uses APIs, what happens with cached data, or the format of returned insights. The description is too vague for a tool that likely involves external data fetching.
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 with the core purpose and uses clear, direct language appropriate for a tool name like 'analyze_competitors'.
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, no output schema, and a tool that likely performs complex web analysis (inferred from sibling tools), the description is incomplete. It doesn't cover what the analysis returns, potential limitations, error handling, or how it integrates with other tools in the server, leaving significant 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?
Schema description coverage is 100%, so the schema already documents all parameters (urls, metrics, useCache) with descriptions and defaults. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Analyze competitor websites for SEO and content insights.' It specifies the verb ('analyze'), resource ('competitor websites'), and domain focus ('SEO and content insights'). However, it doesn't explicitly differentiate from siblings like 'analyze_page_speed' or 'benchmark_performance' that might overlap in analyzing websites.
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 like 'analyze_performance', 'extract_keywords', and 'compare_content', there's no indication of specific contexts, prerequisites, or exclusions for this tool's use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions analysis (implying a read operation), it doesn't specify whether this requires authentication, has rate limits, what format results come in, whether it's a real-time or cached analysis, or any error conditions. For a tool with zero annotation coverage, this 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.
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 without any unnecessary words or fluff. It's appropriately sized and front-loaded with 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 tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what kind of analysis results to expect, whether this is a lightweight or resource-intensive operation, or how it differs from similar sibling tools. The agent would need to guess about the tool's behavior and output format.
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 all parameters well-documented in the schema itself. The description doesn't add any meaningful parameter semantics beyond what's already in the schema descriptions. This meets the baseline expectation when schema coverage is complete.
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 as analyzing page loading speed and performance metrics, which is a specific verb+resource combination. However, it doesn't differentiate from the sibling tool 'analyze_performance' or 'benchmark_performance', leaving some ambiguity about how this tool differs from other performance-related tools in the server.
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 like 'analyze_performance' or 'benchmark_performance'. There's no mention of prerequisites, typical use cases, or distinctions from sibling tools that might handle similar 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. While 'analyze' suggests a read operation, the description doesn't disclose whether this tool makes network requests, has rate limits, requires authentication, what happens when useCache is false, or what the output format looks like. For a tool with no 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 a single, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized for a tool with good schema documentation and gets straight to the point with zero waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description is inadequate. It doesn't explain what 'performance metrics' includes beyond the enum values in the schema, doesn't describe the output format, doesn't mention any constraints or requirements, and doesn't differentiate from closely related sibling tools. Given the complexity of performance analysis and lack of structured documentation, 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 100%, so the schema already documents all three parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it doesn't explain what 'performance metrics' specifically means, how different metrics affect the analysis, or provide examples of URL formats. Baseline 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 tool analyzes web page performance metrics, which is a specific verb (analyze) + resource (web page performance metrics). It distinguishes from some siblings like analyze_competitors or analyze_readability, but doesn't explicitly differentiate from analyze_page_speed which appears to be a closely related sibling tool.
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 sibling tools like analyze_page_speed and benchmark_performance that likely overlap in functionality, there's no indication of when this tool is appropriate versus those alternatives or what specific performance aspects it covers.
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 analyzing readability using metrics but fails to describe key behaviors such as how the text is fetched from the URL, error handling for invalid URLs, output format, or performance considerations like rate limits or authentication needs.
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 without any unnecessary words. It is appropriately sized and front-loaded, making it easy to understand 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 analyzing text from URLs with multiple metrics, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral aspects, output format, and error handling, leaving significant gaps for the agent to understand how to use the tool 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?
Schema description coverage is 100%, so the input schema fully documents the parameters (url, metrics, useCache). The description adds no additional meaning beyond what the schema provides, such as explaining the significance of different metrics or caching implications, resulting in a baseline 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 as analyzing text readability using various metrics, which is a specific verb+resource combination. However, it doesn't distinguish this tool from its sibling tools (like analyze_competitors, analyze_page_speed, etc.) beyond the general domain of analysis, missing explicit 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. There are no explicit instructions on context, prerequisites, or comparisons to sibling tools, leaving the agent without usage direction beyond the basic purpose.
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 tool extracts content but lacks behavioral details: no mention of rate limits, error handling for failed URLs, authentication needs, output structure, or whether it's read-only/destructive. The description is minimal and doesn't compensate for missing 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 a single, efficient sentence with zero waste. It's front-loaded with the core purpose and appropriately sized for the tool's complexity.
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. It doesn't explain return values, error behavior, or operational constraints. For a batch extraction tool with 4 parameters, this leaves 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.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 4 parameters. The description adds no parameter semantics beyond implying 'urls' is an array for batch processing, which is already clear from the schema. Baseline 3 is appropriate as 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 tool's purpose as 'Extract content from multiple URLs in a single operation,' which is a specific verb (extract) + resource (content from URLs) + scope (multiple, batch). It distinguishes from sibling 'extract_content' by emphasizing batch processing, though not explicitly naming the alternative.
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 like 'extract_content' or other extraction siblings. It mentions 'multiple URLs' but doesn't specify thresholds, prerequisites, or exclusions for batch vs. single operations.
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. While 'benchmark' implies a read-only analysis operation, the description doesn't specify whether this tool makes external API calls, has rate limits, requires authentication, what the output format looks like, or whether it's a computationally intensive operation. For a tool with no annotation coverage, this leaves significant behavioral questions unanswered.
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 states the tool's core purpose without unnecessary words. It's appropriately sized for what it communicates and is front-loaded with the essential information. Every word earns its place in this concise formulation.
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 that this is a performance benchmarking tool with 3 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what 'industry standards' means, what specific metrics are benchmarked, how results are presented, or whether this involves external services. For a tool that likely produces comparative analysis results, more context about output format and behavioral characteristics 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 input schema has 100% description coverage, so all parameters are documented in the schema itself. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions. It mentions 'competitors' and 'industry standards' which align with the schema parameters, but provides no additional semantic context about parameter usage or relationships.
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: 'Benchmark website performance against competitors and industry standards'. It specifies the verb ('benchmark'), resource ('website performance'), and scope ('against competitors and industry standards'). However, it doesn't explicitly differentiate from sibling tools like 'analyze_performance' or 'analyze_page_speed', which appear related but have different scopes.
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 multiple sibling tools like 'analyze_performance', 'analyze_page_speed', and 'analyze_competitors', there's no indication of how this benchmarking tool differs or when it should be preferred. The description lacks any 'when to use' or 'when not to use' 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions checking 'broken links and redirects,' which implies network requests and potential timeouts, but doesn't specify error handling, rate limits, authentication needs, or output format. For a tool that likely makes external calls, this is a significant gap 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 a single, efficient sentence: 'Check for broken links and redirects on web pages.' It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's complexity. Every part of the sentence contributes directly to understanding the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a tool that likely performs network requests to check links), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., a list of broken links with status codes), error conditions, or performance implications. This leaves gaps for an AI agent to use the tool 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?
Schema description coverage is 100%, with all parameters well-documented in the input schema (e.g., 'url' as the URL to check, 'checkExternal' for external links). The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage without compensating value.
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: 'Check for broken links and redirects on web pages.' It specifies the verb ('check') and resource ('broken links and redirects'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'check_url_status' or 'extract_links,' which might have overlapping functionality, 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 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 siblings like 'check_url_status' (which might check a single URL's status) and 'extract_links' (which might list links without checking them), there's no indication of context, prerequisites, or exclusions. This lack of comparative guidance limits its utility for an AI agent.
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 analysis and compliance checking but lacks details on permissions, rate limits, output format, or potential side effects (e.g., network requests, data processing). For a tool with no annotations, this is insufficient to inform safe and effective use.
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: 'Analyze privacy policy content and compliance'. It's front-loaded with the core purpose, has zero waste, and is appropriately sized for the tool's complexity.
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. It doesn't explain what the analysis returns (e.g., compliance scores, issues found), behavioral traits, or error handling. For a tool with 3 parameters and potential regulatory implications, more context is needed to guide effective 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 schema fully documents the parameters (url, regulations, useCache). The description adds no additional semantic context beyond what's in the schema, such as explaining what 'compliance' entails or how analysis is performed. 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 tool's purpose: 'Analyze privacy policy content and compliance'. It specifies the action ('analyze') and the target ('privacy policy content and compliance'), which distinguishes it from general content analysis tools. However, it doesn't explicitly differentiate from sibling tools like 'analyze_cookies' or 'detect_tracking' that might also relate to privacy.
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 valid URL), exclusions (e.g., not for non-policy pages), or comparisons to siblings like 'analyze_cookies' for cookie-specific checks. Usage is implied by the name but not explicitly stated.
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 what the tool does (check validity and security details) but doesn't describe important behavioral aspects: what 'validity' means (expiration dates, issuer trust), what 'security details' include (cipher strength, protocol versions), whether it performs active network requests, potential rate limits, or what the output format looks like. This leaves significant gaps for an agent to understand the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that efficiently communicates the core purpose without any wasted words. It's front-loaded with the essential information and doesn't include unnecessary elaboration or repetition.
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 that there are no annotations and no output schema, the description should provide more complete context about what the tool returns and how it behaves. For a tool that checks SSL certificates - which involves network operations, security assessments, and potentially complex return data - the current description is insufficient. It doesn't explain what constitutes 'validity' or 'security details,' nor does it describe the output format or any behavioral constraints.
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 both parameters ('url' and 'includeChain') well-documented in the schema itself. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Check SSL certificate validity and security details' - this specifies the verb ('check') and resource ('SSL certificate') with additional context about what aspects are examined (validity and security details). However, it doesn't explicitly differentiate from sibling tools, though SSL certificate checking is distinct from most listed siblings like content analysis or performance tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. While the purpose is clear, there's no mention of when this tool is appropriate, what problems it solves, or how it differs from potentially related tools like 'check_url_status' or 'scan_vulnerabilities' among the siblings.
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. While it mentions checking accessibility and getting HTTP status codes, it lacks critical details like timeout behavior, error handling, rate limits, authentication requirements, or what constitutes 'accessible'. For a network operation tool with zero annotation coverage, 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 extremely concise (one sentence) and front-loaded with the core functionality. Every word earns its place with no wasted text 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 no annotations, no output schema, and a network operation tool that interacts with external systems, the description is incomplete. It should address behavioral aspects like timeouts, error conditions, response format, and limitations. The current description provides only basic functional intent without operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both parameters (url and followRedirects). The description doesn't add any meaningful parameter semantics beyond what's in the schema - it doesn't explain URL format requirements, redirect behavior implications, or status code interpretation. Baseline 3 is appropriate when schema does all the work.
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 with a specific verb ('check') and resource ('URL'), specifying what it does (check accessibility and get HTTP status codes). However, it doesn't explicitly distinguish itself from sibling tools like 'check_broken_links' or 'monitor_uptime', which might have overlapping functionality.
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 (like check_broken_links, monitor_uptime, scan_vulnerabilities), there's no indication of specific use cases, prerequisites, or exclusions for this URL status checking tool.
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 for behavioral disclosure. It mentions classification but doesn't describe how it works (e.g., machine learning model, rules-based), what the output format looks like, error handling, rate limits, or authentication requirements. For a tool with no annotation coverage, this 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for a tool with clear parameters documented in the schema and no complex behavioral nuances to explain.
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 classification tool. It doesn't explain what the classification output looks like (e.g., confidence scores, multiple categories), how errors are handled, or performance characteristics. For a tool that presumably returns structured classification results, this leaves too much unspecified.
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 schema already documents all three parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain what 'categories' classification entails or how 'useCache' affects performance). 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 tool's purpose: 'Classify web content into categories and topics'. It specifies the verb ('classify'), resource ('web content'), and output ('categories and topics'). However, it doesn't explicitly differentiate from siblings like 'detect_language' or 'sentiment_analysis' which also analyze content, though the classification focus is reasonably distinct.
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 for content analysis (e.g., 'detect_language', 'sentiment_analysis', 'extract_keywords'), there's no indication of when classification is preferred over other methods or what specific scenarios it's designed for.
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 ('Clear') but doesn't specify whether this requires permissions, what 'cached content entries' entails, if the operation is reversible, or any side effects like performance impact. This is inadequate 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 a single, efficient sentence with zero waste. It's front-loaded and appropriately sized for the tool's complexity, 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 that this is a mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits, return values, or error conditions, which are crucial for safe and effective use. The high schema coverage doesn't compensate for these 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 input schema has 100% description coverage, with the 'url' parameter documented as 'Specific URL to clear from cache (if not provided, clears all)'. The description doesn't add any meaning beyond this, 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 'Clear cached content entries' clearly states the verb ('Clear') and resource ('cached content entries'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'get_cache_stats' beyond the obvious action difference, missing explicit comparison.
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 or in what context it's appropriate. The description lacks any mention of prerequisites, timing, or exclusions, leaving usage entirely implicit.
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 'cached versions' but doesn't explain how caching works, what 'compare' entails (e.g., output format, success/failure conditions), or any limitations like rate limits or authentication needs. This leaves significant gaps for an agent to understand the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
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 without unnecessary words. It's appropriately sized and front-loaded, 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 comparison tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the comparison outputs (e.g., differences, similarity scores) or behavioral aspects like error handling, leaving the agent with insufficient context for effective 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?
The description adds minimal value beyond the input schema, which has 100% coverage. It implies parameters for URLs and caching but doesn't elaborate on semantics like URL formats or cache behavior. With high schema coverage, the baseline is 3, as the schema already documents parameters well.
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: comparing content between two URLs or cached versions. It specifies the verb 'compare' and the resource 'content', making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'monitor_changes' or 'track_changes_detailed', which might also involve comparisons.
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 sibling tools like 'monitor_changes' or 'track_changes_detailed' that might serve similar purposes, nor does it specify prerequisites, contexts, or exclusions for 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 full burden for behavioral disclosure. It mentions detection but doesn't explain how detection works (e.g., scanning, analysis methods), what the output looks like, whether it's resource-intensive, or any rate limits. This leaves significant gaps for a tool that likely performs web analysis.
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: 'Detect tracking scripts and privacy-related elements'. It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for a tool with clear parameters in the schema.
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 detection tool. It doesn't explain what the detection results include (e.g., list of trackers, privacy scores), how they're returned, or any behavioral aspects like execution time or error handling. This leaves too much undefined for practical 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 schema fully documents all three parameters (url, trackerTypes, useCache). The description doesn't add any parameter-specific information beyond what's in the schema, such as URL format requirements or cache behavior details. Baseline 3 is appropriate when schema handles parameter documentation.
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: 'Detect tracking scripts and privacy-related elements' with a specific verb ('detect') and target resources ('tracking scripts and privacy-related elements'). It distinguishes itself from siblings like 'analyze_cookies' or 'check_privacy_policy' by focusing on detection rather than analysis or checking, though it doesn't explicitly contrast with them.
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 like 'analyze_cookies', 'check_privacy_policy', and 'extract_structured_data' that might overlap in privacy/analysis contexts, there's no indication of when this specific detection tool is preferred or what its unique value is.
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 for behavioral disclosure. While 'extract' implies a read operation, the description doesn't mention important behavioral aspects: whether authentication is required, rate limits, what happens if no contact info is found, error handling, or output format. It mentions 'web pages' but not whether it follows redirects or handles dynamic content.
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 clearly states the tool's core purpose without unnecessary words. It's appropriately sized for a tool with clear parameters documented in the schema. Every word earns its place in conveying the essential function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the output looks like (structured data? list of contacts?), doesn't mention error conditions, and provides no behavioral context. While the schema covers parameters well, the description fails to compensate for the lack of annotations and output schema documentation.
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 schema already documents all parameters thoroughly. The description doesn't add any parameter semantics beyond what's in the schema - it mentions 'contact information like emails, phones, addresses' which aligns with the 'types' parameter enum values, but provides no additional context about parameter usage or interactions.
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: 'Extract contact information like emails, phones, addresses from web pages'. It specifies the action (extract), resource (contact information), and source (web pages). However, it doesn't explicitly differentiate from sibling tools like 'extract_content' or 'extract_entities', which could also extract information from web pages.
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 extraction tools (e.g., extract_content, extract_entities, extract_links), there's no indication of when this specific contact-focused extraction is appropriate versus other extraction methods. No prerequisites or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but lacks details on behavioral traits: it doesn't mention whether this is a read-only operation, potential rate limits, authentication needs, error handling, or what the output looks like (especially since there's no output schema). For a tool with no annotations, this is a significant gap 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 a single, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It's appropriately sized and front-loaded, making it easy to understand at a glance. Every word earns its place, with no wasted 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?
Given the complexity of web scraping/form extraction, lack of annotations, and no output schema, the description is incomplete. It doesn't address key contextual aspects like what the output structure entails, potential limitations (e.g., JavaScript-rendered forms), error cases, or performance considerations. For a tool with no structured behavioral data, the description should provide more 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?
The input schema has 100% description coverage, with clear documentation for all four parameters (url, includeHidden, includeDisabled, useCache). The description adds no additional meaning beyond what the schema provides, as it doesn't explain parameter interactions or usage nuances. With high schema coverage, the baseline score of 3 is appropriate, as 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 tool's purpose: extracting form elements and their structure from web pages. It specifies the verb 'extract' and the resource 'form elements and their structure', which distinguishes it from sibling tools like extract_content or extract_tables. However, it doesn't explicitly differentiate from all siblings (e.g., extract_structured_data might overlap in some contexts), 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 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 for extracting different types of web content (e.g., extract_links, extract_tables, extract_structured_data), there's no indication of when forms specifically are needed or what scenarios warrant this tool over others. Usage is implied by the purpose but not explicitly stated.
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 metadata extraction but lacks details on behavioral traits such as rate limits, authentication needs, error handling, or what happens with inaccessible URLs. This is inadequate for a tool that interacts with external web pages.
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 zero waste. It's front-loaded with the core purpose and avoids unnecessary elaboration, 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 no annotations and no output schema, the description is incomplete. It doesn't explain what 'metadata' entails beyond implied parameters, return format, or error cases. For a tool with external dependencies and multiple parameters, 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.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no additional meaning beyond implying metadata includes alt text and dimensions, which is already covered by parameter descriptions. Baseline 3 is appropriate as 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 ('extract') and resource ('images from a web page'), specifying 'with metadata' to differentiate from basic extraction. However, it doesn't explicitly distinguish from sibling tools like 'extract_content' or 'extract_structured_data' that might also handle images indirectly.
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 like 'extract_content' or 'get_page_metadata' that might overlap, there's no indication of specific use cases, 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 but provides minimal information. It mentions 'filtering options' which hints at some behavior, but doesn't describe what the tool actually returns (list of URLs? structured data?), whether it performs network requests, error handling, rate limits, or authentication requirements. For a tool that likely makes external HTTP requests, this is inadequate behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise - a single sentence that communicates the core purpose. It's front-loaded with the main action ('extract all links') and includes the key qualifier ('with filtering options'). There's no wasted language, though it could potentially benefit from slightly more detail given the lack of annotations and output schema.
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 that there are no annotations, no output schema, and this is a tool that likely performs network operations to extract data, the description is insufficiently complete. It doesn't explain what format the extracted links are returned in, whether there are rate limits, error conditions, or what happens when the URL is inaccessible. For a tool with 4 parameters and likely complex behavior, this description leaves too many 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?
The schema description coverage is 100%, so all parameters are documented in the schema itself. The description adds minimal value beyond what's in the schema - it mentions 'filtering options' which corresponds to the 'linkType' parameter, but doesn't provide additional context about parameter interactions, edge cases, or practical examples. This meets the baseline expectation when schema coverage is complete.
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 with a specific verb ('extract') and resource ('links from a web page'), and mentions 'filtering options' which adds useful context. However, it doesn't explicitly differentiate this tool from sibling extraction tools like 'extract_content' or 'extract_structured_data', which would require more specific language about what makes link extraction distinct.
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 (like 'extract_content', 'extract_structured_data', 'check_broken_links'), there's no indication of when link extraction is appropriate versus other extraction or analysis operations. The mention of 'filtering options' is too vague to serve as meaningful usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure but only mentions extraction and validation. It doesn't cover critical aspects like whether this is a read-only operation, potential rate limits, authentication needs, error handling, or what happens during validation (e.g., returns errors vs. warnings). This leaves significant gaps for a tool that interacts with external URLs.
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 front-loads the core functionality ('extract and validate') without any wasted words. It's appropriately sized for a tool with a clear purpose and well-documented schema.
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 extracting and validating structured data from URLs, the lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral traits, output format, error conditions, or sibling tool differentiation, which are crucial for an agent to use this tool effectively in a server with many similar extraction tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so parameters are well-documented in the schema itself. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain what 'validate' entails or how 'schemaTypes' affects output). This meets the baseline of 3 since 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 tool's purpose with specific verbs ('extract and validate') and resources ('schema.org structured data markup'), making it immediately understandable. However, it doesn't explicitly distinguish itself from the sibling tool 'extract_structured_data', which could cause confusion about when to use each tool.
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 like 'extract_structured_data' or other extraction tools in the sibling list. It lacks context about prerequisites, typical use cases, or exclusions, leaving the agent with no usage direction beyond the basic purpose.
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 what the tool does but lacks details on how it behaves: e.g., whether it fetches web pages, handles errors, returns structured data, or has performance considerations. For a tool with no annotations, this leaves significant gaps in understanding its operational traits.
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: 'Extract social media links and metadata from web pages'. It is front-loaded with the core purpose, has zero redundant words, and appropriately sized for the tool's complexity, 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 the tool's complexity (extracting data from web pages with 3 parameters) and the absence of annotations and output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., format of extracted links/metadata), error handling, or dependencies, leaving the agent with insufficient context for reliable 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?
The input schema has 100% description coverage, clearly documenting all three parameters (url, platforms, useCache) with their types, defaults, and purposes. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Extract social media links and metadata from web pages'. It specifies the verb ('extract') and resource ('social media links and metadata'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'extract_links' or 'extract_contact_info', which might also extract links or contact information including social media.
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 sibling tools like 'extract_links' (which might extract all links) or 'extract_contact_info' (which might include social media as part of contact data), leaving the agent to infer usage context without explicit 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 the full burden of behavioral disclosure. It states what data types are extracted but doesn't cover aspects like rate limits, authentication needs, error handling, or what the output looks like (e.g., format, structure). For a tool with no annotations, this is a significant gap in transparency about its operation and 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 extremely concise—a single phrase listing the data types extracted. It's front-loaded with the core purpose and wastes no words, making it easy to parse quickly. Every element in the description earns its place by specifying the tool's scope.
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 data extraction (3 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the output format, error conditions, or behavioral traits like performance or limitations. Without annotations or an output schema, the description should provide more context to guide effective use, but it falls short.
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, clearly documenting all parameters (url, dataTypes, useCache) with details like defaults and enums. The description adds no additional parameter semantics beyond what's in the schema, such as examples or edge cases. Given the high schema coverage, a baseline score of 3 is appropriate as 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 tool's purpose: extracting JSON-LD, microdata, and schema.org data. It specifies the action ('extract') and the resource ('structured data'), though it doesn't explicitly differentiate from sibling tools like 'extract_schema_markup' or 'extract_content' beyond listing data types. This makes the purpose clear but not fully distinguished from alternatives.
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 sibling tools like 'extract_schema_markup' or 'extract_content', nor does it specify scenarios or prerequisites for use. This leaves the agent without explicit usage context, relying solely on the tool name and parameters.
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 'optional CSV export' but doesn't explain key behaviors like whether the tool fetches web content, handles errors, respects robots.txt, or returns structured data. For a tool with 5 parameters and no annotations, this is insufficient to guide safe and effective use.
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 front-loads the core purpose ('Extract and parse HTML tables') and adds a key feature ('optional CSV export'). There is no wasted language, 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 tool's complexity (5 parameters, no output schema, no annotations), the description is incomplete. It lacks details on behavioral traits, error handling, output structure, and usage context. Without annotations or an output schema, the description should provide more guidance to compensate, but it doesn't, leaving significant gaps for the 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?
Schema description coverage is 100%, so the schema fully documents all 5 parameters. The description adds no parameter-specific information beyond what's in the schema, such as explaining the 'url' parameter's requirements or 'format' options. With high schema coverage, the 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: 'Extract and parse HTML tables with optional CSV export.' It specifies the verb ('extract and parse'), resource ('HTML tables'), and capability ('optional CSV export'). However, it doesn't explicitly differentiate from sibling tools like 'extract_content' or 'extract_structured_data' that might also handle table extraction, 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 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 sibling tools like 'extract_content' or 'extract_structured_data' that might overlap in functionality, nor does it specify prerequisites or exclusions. This leaves the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'optimized meta tags for SEO' and 'content analysis,' but fails to describe key behaviors: what the output looks like (e.g., format, structure), whether it involves external API calls, potential rate limits, or any side effects like caching implications. For a tool with no annotations, this is a significant gap 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 a single, efficient sentence: 'Generate optimized meta tags for SEO based on content analysis.' It is front-loaded with the core purpose, has zero waste, and is appropriately sized for the tool's complexity. Every word earns its place, 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 the tool's complexity (involves content analysis and SEO optimization), no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, output format, and usage context. While the schema covers parameters, the overall context for an AI agent to effectively use this tool is insufficient, especially compared to sibling tools that might offer similar functionalities.
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%, meaning all parameters are documented in the input schema. The description doesn't add any additional semantic context beyond what's in the schema (e.g., it doesn't explain how 'targetKeywords' influence optimization or what 'useCache' entails beyond the default). Since the schema does the heavy lifting, the baseline score of 3 is appropriate, but no extra value is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate optimized meta tags for SEO based on content analysis.' It specifies the action ('Generate'), resource ('meta tags'), and context ('SEO based on content analysis'). However, it doesn't explicitly differentiate from sibling tools like 'get_page_metadata' or 'extract_structured_data,' which might have overlapping functionality, so it doesn't reach 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 Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or comparisons to sibling tools such as 'get_page_metadata' or 'extract_structured_data,' which could be relevant for similar tasks. This lack of contextual usage information limits its effectiveness for an AI agent.
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 crawling behavior but lacks critical details: whether this is a read-only operation, potential rate limits, timeouts, authentication needs, output format (e.g., XML sitemap vs list), or error handling. For a tool that likely performs extensive network requests, this is a significant gap in behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function. It's front-loaded with the core purpose and has zero wasted words. Every part of the sentence earns its place by specifying both what is generated and how.
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 crawling tool with 5 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the output looks like (critical for a 'generate' tool), performance characteristics, or error conditions. For a tool that likely has significant behavioral implications, this leaves too many unknowns for effective 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%, with all parameters well-documented in the schema itself. The description adds no additional parameter semantics beyond implying crawling behavior. This meets the baseline of 3 when the schema does the heavy lifting, but the description doesn't compensate with any extra context about parameter interactions or usage 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 with a specific verb ('generate') and resource ('sitemap'), and specifies the method ('by crawling website pages'). It distinguishes from many sibling tools that analyze, extract, or monitor rather than generate sitemaps. However, it doesn't explicitly differentiate from potential similar tools like 'extract_links' or 'check_broken_links' that might also involve crawling.
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 involving crawling or link extraction (e.g., 'extract_links', 'check_broken_links', 'search_content'), there's no indication of when this specific sitemap generation is preferred. No context, exclusions, or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It states what the tool does but lacks details on permissions, rate limits, response format, or whether it's read-only (implied by 'Get' but not explicit). This is inadequate 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.
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 without unnecessary words. It's appropriately sized and front-loaded, with every part contributing 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?
Given no annotations and no output schema, the description is incomplete. It doesn't explain what 'detailed cache statistics and usage information' entails in terms of return values or behavioral traits, leaving significant gaps for a tool that likely provides structured 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 100% description coverage, documenting the single parameter 'includeEntries' with its type, description, and default. The description adds no parameter-specific information beyond what the schema provides, so it meets the baseline score for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 resource ('detailed cache statistics and usage information'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'clear_cache' or 'monitor_changes' that might also relate to cache operations, 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 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 siblings like 'clear_cache' and 'monitor_changes' that could involve cache-related operations, there's no indication of context, prerequisites, or exclusions for this specific tool.
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 'over time' suggesting ongoing monitoring, but doesn't disclose behavioral traits like whether this creates persistent monitoring jobs, how results are delivered, rate limits, authentication needs, or what 'changes' means operationally.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with core purpose, zero waste. Every word earns its place in this efficient description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a monitoring tool with 4 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what 'monitor' means operationally, how results are returned, or the tool's behavioral characteristics, leaving significant 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?
Schema description coverage is 100%, so parameters are well-documented in the schema. The description adds no additional parameter semantics beyond implying temporal monitoring. 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 tool's purpose: 'Monitor web page content changes over time' specifies the verb (monitor), resource (web page content), and temporal aspect. It distinguishes from siblings like 'track_changes_detailed' by being more general, but doesn't explicitly differentiate.
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 like 'track_changes_detailed' or 'compare_content'. The description implies continuous monitoring but doesn't specify use cases, 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. While 'monitor' implies ongoing observation, the description doesn't specify whether this is a one-time check or continuous monitoring, what the output format is, or any rate limits or permissions required. It lacks critical behavioral details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single, clear sentence that front-loads the core purpose. There's no wasted verbiage or 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.
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 monitoring tool with four parameters. It doesn't explain what the tool returns (e.g., uptime percentage, status logs, alerts), how results are formatted, or any behavioral nuances like polling behavior or error handling, leaving significant 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?
Schema description coverage is 100%, so the schema fully documents all four parameters (url, interval, timeout, expectedStatus) with descriptions and defaults. The description adds no additional parameter semantics beyond what's already in the schema, meeting 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 with a specific verb ('monitor') and resource ('website uptime and availability'), making it immediately understandable. However, it doesn't distinguish itself from potential sibling tools like 'check_url_status' or 'analyze_performance', which might have overlapping functionality.
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 sibling tools like 'check_url_status' and 'analyze_performance' available, there's no indication of how this tool differs in context, prerequisites, or specific use cases.
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. While 'scan' implies a read-only operation, it doesn't specify whether this is a passive scan or involves active probing, potential rate limits, authentication requirements, or what the output looks like (e.g., report format, severity levels). For a security tool with zero annotation coverage, this leaves critical behavioral aspects unclear.
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 without unnecessary words. It's appropriately sized and front-loaded, with every part contributing to clarity, making it a model of 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 a security scanning tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., scan depth, timeouts), output format, or error handling, which are crucial for an agent to use this tool effectively in a security context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters (url, scanTypes, useCache) with descriptions and defaults. The description adds no additional meaning beyond what's in the schema, such as examples of vulnerability types or caching implications, resulting in a baseline score 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 tool's purpose as 'Scan web pages for common security vulnerabilities' with a specific verb ('scan') and resource ('web pages'), making it distinct from most sibling tools that focus on analysis, extraction, or monitoring rather than security scanning. However, it doesn't explicitly differentiate from potential security-related siblings like 'check_ssl_certificate' or 'check_privacy_policy'.
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., authentication needs), compare it to similar tools like 'check_ssl_certificate' for specific vulnerability types, or indicate scenarios where it's most appropriate, leaving the agent to infer usage from the purpose 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions analyzing 'sentiment and emotional tone,' it doesn't describe how the analysis is performed (e.g., model used, confidence scores), what the output format looks like, potential rate limits, or error handling for inaccessible URLs. This leaves significant gaps for a tool with no structured behavioral hints.
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 front-loads the core purpose without unnecessary elaboration. Every word contributes directly to understanding the tool's function, making it appropriately concise and well-structured for quick comprehension.
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 sentiment analysis (which involves nuanced text processing) and the absence of both annotations and an output schema, the description is insufficient. It doesn't cover behavioral aspects like output format, error conditions, or performance characteristics, leaving the agent with incomplete context for effective 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 schema already documents all parameters (url, granularity, useCache) with descriptions and defaults. The description adds no additional parameter semantics beyond what's in the schema, such as explaining the implications of different granularity levels or cache usage. 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 as analyzing sentiment and emotional tone of web content, which is a specific verb+resource combination. However, it doesn't explicitly distinguish itself from sibling tools like 'analyze_readability' or 'classify_content' that might also analyze textual aspects, leaving some ambiguity about its unique role.
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 for content analysis (e.g., analyze_readability, classify_content, extract_keywords), there's no indication of whether sentiment_analysis is preferred for emotional assessment or how it complements other tools, leaving usage context unclear.
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 'diff analysis' but doesn't explain how changes are tracked (e.g., over time, with notifications), what the output includes, or any limitations like rate limits or data retention. This leaves significant gaps for a tool that likely involves monitoring and comparison.
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 unnecessary words. It's front-loaded with the core purpose, making it easy to parse quickly, and every part of the sentence contributes to 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 change tracking and the lack of annotations and output schema, the description is insufficient. It doesn't cover behavioral aspects like how diffs are generated, stored, or reported, nor does it address potential side effects or error handling. For a tool with 4 parameters and no structured output info, more detail is needed to guide effective 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 all parameters thoroughly. The description doesn't add any extra meaning or context about the parameters beyond what's in the schema, such as explaining the impact of 'sensitivity' levels or 'useCache' on results. Baseline 3 is appropriate since 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 tool's purpose: 'Track detailed changes in web page content with diff analysis.' It specifies the verb ('track'), resource ('web page content'), and method ('diff analysis'), making it easy to understand. However, it doesn't explicitly differentiate from the sibling tool 'monitor_changes,' which might have overlapping functionality.
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 like 'monitor_changes' or other analysis tools in the sibling list. It lacks context about prerequisites, such as needing previous snapshots or specific use cases, 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 provided, the description carries full burden for behavioral disclosure. It states what gets validated but doesn't describe how the validation works, what the output format might be, whether it makes network requests, potential rate limits, authentication needs, or error conditions. For a validation tool with zero annotation coverage, this 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single phrase with zero waste. It's front-loaded with the core purpose and uses parallel structure ('structure, accessibility, and SEO'). Every word earns its place without 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?
For a validation tool with 3 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what validation results look like, what standards are used, whether validation is synchronous or asynchronous, or what happens when validation fails. The agent lacks crucial context about the tool's behavior and outputs.
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 schema already documents all three parameters thoroughly. The description doesn't add any parameter semantics beyond what's in the schema - it doesn't explain what 'structure', 'accessibility', or 'SEO' validation entails, or provide context about the 'useCache' parameter. Baseline 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 tool's purpose: 'Validate HTML structure, accessibility, and SEO' - a specific verb ('validate') with the resource ('HTML') and scope ('structure, accessibility, and SEO'). It distinguishes from siblings like 'validate_robots' (which validates robots.txt) or 'check_broken_links' (which checks links), but doesn't explicitly differentiate from all possible alternatives.
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, when it's appropriate compared to other validation tools like 'validate_robots', or any context about typical use cases. The agent must infer usage from the tool name and parameters 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'check robots.txt compliance' but doesn't specify what compliance entails, how results are returned, whether it's a read-only operation, or any rate limits or authentication needs. This leaves significant gaps in understanding the tool's behavior and 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, front-loaded sentence that directly states the tool's function without unnecessary words. It's appropriately sized for a simple tool, making it easy to parse and understand 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns, how compliance is assessed, or any behavioral details, which are crucial for a validation tool. This leaves the agent with insufficient information to use the tool effectively beyond basic parameter input.
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, clearly documenting both parameters ('url' and 'userAgent') with their types and purposes. The description doesn't add any extra meaning beyond this, such as examples or edge cases, but the schema is sufficient, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Check') and resource ('robots.txt compliance for specific URLs'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'check_url_status' or 'scan_vulnerabilities', which might also involve URL validation aspects, leaving some ambiguity about its unique role.
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 other validation or analysis tools in the sibling list. It lacks context about prerequisites, typical use cases, 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?
No annotations are provided, so the description carries full burden. It mentions analyzing for 'privacy and security' but doesn't disclose behavioral traits like whether this is a read-only operation, if it requires network access, potential rate limits, or what the output format might be. For a tool with no annotations, this leaves significant gaps in understanding how it 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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded with the core functionality, earning its place 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, no output schema, and 3 parameters with full schema coverage, the description is minimally adequate. It states what the tool does but lacks behavioral context, usage guidance relative to siblings, and output information. For a tool analyzing web privacy/security, 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?
Schema description coverage is 100%, so the schema fully documents all three parameters (url, includeThirdParty, checkSecurity). The description adds no additional parameter semantics beyond what's in the schema, but with high coverage, the baseline is 3 as 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 tool analyzes cookies for privacy and security, specifying the resource (cookies) and purpose (privacy/security analysis). However, it doesn't explicitly differentiate from sibling tools like 'detect_tracking' or 'check_privacy_policy' that might have overlapping domains, missing 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. With siblings like 'detect_tracking' and 'check_privacy_policy' that might cover similar privacy aspects, there's no indication of context, prerequisites, or exclusions for this specific cookie analysis tool.
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 ('convert') but lacks details on permissions, rate limits, error handling, or output specifics (e.g., PDF file format, size, or download method). For a mutation tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves beyond its basic function.
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: 'Convert web page content to PDF format.' It is front-loaded with the core purpose, has zero waste, and is appropriately sized for the tool's complexity. Every word 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 moderate complexity (4 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral traits, usage context, and output handling. Without annotations or an output schema, the agent must infer behavior from the description alone, which is insufficient for full understanding but meets a baseline for a simple conversion 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?
Schema description coverage is 100%, with all parameters well-documented in the schema (e.g., URL, format with enum, includeImages, useCache). The description adds no additional parameter semantics beyond what the schema provides, such as explaining format implications or cache behavior. Baseline is 3 since the schema does the heavy lifting, but the description doesn't compensate with extra insights.
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: 'Convert web page content to PDF format' specifies the verb (convert), resource (web page content), and output format (PDF). It distinguishes from siblings like 'extract_content' or 'generate_reports' by focusing on PDF conversion, though it doesn't explicitly differentiate from similar tools not present in the sibling list.
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., URL accessibility), exclusions (e.g., dynamic content limitations), or comparisons to sibling tools like 'extract_content' for text-only extraction or 'generate_reports' for other formats. Usage is implied by the purpose but not explicitly stated.
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 ('detect') but doesn't explain how it works (e.g., whether it analyzes text from the URL, handles errors, or has rate limits). For a tool with no annotations, this is a significant gap, as it lacks details on performance, reliability, or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Detect the primary language of web page content.' It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for a straightforward tool, making it easy 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 low complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks context on usage, behavior, or output. Without annotations or an output schema, the agent might struggle with how to interpret results or handle edge cases, leaving room for improvement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear documentation for 'url', 'confidence', and 'useCache'. The description doesn't add any parameter-specific information beyond what's in the schema. According to the rules, with high schema coverage (>80%), the baseline is 3, as the schema does the heavy lifting without needing extra details in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Detect the primary language of web page content.' It specifies the verb ('detect') and resource ('language of web page content'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'translate_content' or 'classify_content', which might involve language-related operations, so it doesn't reach the highest 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 doesn't mention sibling tools like 'translate_content' (for translation after detection) or 'classify_content' (which might involve language classification), nor does it specify prerequisites or exclusions. This lack of context leaves the agent without clear usage instructions.
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. While 'extract' implies a read-only operation, the description doesn't specify whether this tool makes network requests, has rate limits, requires authentication, or what the output format looks like (e.g., structured JSON, plain text). For a tool with no annotation coverage, this 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence ('Extract named entities', 'people, places, organizations', 'from web content') contributes directly to understanding the tool's function, 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?
For a tool with no annotations and no output schema, the description is minimally adequate but incomplete. It covers the basic purpose and parameters (via schema), but lacks behavioral context (e.g., network behavior, error handling) and output details. Given the complexity of web content extraction and the absence of structured output documentation, the description should provide more guidance on what to expect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear documentation for all three parameters (url, entityTypes, useCache). The description adds no additional parameter semantics beyond what's already in the schema, such as explaining what 'web content' encompasses or how entity extraction works technically. This meets the baseline score when schema coverage is high.
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 'extract' and the resource 'named entities (people, places, organizations) from web content', making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'extract_content', 'extract_keywords', or 'extract_contact_info', which also extract information from web content but focus on different data types.
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 like 'extract_content' (which might extract full text) or 'extract_keywords' (which focuses on keywords rather than entities). There's no mention of prerequisites, use cases, or exclusions, leaving the agent to infer usage from the tool 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'discover and parse' but doesn't clarify if this is a read-only operation, what permissions might be needed, potential rate limits, or how errors are handled. For a tool interacting with external URLs, this lack of behavioral context is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without any fluff or redundancy. It's appropriately sized and front-loaded, making it easy to parse quickly, which is ideal for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a tool that interacts with external web pages to extract feeds), lack of annotations, and no output schema, the description is somewhat incomplete. It covers the basic purpose but misses behavioral details like error handling or output format. However, the high schema coverage helps compensate, making it minimally adequate but with clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so parameters like 'url', 'maxItems', 'includeContent', and 'useCache' are well-documented in the schema. The description doesn't add any additional meaning or context beyond what the schema provides, such as explaining how 'discover' relates to the parameters. 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 with specific verbs ('discover and parse') and resource ('RSS/Atom feeds from web pages'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'extract_content' or 'extract_structured_data', which might also extract content from web pages, so it doesn't reach the highest 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. With many sibling tools like 'extract_content' or 'extract_structured_data' that might handle similar web page processing, there's no indication of specific use cases, prerequisites, or exclusions, leaving the agent to guess 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'extract' but doesn't clarify operational aspects like whether it requires internet access, how it handles errors, if it modifies data, or what the output format is. For a tool with zero annotation coverage, this is a significant gap 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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with every element contributing to understanding 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 the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavior, output, and differentiation from siblings. With no output schema, the agent must infer return values, making the description incomplete for full contextual understanding.
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, clearly documenting all four parameters (url, maxKeywords, includePhrases, useCache) with types and defaults. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function as 'Extract important keywords and phrases from web content,' specifying the verb (extract), resource (keywords and phrases), and source (web content). However, it doesn't explicitly differentiate from sibling tools like 'extract_content' or 'extract_entities,' which reduces it from 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. With many sibling extraction tools (e.g., extract_content, extract_entities, extract_structured_data), there's no indication of specific use cases, prerequisites, or exclusions, leaving the agent to infer usage from 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'without any formatting or HTML', which hints at output behavior, but lacks details on permissions, rate limits, error handling, or what happens with cached content (implied by the useCache parameter). For a tool with no annotation coverage, this is a significant gap, though it does add some context about the 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 a single, efficient sentence: 'Extract plain text content without any formatting or HTML'. It's front-loaded with the core purpose and has zero waste, making it easy to parse quickly. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, 100% schema coverage, no output schema, no annotations), the description is minimally adequate. It covers the purpose and output format but lacks usage guidelines and detailed behavioral context. Without an output schema, it doesn't explain return values, which is a gap. It's complete enough for basic use but has clear room for improvement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters (url, removeWhitespace, useCache) with descriptions. The description adds no additional parameter semantics beyond what's in the schema, such as examples or edge cases. Baseline 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 tool's purpose: 'Extract plain text content without any formatting or HTML'. It specifies the verb ('extract') and resource ('plain text content'), distinguishing it from siblings like extract_content (which might preserve formatting) or extract_structured_data. However, it doesn't explicitly differentiate from all siblings (e.g., extract_entities or extract_keywords), so it's not a perfect 5.
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 when to choose extract_text_only over extract_content, extract_entities, or other extraction siblings, nor does it specify prerequisites or exclusions. Usage is implied by the name and purpose but not explicitly stated.
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 generating data from web content but doesn't specify output format, potential rate limits, authentication needs, or whether it performs network requests. For a tool that likely fetches and processes external content, this lack of detail is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core functionality without unnecessary words. Every part of the sentence ('Generate word frequency analysis and word cloud data from web content') directly contributes to understanding the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (web content analysis with 4 parameters), no annotations, and no output schema, the description is minimally adequate. It states what the tool does but lacks details on behavior, output format, and usage context. For a tool that likely involves external requests and data processing, 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?
Schema description coverage is 100%, with clear documentation for all four parameters (url, maxWords, minLength, useCache). The description adds no additional parameter semantics beyond what's in the schema. According to the rules, when schema coverage is high (>80%), the baseline score is 3 even without param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate word frequency analysis and word cloud data from web content.' It specifies the action (generate), the resource (word frequency analysis and word cloud data), and the source (web content). However, it doesn't explicitly differentiate from sibling tools like 'extract_keywords' or 'analyze_readability,' which might have overlapping functionality.
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 focused on content analysis (e.g., 'extract_keywords,' 'analyze_readability,' 'summarize_content'), there's no indication of specific use cases, prerequisites, or exclusions. The agent must infer usage based on the name and description 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 provided, the description carries the full burden of behavioral disclosure. It mentions what gets extracted but doesn't cover important aspects like rate limits, authentication needs, error handling, or whether the tool performs web requests (implied but not stated). The description is functional but 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, efficient sentence that clearly states the tool's function without any wasted words. It's appropriately sized for a straightforward extraction tool and gets directly to the point.
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 relatively simple read operation with good schema coverage but no output schema or annotations, the description is minimally adequate. It explains what the tool does but lacks context about performance characteristics, limitations, or relationship to sibling tools. The absence of output schema means users must infer what the return format looks like.
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 schema already documents both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. This meets the baseline expectation when schema coverage is complete.
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 with specific verbs ('extract') and resources ('meta tags, title, description, keywords from web pages'). It distinguishes itself from siblings like 'extract_content' or 'extract_text_only' by focusing specifically on metadata extraction, though it doesn't explicitly mention this 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 like 'extract_content' or 'extract_structured_data'. There's no mention of prerequisites, constraints, or typical use cases beyond the basic function.
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. While 'search' implies a read-only operation, the description doesn't specify what happens when no matches are found, whether the search is performed locally or remotely, performance characteristics, or what the output format looks like (since there's no output schema). It mentions 'extracted content' but doesn't clarify if this refers to content from a previous extraction step or any text input.
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 states the core purpose without unnecessary words. It's appropriately sized for a search tool and front-loads the essential information. Every word earns its place in this concise formulation.
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 search tool with 5 well-documented parameters but no annotations and no output schema, the description is minimally adequate. It identifies the tool's purpose but leaves significant gaps: no output format description, no behavioral context about search mechanics, and no integration guidance with sibling extraction tools. The 100% schema coverage helps, but the description itself doesn't provide complete context for effective tool selection and 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 schema already documents all 5 parameters thoroughly with descriptions and defaults. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain parameter interactions, provide examples of valid queries, or clarify the 'content' parameter's relationship to sibling extraction tools. Baseline 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 tool's purpose as 'Search for specific text patterns within extracted content', which is a specific verb+resource combination. It distinguishes from siblings like 'extract_content' or 'analyze_content' by focusing on search rather than extraction or analysis. However, it doesn't explicitly differentiate from potential similar search tools that might exist.
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 extracted content first), nor does it compare to sibling tools like 'extract_keywords', 'detect_language', or 'search' tools that might exist in other contexts. The agent must infer usage from the name and description 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 provided, the description carries full burden for behavioral disclosure. While 'translate' implies a read operation, it doesn't specify whether this requires external API calls, has rate limits, affects the original content, or what the output format looks like. The description mentions web page content but doesn't clarify if this is a live translation service or cached translation. Significant behavioral details are missing 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized for a straightforward translation tool and front-loads the essential information. Every word earns its place in communicating the tool's 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?
For a translation tool with 4 parameters, 100% schema coverage, but no annotations or output schema, the description is minimally adequate. It states what the tool does but lacks important context about behavioral characteristics, output format, and usage scenarios. The schema handles parameter documentation well, but the description doesn't compensate for the missing behavioral transparency that annotations would normally provide.
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 schema already documents all 4 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain URL requirements, language code formats beyond examples, or cache behavior implications. With complete schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter understanding.
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: 'Translate web page content to different languages' - a specific verb ('translate') and resource ('web page content'). It distinguishes from most sibling tools which focus on analysis, extraction, or validation rather than translation. However, it doesn't explicitly differentiate from potential translation alternatives that might exist in other contexts.
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. Among the 50+ sibling tools, there's no explicit translation alternative listed, but the description doesn't mention this tool's specific scope or any prerequisites. It lacks context about when translation is appropriate versus other content processing tools like 'detect_language' or 'extract_content'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the output format (Markdown with citation) but fails to describe critical behaviors such as error handling (e.g., for invalid URLs or access restrictions), performance characteristics (e.g., timeouts or rate limits), or side effects (e.g., caching implications from the useCache parameter). This leaves significant gaps for a tool that interacts with external web resources.
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 front-loads the core purpose ('Extract and clean content from a web page') and immediately states the output ('returning Markdown with citation'). There is no wasted verbiage, and every word contributes directly to understanding the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of web content extraction (involving external resources, multiple parameters, and no output schema), the description is insufficient. It lacks details on output structure (beyond 'Markdown with citation'), error conditions, or behavioral constraints, which are critical for an agent to use this tool effectively. The high schema coverage does not compensate for these missing contextual elements.
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, clearly documenting all 6 parameters with their types, defaults, and purposes. The description adds no additional parameter semantics beyond what the schema provides, such as explaining trade-offs between formats or the implications of bypassRobots. This meets the baseline score of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Extract and clean content from a web page') and the output ('returning Markdown with citation'), which distinguishes it from sibling tools like extract_text_only or extract_structured_data that focus on specific content types. It uses precise verbs and specifies the resource (web page content).
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 like extract_text_only, extract_structured_data, or summarize_content. It lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name and parameters 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 provided, the description carries the full burden of behavioral disclosure. While it mentions what the tool does, it doesn't describe important behavioral aspects like error handling, rate limits, authentication requirements, performance characteristics, or what the output format looks like. The description is functional but 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, efficient sentence that communicates the core purpose without unnecessary words. It's front-loaded with the essential information and contains no redundant or verbose language.
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 4 parameters, 100% schema coverage, but no annotations and no output schema, the description is minimally adequate. It states what the tool does but lacks important context about behavior, output format, and usage guidelines. The completeness is borderline given the tool's moderate complexity and the absence of structured output documentation.
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, so all parameters are documented in the schema itself. The description doesn't add any additional parameter semantics beyond what's already in the schema descriptions. This meets the baseline expectation when schema coverage is complete.
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 specific action ('extract'), resource ('document structure and heading hierarchy'), and scope ('from web pages'). It distinguishes this tool from siblings like 'extract_content' or 'extract_text_only' by focusing specifically on headings and hierarchy rather than general content extraction.
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 like 'extract_content' or 'extract_structured_data'. It doesn't mention prerequisites, limitations, or typical use cases for heading extraction versus other content analysis tools available on the server.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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