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 but doesn't disclose behavioral traits like whether it's read-only, if it modifies files, what output format to expect, or any performance considerations. This leaves critical operational details unspecified.

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

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

The description is a single, efficient sentence with no wasted words. It's front-loaded and appropriately sized for the tool's apparent simplicity, making it easy to parse quickly.

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

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

Given no annotations, no output schema, and a single parameter, the description is incomplete. It fails to explain what the analysis entails, what results are returned, or how it differs from related tools, leaving significant gaps for an AI agent to understand and use the tool effectively.

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

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

The description adds no parameter semantics beyond the input schema, which has 100% coverage for the single parameter 'projectPath'. Since schema coverage is high, the baseline score is 3, as the schema adequately documents the parameter without additional description value.

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

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

The description 'Analyze bundle size of dependencies' states the verb ('analyze') and resource ('bundle size of dependencies'), but it's vague about scope and depth. It doesn't distinguish from siblings like 'analyze_dependencies' or 'find_unused_dependencies', leaving ambiguity about what specifically is analyzed.

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

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

No guidance is provided on when to use this tool versus alternatives. With many sibling tools (e.g., 'analyze_dependencies', 'check_vulnerabilities'), the description lacks context on prerequisites, typical use cases, or exclusions, offering no help in tool selection.

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

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

No annotations are provided, so the description carries full burden for behavioral disclosure. While 'analyze' implies a read-only operation, the description doesn't clarify whether this tool modifies files, requires specific permissions, has rate limits, or what the output format looks like. For a multi-parameter analysis tool with no annotation coverage, this is insufficient behavioral context.

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

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

The description is a single, efficient sentence that clearly states the tool's purpose. It's appropriately sized and front-loaded with the core functionality, with zero wasted words or redundant information.

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

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

Given the tool's complexity (6 parameters, no output schema, no annotations), the description is minimally adequate but has clear gaps. It states what the tool does but lacks crucial context about when to use it versus specialized siblings, behavioral characteristics, and output format. For a comprehensive analysis tool in a crowded namespace, more guidance would be helpful.

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

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

Schema description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description mentions the analysis dimensions (complexity, maintainability, code smells, duplications) which loosely map to some parameters like maxComplexity, checkDuplicates, and checkCodeSmells, but doesn't add significant semantic value beyond what the schema provides. 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.

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

The description clearly states the tool's purpose: analyzing code quality across multiple dimensions (complexity, maintainability, code smells, duplications). It uses specific verbs ('analyze') and identifies the resource ('code quality'), but doesn't explicitly differentiate from sibling tools like 'calculate_complexity', 'detect_code_smells', or 'find_duplicate_code' which appear to cover overlapping functionality.

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

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

The description provides no guidance on when to use this tool versus alternatives. With multiple sibling tools covering similar aspects (e.g., calculate_complexity, detect_code_smells, find_duplicate_code), there's no indication whether this is a comprehensive analysis tool versus those specialized tools, or what context would make this the appropriate choice.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions what the tool analyzes but doesn't describe execution behavior (e.g., runtime, permissions, output format, or side effects). For a tool with 5 parameters and 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.

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 earns its place by specifying the resource and analysis types, making it appropriately sized and well-structured.

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

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

Given the tool's complexity (5 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits, output format, and usage context relative to siblings, leaving gaps that could hinder an AI agent's ability to invoke it correctly.

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

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

The description lists analysis categories (unused, outdated, vulnerable packages) that partially map to parameters like 'checkUnused', 'checkOutdated', and 'checkVulnerabilities', adding some meaning. However, with 100% schema description coverage, the schema already documents all parameters thoroughly, so the description provides only marginal value beyond the schema.

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

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

The description clearly states the tool's purpose as analyzing project dependencies across three specific categories (unused, outdated, vulnerable packages). It uses a specific verb ('analyze') and resource ('project dependencies'), but doesn't explicitly differentiate from sibling tools like 'find_unused_dependencies' or 'check_vulnerabilities', which would require a 5.

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

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. With many sibling tools like 'find_unused_dependencies', 'check_outdated_packages', and 'check_vulnerabilities', the description lacks explicit context for choosing this comprehensive tool over more specific ones, offering only implied usage through its broad scope.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states what the tool does ('calculate maintainability index') without mentioning how it works (e.g., algorithm used, output format, whether it's read-only or has side effects, or any performance considerations). This is insufficient 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.

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 action ('calculate') and resource ('maintainability index for code'), making it easy to parse quickly. Every word contributes directly to the tool's purpose.

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

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

Given the complexity of code analysis tools and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the maintainability index measures, how results are returned, or any prerequisites (e.g., supported file types). This leaves significant gaps for an agent to use the tool effectively in context with many sibling alternatives.

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

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

The input schema has 100% description coverage, with the 'files' parameter clearly documented as 'File paths to analyze'. The description adds no additional meaning beyond this, but since the schema is comprehensive, a baseline score of 3 is appropriate as the schema handles the parameter documentation adequately.

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

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

The description clearly states the verb ('calculate') and resource ('maintainability index for code'), making the tool's purpose understandable. However, it doesn't differentiate from sibling tools like 'calculate_complexity' or 'generate_code_metrics', which also compute code-related metrics, leaving some ambiguity about when to choose this specific tool.

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

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

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools focused on code analysis (e.g., 'analyze_code_quality', 'calculate_complexity'), there's no indication of whether this tool is for specific file types, programming languages, or scenarios, 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.

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

With no annotations provided, the description carries full burden for behavioral disclosure but offers minimal information. It mentions analyzing 'all network requests' but doesn't specify whether this requires special permissions, what format results are returned in, whether it performs active requests or analyzes existing logs, or any rate limits. For a tool that likely interacts with web resources, 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.

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

The description is a single, efficient sentence that communicates the core functionality without unnecessary words. It's appropriately sized for a tool with two parameters and no complex behavioral nuances to explain. Every word earns its place by specifying what is analyzed and the scope of analysis.

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

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

For a tool that analyzes network requests with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what kind of analysis is performed, what data is returned, whether it makes actual requests or analyzes existing data, or any prerequisites. Given the complexity of network analysis and the lack of structured metadata, the description should provide more operational context.

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

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

Schema description coverage is 100%, providing complete documentation for both parameters ('url' and 'timeout'). The description adds no additional parameter semantics beyond what's already in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no parameter information in the description.

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

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

The description clearly states the action ('analyze') and target resource ('network requests made by a web page'), making the purpose immediately understandable. It distinguishes itself from siblings like 'scrape_html' or 'test_api_endpoint' by focusing specifically on network request analysis rather than content extraction or endpoint testing. However, it doesn't specify what aspects of network requests are analyzed (e.g., timing, headers, size), 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.

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

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools available for web analysis (like 'scrape_dynamic_content', 'test_api_endpoint', 'scan_security_issues'), there's no indication whether this tool is for performance analysis, security auditing, or general monitoring. The agent must infer usage context 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.

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 'analyze' suggests a read-only operation, the description doesn't clarify whether this tool requires specific permissions, what kind of output it produces, whether it makes network calls, or any rate limits. For a security/permissions analysis tool with zero annotation coverage, this is inadequate.

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

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

The description is a single, efficient sentence with zero wasted words. It's appropriately sized for a tool with one parameter and gets straight to the point without unnecessary elaboration.

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

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

For a security/permissions analysis tool with no annotations and no output schema, the description is insufficient. It doesn't explain what kind of analysis is performed, what format the results take, whether it's a local or remote analysis, or what security context is required. The description leaves too many open questions 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.

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

Schema description coverage is 100% with the 'files' parameter clearly documented in the schema. The description doesn't add any parameter-specific information beyond what's already in the schema. With good schema coverage, the baseline score of 3 is appropriate - the description doesn't enhance parameter understanding but doesn't need to compensate for schema gaps either.

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

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

The description clearly states the tool's purpose: 'Analyze file permissions and access control issues' - a specific verb ('analyze') and resource ('file permissions and access control issues'). However, it doesn't explicitly differentiate from sibling tools like 'scan_security_issues' or 'check_compliance' that might have overlapping security analysis domains.

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

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

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools focused on analysis (e.g., analyze_code_quality, analyze_dependencies, scan_security_issues), there's no indication of what makes this tool distinct or when it should be preferred over other analysis tools.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'analyze' suggests a read-only operation, the description doesn't clarify whether this tool performs calculations, generates reports, has side effects, or requires specific permissions. The '(simplified)' hint suggests limitations but doesn't specify what they are (e.g., limited file types, basic metrics).

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

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

The description is extremely concise at just 6 words, with zero wasted language. It's front-loaded with the core purpose and includes a useful qualifier ('simplified') that adds context without verbosity. Every word earns its place in this minimal description.

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

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

For a tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what 'analyze' means operationally, what kind of coverage metrics are calculated, what format results are returned in, or what 'simplified' entails. Given the complexity of test coverage analysis and lack of structured metadata, more guidance is needed for effective tool use.

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

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

Schema description coverage is 100%, with both parameters ('files' and 'testFiles') clearly documented in the schema. The description adds no parameter-specific information beyond what's in the schema. The baseline score of 3 is appropriate when the schema does the heavy lifting, though the description could have explained the relationship between files and testFiles.

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

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

The description 'Analyze test coverage for code files (simplified)' clearly states the verb ('analyze'), resource ('test coverage'), and scope ('code files'), with 'simplified' providing useful context. However, it doesn't explicitly differentiate from sibling tools like 'analyze_code_quality' or 'calculate_complexity' that might overlap in analyzing code aspects.

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

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

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools like 'analyze_code_quality' and 'calculate_complexity' that might analyze similar aspects of code, the agent receives no help in choosing between them. The '(simplified)' hint implies this might be a basic version but doesn't specify what makes it simpler or when to prefer it.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't describe how it behaves: no information about computational cost, output format, error handling, or whether it modifies files. For a tool that analyzes code, this leaves significant gaps in understanding its operational characteristics.

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

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

The description is a single, efficient sentence that communicates the core purpose without any wasted words. It's appropriately sized for a straightforward tool and is immediately understandable without requiring parsing of unnecessary details.

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

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

For a tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the calculation returns (e.g., numerical scores, reports, or structured data), how results are formatted, or any behavioral aspects. Given the complexity of code analysis and lack of structured metadata, more context is needed for effective use.

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

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

The schema description coverage is 100%, with the single parameter 'files' clearly documented as 'File paths to analyze'. The description adds no additional parameter information beyond what the schema provides, so it meets the baseline for adequate but unenhanced parameter documentation.

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

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

The description clearly states the verb ('calculate') and resource ('cyclomatic complexity for code files'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'analyze_code_quality' or 'generate_code_metrics' that might also provide complexity metrics, so it doesn't fully distinguish itself from alternatives.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'analyze_code_quality' or 'generate_code_metrics'. It doesn't mention prerequisites, exclusions, or specific contexts where this tool is preferred over other analysis tools in the server.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'check' implies a read-only operation, it doesn't specify whether this involves external API calls, rate limits, authentication requirements, or what the output format looks like. The 'simplified' hint suggests limitations but doesn't detail what aspects are simplified. For a security 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.

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

The description is extremely concise with just one sentence that directly states the tool's purpose. Every word earns its place, and there's no redundant or verbose language. The 'simplified' qualifier adds useful context without unnecessary elaboration.

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

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

Given the tool's moderate complexity (2 parameters, security-focused), no annotations, and no output schema, the description is minimally adequate but has clear gaps. It covers the basic purpose but lacks behavioral details, usage guidelines, and output information. The high schema coverage helps with parameters, but overall completeness is limited for a tool that likely produces important security results.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema—it doesn't explain what 'files' should contain beyond paths, how 'standard' choices differ, or provide examples. With high schema coverage, the baseline 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.

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 checking code compliance with security standards, using specific verbs ('check') and resources ('code compliance', 'security standards'). It distinguishes itself from most siblings by focusing on compliance rather than analysis, scanning, or extraction, though it doesn't explicitly differentiate from 'scan_security_issues' or 'check_vulnerabilities' which might have overlapping security concerns.

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

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

The description provides no guidance on when to use this tool versus alternatives. With multiple security-related siblings like 'scan_security_issues' and 'check_vulnerabilities', there's no indication of what makes this compliance check different or when it should be preferred. The 'simplified' qualifier hints at a scope limitation but doesn't clarify specific use cases or exclusions.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the action ('Check') without details on permissions, output format, rate limits, or whether it's read-only or destructive. This is inadequate for a tool with potential behavioral implications.

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

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

The description is a single, efficient sentence with no wasted words. It is 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.

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

Given the tool's potential complexity (checking packages in a project) and lack of annotations or output schema, the description is incomplete. It doesn't explain what 'outdated' means, how results are returned, or any behavioral traits, 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.

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

The input schema has 1 parameter with 100% description coverage ('Path to project root'), so the schema fully documents the parameter. The description adds no additional meaning beyond what the schema provides, resulting in a baseline score of 3.

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

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

The description 'Check for outdated packages' states the verb ('Check') and resource ('outdated packages'), providing a basic purpose. However, it lacks specificity about scope (e.g., all packages vs. specific ones) and doesn't differentiate from sibling tools like 'suggest_dependency_updates' or 'analyze_dependencies', making it vague in context.

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

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

No guidance is provided on when to use this tool versus alternatives such as 'suggest_dependency_updates' or 'analyze_dependencies'. The description offers no context, prerequisites, or exclusions, leaving the agent without usage direction.

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

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

No annotations are provided, so the description carries full burden for behavioral disclosure. While 'check' implies a read-only analysis operation, the description doesn't specify whether this tool modifies files, requires specific TypeScript configurations, has performance implications, or provides structured output. For a tool with zero 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.

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

The description is a single, efficient sentence that states the core functionality without unnecessary words. It's appropriately sized for a straightforward tool and front-loads the essential information. Every word earns its place in communicating the tool's purpose.

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

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

Given the tool's moderate complexity (type checking can involve configuration, dependencies, and detailed output), no annotations, and no output schema, the description is minimally adequate but incomplete. It identifies what the tool does but doesn't address behavioral aspects, output format, or integration context that would help an agent use it effectively. The description meets basic requirements but leaves important contextual gaps.

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

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

The input schema has 100% description coverage with the 'files' parameter clearly documented as 'TypeScript file paths to check'. The description adds no additional parameter information beyond what's in the schema. With complete schema documentation, the baseline score of 3 is appropriate - the description doesn't enhance parameter understanding but doesn't need to compensate for gaps.

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

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

The description clearly states the tool's purpose with a specific verb ('check') and resource ('TypeScript files for type errors'). It distinguishes itself from siblings like 'validate_syntax' or 'lint_code' by focusing specifically on type checking rather than general syntax validation or linting. However, it doesn't explicitly contrast with all potential alternatives like 'analyze_code_quality' which might include type checking as part of broader analysis.

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

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

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools for code analysis (e.g., 'validate_syntax', 'lint_code', 'analyze_code_quality'), there's no indication whether this tool is preferred for TypeScript type checking specifically, whether it should be used before/after other tools, or what scenarios it's designed for. The agent must 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.

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

With no annotations provided, the description carries full burden but only states what the tool does, not how it behaves. It doesn't disclose critical details like whether it's read-only, requires authentication, has rate limits, returns structured data, or handles errors, leaving significant gaps for an agent.

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

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

The description is a single, efficient sentence with zero waste—it directly states the tool's purpose without fluff. 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.

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

Given the complexity of security vulnerability checking, no annotations, and no output schema, the description is incomplete. It doesn't explain what 'check' entails, what the output looks like, or behavioral traits, making it inadequate for an agent to use effectively.

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

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

Schema description coverage is 100%, so the schema already documents the 'projectPath' parameter. The description adds no additional meaning about parameters, such as format examples or constraints, but meets the baseline 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.

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

The description clearly states the action ('Check for') and target ('security vulnerabilities in dependencies'), which is specific and understandable. However, it doesn't differentiate from sibling tools like 'scan_security_issues' or 'check_outdated_packages', leaving some ambiguity about scope.

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

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

No guidance is provided on when to use this tool versus alternatives like 'scan_security_issues' or 'check_outdated_packages'. The description lacks context about prerequisites, timing, or exclusions, offering minimal help for tool selection.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('detect') but doesn't explain what 'code smells' entail, how detection works, whether it's read-only or has side effects, or any performance or output details. This leaves significant gaps in understanding 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.

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 front-loaded and appropriately sized for its content, making it easy to parse quickly.

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

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

Given the complexity of code analysis and lack of annotations or output schema, the description is incomplete. It doesn't cover what 'code smells' are, how results are returned, or behavioral traits like side effects or performance, which are crucial for effective tool use in this context.

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

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

Schema description coverage is 100%, so the schema already documents both parameters ('files' and 'maxComplexity') with descriptions. The tool description adds no additional meaning or context beyond what's in the schema, such as explaining file formats or complexity thresholds, resulting in a baseline score.

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

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

The description clearly states the tool's purpose with a specific verb ('detect') and resource ('code smells in code files'), making it understandable. However, it doesn't differentiate from sibling tools like 'analyze_code_quality' or 'find_duplicate_code', which might have overlapping functionality, so it's not fully distinguished.

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

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

The description provides no guidance on when to use this tool versus alternatives, such as 'analyze_code_quality' or 'find_duplicate_code', which are related siblings. There's no mention of prerequisites, exclusions, or specific contexts for its application, leaving the agent to infer usage.

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

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

No annotations are provided, so the description carries the full burden. It states the tool detects secrets but doesn't disclose behavioral traits such as what types of secrets are detected (e.g., API keys, passwords), whether it's a read-only scan, if it requires specific permissions, or how results are returned. This leaves significant gaps for a security tool.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, making it easy to understand at a glance.

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

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

Given the complexity of secret detection and the lack of annotations and output schema, the description is insufficient. It doesn't explain what constitutes a 'secret', the detection methodology, output format, or error handling. For a security-focused tool with no structured support, more context is needed for effective use.

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

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

Schema description coverage is 100%, with the parameter 'files' clearly documented as 'File paths to scan'. The description adds no additional meaning beyond this, such as file format support or scanning depth. With high schema coverage, the baseline score of 3 is appropriate as the schema handles parameter documentation adequately.

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

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

The description clearly states the tool's purpose with a specific verb ('detect') and resource ('hardcoded secrets and credentials in code'). It distinguishes from most siblings that analyze other aspects like bundle size, dependencies, or code quality, though it doesn't explicitly differentiate from 'scan_security_issues' which might overlap in security scanning.

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

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

No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing code files), exclusions (e.g., not for runtime secrets), or comparisons to similar tools like 'scan_security_issues' or 'check_compliance' that might handle security aspects.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions monitoring network requests but doesn't specify behavioral traits such as whether it requires specific permissions, how it handles dynamic content, what the output format is, or any rate limits. This is a significant gap 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.

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 details. Every word earns its place, 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.

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

Given the complexity of discovering API endpoints (which may involve dynamic interactions) and the lack of annotations and output schema, the description is incomplete. It doesn't address key aspects like output format, error handling, or integration with sibling tools, leaving gaps for effective agent use.

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

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

The input schema has 100% description coverage, so the schema already documents both parameters ('url' and 'timeout'). The description doesn't add any meaning beyond what the schema provides, such as explaining the context of the URL or timeout usage. 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.

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

The description clearly states the tool's purpose: 'Discover API endpoints by monitoring network requests on a web page.' It specifies the verb ('discover') and resource ('API endpoints') with the method ('monitoring network requests'). However, it doesn't explicitly differentiate from sibling tools like 'analyze_network_requests' or 'extract_api_schema,' which reduces clarity in context.

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

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

The description provides no guidance on when to use this tool versus alternatives. With siblings like 'analyze_network_requests' and 'extract_api_schema' available, it lacks explicit when-to-use, when-not-to-use, or prerequisite information, 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.

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 clicking and extracting, implying interaction and data retrieval, but fails to detail critical aspects like error handling (e.g., what happens if selectors aren't found), performance (e.g., timeouts or delays), or output format (e.g., text or structured data). This leaves significant gaps for 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.

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

The description is extremely concise and front-loaded, consisting of a single, direct sentence that captures the core functionality without any wasted words. Every part of the sentence earns its place by clearly stating the tool's actions and targets.

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

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

Given the complexity of a web interaction tool with no annotations and no output schema, the description is insufficiently complete. It lacks details on behavioral traits (e.g., what 'extract' entails in terms of output), error conditions, or usage context, making it hard for an agent to use this tool reliably without additional information.

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

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

The input schema has 100% description coverage, clearly documenting all three parameters (url, clickSelector, extractSelector) with their purposes. The description adds no additional meaning beyond this, such as examples or constraints, so it meets the baseline score of 3 for adequate but not enhanced parameter semantics.

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

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

The description clearly states the tool's purpose with specific verbs ('click' and 'extract') and resources ('element' and 'content'), making it easy to understand what it does. However, it doesn't distinguish itself from sibling tools like 'scrape_with_interaction' or 'scrape_dynamic_content', which may have overlapping functionality, 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.

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, such as requiring a dynamic webpage, or compare it to siblings like 'scrape_by_selector' or 'scrape_with_interaction', leaving the agent to guess based on context alone.

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

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

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It doesn't disclose whether this is a read-only operation, what permissions are needed, how it handles errors, or the format of extracted schema. This leaves critical behavioral traits unspecified.

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

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

The description is a single, efficient sentence with no wasted words, clearly front-loading the core purpose. It's appropriately sized for a tool with two parameters and no complex output schema.

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

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

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate but incomplete. It lacks details on behavioral traits and usage context, though the schema covers parameters well, resulting in a baseline score.

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

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

Schema description coverage is 100%, so parameters are well-documented in the schema. The description adds no additional meaning beyond implying 'url' and 'endpoint' relate to API schema extraction, aligning with the baseline for high schema coverage without extra param details.

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

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

The description clearly states the action ('extract') and resource ('API schema from discovered endpoints'), making the tool's purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'discover_api_endpoints' or 'test_api_endpoint', 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.

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 'discover_api_endpoints' or 'test_api_endpoint'. It lacks context about prerequisites (e.g., needing discovered endpoints first) or exclusions, leaving usage ambiguous.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure but only states the basic function without details on permissions, rate limits, output format, or error handling. It fails to address how it handles dynamic content beyond the schema's 'useBrowser' parameter, leaving gaps in operational understanding.

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

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

The description is a single, direct sentence with zero wasted words, front-loading the core purpose ('Extract all image URLs from a web page'). It is appropriately sized for the tool's simplicity and efficiently communicates its intent without unnecessary elaboration.

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

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

Given the lack of annotations and output schema, the description is incomplete for a tool with parameters and potential behavioral complexity. It doesn't explain return values, error cases, or how it interacts with dynamic content, leaving significant gaps in contextual understanding despite the concise purpose statement.

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

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

The schema description coverage is 100%, so the schema already fully documents the 'url' and 'useBrowser' parameters. The description adds no additional meaning beyond implying image extraction from a web page, which aligns with the schema but doesn't enhance parameter understanding. 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.

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 resource ('all image URLs from a web page'), distinguishing it from siblings like extract_links, extract_text, or extract_tables by focusing exclusively on images. It uses precise language that leaves no ambiguity about what the tool does.

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

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

The description provides no guidance on when to use this tool versus alternatives like extract_links or scrape_html, nor does it mention prerequisites such as needing a valid URL or handling dynamic content. It lacks explicit context for selection among sibling tools.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. While 'extract' implies a read operation, it doesn't specify whether this requires network access, what happens with dynamic content (only hinted at by the 'useBrowser' parameter), rate limits, authentication needs, or what format the extracted links are returned in. The description is minimal and lacks important operational context.

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

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

The description is a single, clear sentence that states exactly what the tool does without any unnecessary words. It's perfectly front-loaded and wastes no space on redundant information, making it highly efficient for an agent to parse.

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

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

For a web scraping tool with no annotations and no output schema, the description is insufficient. It doesn't explain what constitutes a 'link', how results are structured, whether there are limitations (like maximum links extracted), or how it handles different types of web pages. Given the complexity of web scraping and the lack of structured behavioral information, more context is needed.

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

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

The description adds no parameter information beyond what's already in the schema (which has 100% coverage). It doesn't explain what 'extract all links' means in practice, how links are identified, or provide context about the 'useBrowser' parameter's implications. With complete schema coverage, the baseline is 3, but the description doesn't enhance understanding of parameter usage.

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

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

The description clearly states the action ('extract') and target resource ('all links from a web page'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'extract_images', 'extract_tables', or 'extract_text', which all perform extraction operations on web pages but target different content types.

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

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

The description provides no guidance on when to use this tool versus alternatives. With multiple sibling extraction tools (extract_images, extract_tables, extract_text, extract_after_click, extract_api_schema) and scraping tools (scrape_by_selector, scrape_dynamic_content, scrape_html), there's no indication of when link extraction is appropriate versus other extraction methods or when to choose this over general scraping tools.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but lacks critical behavioral details: it does not describe the output format (e.g., structured data like JSON or CSV), error handling, performance implications, or rate limits. For a web scraping tool with no 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.

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

The description is a single, efficient sentence that is front-loaded with the core purpose. There is no wasted language or redundancy, making it easy to parse quickly. It earns its place by clearly stating the tool's function without unnecessary elaboration.

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

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

Given the complexity of web scraping (which can involve dynamic content, errors, and varied outputs), the description is incomplete. There is no output schema, and the description does not explain return values or behavioral traits. With no annotations and minimal parameter guidance beyond the schema, the description fails to provide enough context for effective tool use in a real-world scenario.

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

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

Schema description coverage is 100%, with both parameters ('url' and 'useBrowser') well-documented in the schema. The description does not add any meaning beyond what the schema provides—it mentions 'web page' which aligns with the 'url' parameter but offers no additional context for 'useBrowser' or parameter interactions. With high schema coverage, the baseline score of 3 is appropriate.

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

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

The description 'Extract table data from a web page' clearly states the verb ('extract'), resource ('table data'), and source ('web page'), making the purpose immediately understandable. It does not explicitly differentiate from sibling tools like 'extract_text' or 'scrape_html', but the focus on 'table data' provides some implicit distinction. This is clear but lacks explicit sibling differentiation.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention when to choose it over sibling tools like 'extract_text', 'scrape_html', or 'parse_csv', nor does it specify prerequisites or exclusions. Without any usage context, the agent must infer based on 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.

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 extracting text but fails to detail how it handles dynamic content (implied by the 'useBrowser' parameter), error conditions, rate limits, or output format. This leaves significant gaps in understanding 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.

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

The description is a single, direct sentence with no wasted words, making it highly efficient and front-loaded. It immediately conveys the core function without unnecessary elaboration, earning a top score for conciseness.

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

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

Given the complexity of web scraping (dynamic content, potential errors) and the lack of annotations and output schema, the description is insufficient. It does not address behavioral aspects like handling JavaScript-rendered content or return values, leaving the agent with incomplete information for effective use.

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

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

The schema description coverage is 100%, so the input schema fully documents the parameters ('url' and 'useBrowser'). The description adds no additional meaning beyond what the schema provides, such as explaining when to set 'useBrowser' to true or the expected URL format, resulting in a baseline score.

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

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

The description clearly states the action ('extract') and resource ('text content from a web page'), making the purpose immediately understandable. However, it does not explicitly differentiate from sibling tools like 'scrape_html' or 'extract_tables', which could perform similar but distinct 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.

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 'scrape_html' for raw HTML or 'extract_tables' for structured data. It lacks context about use cases, prerequisites, 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.

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's read-only or has side effects, performance characteristics, output format, or error handling. This is a significant gap 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.

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.

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

Given the complexity of code analysis, lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a list of duplicates, locations, or metrics), behavioral traits, or how it integrates with sibling tools, leaving gaps for effective agent use.

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

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

The schema description coverage is 100%, so the schema already documents both parameters ('files' and 'minLines') with descriptions and defaults. The tool description adds no additional parameter semantics beyond what's in the schema, meeting 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.

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 ('find') and resource ('duplicate code blocks across files'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'detect_code_smells' or 'analyze_code_quality', which might also involve code analysis but for different purposes.

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

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

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools for code analysis (e.g., 'detect_code_smells', 'analyze_code_quality'), there's no indication of specific contexts, prerequisites, or exclusions for this tool's application.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('Find') but doesn't describe traits like whether it's read-only, what output format to expect, if it requires specific project structures, or any performance considerations. This is a significant gap 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.

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, making it easy to scan and understand quickly, which is ideal for conciseness.

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

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

Given the tool's complexity (analyzing dependencies) and lack of annotations and output schema, the description is incomplete. It doesn't explain what 'unused' means, how dependencies are identified, or what the result looks like, leaving gaps that could hinder effective use by an agent.

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

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

The input schema has 100% description coverage, with the single parameter 'projectPath' documented as 'Path to project root'. The description doesn't add any meaning beyond this, such as examples or constraints, 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.

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

The description 'Find unused dependencies in project' clearly states the verb ('Find') and resource ('unused dependencies'), specifying what the tool does. It distinguishes from siblings like 'analyze_dependencies' or 'check_outdated_packages' by focusing on unused ones, but doesn't explicitly contrast them, keeping it at a 4 rather than a 5.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'analyze_dependencies' or 'suggest_dependency_updates'. It lacks context about prerequisites, such as needing a project with dependencies, 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.

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 'Automatically fix[es]' issues, implying mutation, but doesn't disclose behavioral traits like whether it modifies files in-place, requires specific permissions, handles errors, or has side effects. 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.

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 front-loaded and wastes no space, making it easy for an agent to parse quickly.

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

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

Given the tool's complexity (a mutation operation to fix code issues), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'fixing' entails, potential risks, return values, or error handling, leaving significant gaps for safe and effective use by an agent.

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

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

The input schema has 100% description coverage, with the 'files' parameter documented as 'File paths to fix'. The description doesn't add meaning beyond this, such as file format support or path requirements. With high schema coverage, the baseline score of 3 is appropriate, as the schema handles parameter documentation adequately.

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

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

The description clearly states the action ('Automatically fix') and target ('linting issues in code files'), which is specific and understandable. However, it doesn't distinguish this tool from its sibling 'lint_code', which presumably identifies linting issues without fixing them, leaving some ambiguity about differentiation.

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

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

The description provides no guidance on when to use this tool versus alternatives. Given siblings like 'lint_code' (likely for detection) and 'format_code' (for formatting, not necessarily linting), there's no indication of prerequisites, timing, or comparative use cases, leaving the agent to guess.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool formats code files using Prettier, implying a mutation operation that modifies files. However, it doesn't disclose critical behaviors: whether formatting is in-place or creates new files, what happens on errors, if it requires write permissions, or any rate limits. For a mutation tool with zero annotation coverage, this is a significant gap.

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

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

The description is a single, efficient sentence: 'Format code files using Prettier'. It is front-loaded with the core purpose, has zero wasted words, and is appropriately sized for a tool with clear parameters and context. Every part of the sentence earns its place by specifying the action, resource, and method.

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

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

Given the complexity (a mutation tool with 2 parameters, no output schema, and no annotations), the description is minimally complete. It states what the tool does but lacks details on behavior, usage context, or output. Without annotations or output schema, it should provide more guidance on results or errors, but it's adequate as a basic overview, leaving gaps in practical application.

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

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

Schema description coverage is 100%, with both parameters ('files' and 'options') fully described in the schema. The description adds no additional meaning beyond what the schema provides, such as examples of file paths or common Prettier options. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, but the description doesn't compensate or enhance parameter understanding.

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

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

The description clearly states the action ('format') and resource ('code files'), specifying the tool as 'Format code files using Prettier'. It distinguishes from sibling tools like 'fix_lint_issues' or 'validate_syntax' by focusing on formatting rather than fixing or validating. However, it doesn't explicitly differentiate from 'format_scraped_data', which is a formatting tool for different content.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose it over 'lint_code' (which might include formatting) or 'format_scraped_data', nor does it specify prerequisites like needing Prettier installed or appropriate file types. Usage is implied by the name and description 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.

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

With no annotations provided, the description carries full burden but only states the basic function without disclosing behavioral traits. It doesn't cover aspects like whether formatting is idempotent, error handling for invalid data, performance implications, or output structure, which are critical for a tool that transforms data.

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

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

The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part earns its place by directly stating the tool's function, 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.

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

Given the complexity of data formatting (2 parameters, nested objects, no output schema), the description is incomplete. It lacks details on output behavior, error cases, or how formats like CSV handle structured data, leaving gaps that could hinder correct tool invocation despite good schema coverage.

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

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

Schema description coverage is 100%, so the schema fully documents parameters ('data' as a scraped object, 'format' as an enum with default). The description adds no extra meaning beyond implying 'data' should be scraped, which is minimal value over the schema, meeting the baseline for high coverage.

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

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

The description clearly states the verb ('format') and resource ('scraped data'), specifying the action of converting data into different output formats. It distinguishes from siblings like 'scrape_by_selector' or 'extract_text' by focusing on post-processing rather than extraction, though it doesn't explicitly contrast with 'parse_csv' or 'parse_json' which handle specific formats.

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

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

No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing scraped data as input), exclusions, or comparisons to sibling tools like 'parse_csv' or 'parse_json', leaving the agent to infer usage context.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'Generate' but doesn't clarify if this is a read-only analysis or a write operation, what permissions might be needed, how it handles large files, or what the output looks like (e.g., report structure). For a tool with no 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.

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 simple tool, though it could be slightly more informative without losing conciseness.

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

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

Given the complexity of code analysis and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'code metrics' entail (e.g., lines of code, complexity scores), how the report is structured, or any behavioral aspects like performance or limitations. This leaves the agent with insufficient context to use the tool effectively.

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

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

The input schema has 100% description coverage, with clear documentation for 'files' and 'format' parameters. The description adds no additional meaning beyond what's in the schema (e.g., it doesn't explain what 'code metrics' include or how file paths are interpreted). Given the 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.

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

The description 'Generate detailed code metrics report' clearly states the verb ('Generate') and resource ('detailed code metrics report'), making the purpose understandable. However, it doesn't distinguish this tool from its many sibling analysis tools (e.g., analyze_code_quality, calculate_complexity), which all seem to analyze code in different ways, so it lacks sibling differentiation.

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

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 numerous sibling tools like analyze_code_quality and calculate_complexity that likely overlap in analyzing code aspects, there's no indication of what makes 'code metrics' unique or when it's preferred, leaving the agent without usage context.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('lint code files') but lacks details on what linting entails (e.g., checking for style, errors, best practices), potential side effects (e.g., no changes to files, only reporting), or operational constraints (e.g., performance impact, ESLint version requirements). 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.

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

The description is a single, efficient sentence with zero waste: 'Lint code files using ESLint'. It's front-loaded with the core action and tool, making it easy to parse. Every word earns its place by specifying the verb, resource, and implementation method without unnecessary elaboration.

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

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

Given the complexity of a linting tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., linting results, error reports), how failures are handled, or dependencies like ESLint configuration. For a tool that performs code analysis, more context is needed to guide an agent effectively, especially with rich sibling tools available.

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

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

The input schema has 100% description coverage, with the 'files' parameter documented as 'File paths to lint'. The description adds no additional parameter semantics beyond what the schema provides, such as file format expectations or path resolution rules. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't need to given the schema's completeness.

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

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

The description clearly states the verb ('lint') and resource ('code files'), and specifies the tool used ('using ESLint'). It distinguishes itself from siblings like 'validate_syntax' or 'fix_lint_issues' by focusing on linting specifically. However, it doesn't explicitly differentiate from all code analysis tools in the list, such as 'analyze_code_quality', which might overlap in purpose.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose linting over other code analysis tools like 'validate_syntax' for syntax checking or 'fix_lint_issues' for automated fixes. There's no context about prerequisites, such as requiring ESLint configuration files, or exclusions for certain file types.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Parse CSV data' implies a read-only transformation, but it doesn't specify output format (e.g., array of objects), error handling for malformed data, or performance considerations like size limits. This leaves significant gaps for a tool with no annotation coverage.

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

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

The description is extremely concise at three words, with no wasted language. It's front-loaded with the core action, though this brevity contributes to gaps in other dimensions. Every word earns its place by directly stating the tool's function.

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

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

Given no annotations and no output schema, the description is incomplete for a data parsing tool. It doesn't explain the return value (e.g., parsed structure), error cases, or usage context compared to siblings. While the schema covers inputs well, the overall tool behavior remains underspecified for effective agent use.

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

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

Schema description coverage is 100%, with clear parameter descriptions in the schema (e.g., 'CSV string to parse' for data, 'CSV delimiter' for delimiter). The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline 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.

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

The description 'Parse CSV data' clearly states the verb (parse) and resource (CSV data), making the basic purpose understandable. However, it lacks specificity about what parsing entails (e.g., converting to structured format) and doesn't differentiate from sibling tools like parse_json or parse_xml, which perform similar parsing operations on different data formats.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like parse_json or parse_xml for different data formats, or tools like extract_tables that might handle tabular data extraction. There's no context about prerequisites, such as requiring raw CSV text as input.

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

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

No annotations are provided, so the description carries the full burden. 'Parse JSON data' implies a read-only operation that transforms input, but it doesn't disclose behavioral traits like error handling (e.g., invalid JSON), performance, or output format. For a tool with zero annotation coverage, this is a significant gap.

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

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

The description is extremely concise ('Parse JSON data') with zero wasted words. It's front-loaded and efficiently communicates the core function in three words, making it easy for an agent to scan and understand.

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

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

Given no annotations, no output schema, and a simple parameter, the description is incomplete. It doesn't explain what the tool returns (e.g., parsed object, validation result) or error behavior, which are critical for an agent to use it correctly. The simplicity of the tool mitigates this somewhat, but key details are missing.

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

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

Schema description coverage is 100%, with the parameter 'data' documented as 'JSON string to parse'. The description adds no additional meaning beyond this, such as examples or constraints. With high schema coverage, the baseline score of 3 is appropriate.

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

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

The description 'Parse JSON data' clearly states the verb ('parse') and resource ('JSON data'), making the purpose immediately understandable. It doesn't differentiate from sibling tools like parse_csv or parse_xml, but the purpose is unambiguous.

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

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

The description provides no guidance on when to use this tool versus alternatives like parse_csv, parse_xml, or validate_syntax. It doesn't mention prerequisites, context, 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.

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. 'Parse XML data' doesn't reveal any behavioral traits such as error handling, output format, performance implications, or side effects. It's a minimal statement that fails to inform the agent about 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.

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

The description is extremely concise with just three words, front-loaded and free of unnecessary details. Every word ('Parse', 'XML', 'data') contributes directly to the tool's purpose, making it efficient and easy to scan.

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

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

Given the tool's complexity (parsing XML, which can involve validation, structure, etc.), no annotations, and no output schema, the description is incomplete. It doesn't explain what the parsing outputs (e.g., structured data, errors), how it handles malformed XML, or any dependencies, leaving significant gaps for the agent to operate effectively.

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

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

The input schema has 100% description coverage, with the single parameter 'data' documented as 'XML string to parse'. The description adds no additional meaning beyond this, so it meets the baseline of 3 where the schema does the heavy lifting, but doesn't compensate or enhance understanding.

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

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

The description 'Parse XML data' clearly states the verb ('parse') and resource ('XML data'), but it's vague about what parsing entails (e.g., validation, transformation, extraction) and doesn't distinguish from sibling tools like 'parse_csv' or 'parse_json' beyond the data format. It meets the basic requirement but lacks specificity.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'parse_csv' or 'parse_json' for different data formats, or other XML-related tools if they exist, 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.

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

No annotations are provided, so the description carries the full burden. It mentions scanning for security issues but doesn't disclose behavioral traits such as whether this is a read-only operation, if it modifies files, what permissions are required, performance characteristics, or output format. For a security scanning tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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

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

The description is a single, efficient sentence that front-loads the core action ('scan code for security issues') and elaborates with examples ('including secrets, vulnerabilities, and insecure patterns'). There is zero wasted verbiage, and it's 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.

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

Given the complexity of security scanning, no annotations, no output schema, and incomplete behavioral disclosure, the description is inadequate. It doesn't explain what the tool returns, how results are structured, or critical behavioral aspects. For a tool with potential side effects or significant output, 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.

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

Schema description coverage is 100%, with the single parameter 'files' fully documented in the schema as 'File paths to scan'. The description adds no additional parameter semantics beyond what the schema provides, such as file format constraints or scanning depth. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't need to heavily.

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

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

The description clearly states the verb 'scan' and resource 'code' with specific targets: 'security issues including secrets, vulnerabilities, and insecure patterns'. This distinguishes it from siblings like 'detect_secrets' (narrower) and 'check_vulnerabilities' (different focus), though it doesn't explicitly contrast with them. The purpose is specific but could better differentiate from overlapping tools.

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

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

No guidance on when to use this tool versus alternatives like 'detect_secrets' or 'check_vulnerabilities' is provided. The description implies a broad security scan context, but it lacks explicit when/when-not instructions or prerequisites. Usage is only vaguely implied by the tool's name and description.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the action ('scrape') but doesn't specify what happens—e.g., whether it returns text, HTML, or structured data, if it handles errors, requires authentication, or has rate limits. 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.

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

The description is a single, efficient sentence with no wasted words. It is front-loaded with the core purpose, making it easy to parse quickly, though it could benefit from 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.

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

Given the complexity of a scraping tool with no annotations, no output schema, and multiple parameters, the description is incomplete. It doesn't explain what the tool returns, how errors are handled, or any behavioral nuances, 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.

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, selector, useBrowser). The description adds no additional meaning beyond the schema, such as explaining selector syntax or useBrowser implications. Baseline 3 is appropriate when the schema does all the work.

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

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

The description clearly states the action ('scrape content') and the method ('using CSS selector'), which is specific and distinguishes it from generic scraping tools. However, it doesn't explicitly differentiate from sibling tools like 'scrape_html' or 'scrape_dynamic_content', which likely have overlapping purposes but different approaches.

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

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

The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'scrape_dynamic_content' and 'scrape_html' available, there is no indication of scenarios where CSS selector-based scraping is preferred or when other methods might be more appropriate.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'using browser' which implies headless browser automation, but fails to detail critical behaviors like performance impact, resource usage, error handling, or output format. For a tool that likely involves network requests and JavaScript execution, 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.

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 ('scrape', 'JavaScript-rendered content', 'using browser') earns its place by conveying essential information concisely.

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

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

Given the complexity of scraping dynamic content (which involves browser automation, potential errors, and varied outputs) and the absence of both annotations and an output schema, the description is incomplete. It doesn't address what the tool returns (e.g., HTML, text, structured data), error conditions, or performance considerations, making it inadequate for safe and effective use by an agent.

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

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

Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no additional semantic context beyond what's in the schema (e.g., it doesn't explain how 'waitForSelector' interacts with JavaScript rendering or typical timeout values). With high schema coverage, the baseline score of 3 is appropriate as 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.

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

The description clearly states the action ('scrape') and target ('JavaScript-rendered content'), and specifies the method ('using browser'), which distinguishes it from non-browser scraping tools. However, it doesn't explicitly differentiate from sibling tools like 'scrape_html' or 'scrape_by_selector', which likely handle different content types or methods.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention scenarios like scraping dynamic websites versus static ones, or compare it to sibling tools such as 'scrape_html' or 'scrape_with_interaction', 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.

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 scraping HTML content but doesn't address critical aspects like error handling, rate limits, authentication needs, response format, or whether it performs destructive operations. This leaves significant gaps for an agent to understand how the tool behaves in practice.

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

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 no wasted words. It's appropriately sized for a tool with this complexity and is front-loaded with the core functionality.

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

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

Given the lack of annotations and output schema, the description is insufficiently complete. It doesn't explain what the tool returns (e.g., raw HTML, structured data), error conditions, or performance characteristics. For a scraping tool with multiple parameters and no structured output documentation, more context is needed.

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

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

Schema description coverage is 100%, so the schema fully documents all four parameters (url, useBrowser, timeout, headers). The description adds no additional parameter semantics beyond what's already in the schema, which meets the baseline expectation but doesn't provide extra value.

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

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

The description clearly states the action ('scrape') and resource ('HTML content from a URL'), making the purpose immediately understandable. However, it doesn't differentiate from sibling scraping tools like scrape_by_selector, scrape_dynamic_content, or scrape_with_interaction, 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.

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 scraping tools available (e.g., scrape_dynamic_content, scrape_by_selector), the agent receives no indication of which tool is appropriate for different scenarios like static vs. dynamic content or targeted extraction.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions performing interactions like click and scroll, but doesn't describe what happens after interactions (e.g., does it return HTML, text, or something else?), potential side effects, error handling, or performance considerations. This is a significant gap for a tool that likely involves complex web interactions.

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

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

The description is a single, efficient sentence that front-loads the core action ('Scrape content') and key constraint ('after user interactions'). There is zero waste, and it's 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.

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

Given the complexity of a tool that performs web interactions and scraping, with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., scraped data format), error conditions, or behavioral nuances, leaving significant gaps for the agent to infer.

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

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

Schema description coverage is 100%, so the schema already documents both parameters ('url' and 'interactions') thoroughly. The description adds no additional meaning beyond what's in the schema, such as explaining the purpose of interactions or providing examples. 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.

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 ('scrape') and resource ('content'), and it adds the key constraint 'after user interactions (click, scroll, etc.)'. However, it doesn't explicitly differentiate from sibling tools like 'scrape_dynamic_content' or 'extract_after_click', which appear related, so it misses full sibling distinction.

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

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 with sibling tools such as 'scrape_dynamic_content' or 'extract_after_click', leaving the agent with no usage context 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.

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. 'Take a screenshot' implies a read-only operation that captures visual output, but it doesn't mention critical behaviors like whether it requires network access, handles dynamic content, returns an image file or data, or has any rate limits or permissions needed.

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

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

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core action and resource, making it easy to scan and understand immediately.

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

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

Given the tool's complexity (capturing web content with parameters for full-page and waiting), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., image data, file path, error handling) or behavioral constraints, leaving significant gaps for an agent to use it correctly.

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

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 three parameters (url, fullPage, waitForSelector). The description adds no additional meaning about parameters beyond implying a 'web page' context for the URL, which aligns with the schema. This meets the baseline for high schema coverage.

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

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

The description 'Take a screenshot of a web page' clearly states the verb ('Take') and resource ('a screenshot of a web page'), making the purpose immediately understandable. However, it doesn't differentiate from potential sibling tools like 'extract_images' or 'scrape_html' that might also capture visual content, 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.

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 analysis, extraction, and scraping, there's no indication of whether this is for visual capture versus content extraction, or any prerequisites like needing a live webpage.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'analysis' but doesn't detail what the analysis entails (e.g., static analysis, runtime checks), whether it's read-only or modifies files, what permissions are needed, or how results are presented. This leaves significant gaps in understanding 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.

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 is appropriately sized for a simple tool, though it could be more front-loaded with specific details to improve clarity.

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

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

Given the complexity of dependency analysis (which can involve multiple factors like versions, security, compatibility), no annotations, no output schema, and a vague description, the description is incomplete. It fails to provide enough context for an agent to understand the tool's scope, behavior, or results, making it inadequate for effective use.

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

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

The input schema has 1 parameter with 100% description coverage ('Path to project root'), so the schema already documents it fully. The description doesn't add any parameter-specific information beyond the schema, but with 0 parameters needing compensation and high schema coverage, the baseline is 4 as it doesn't detract from the schema's clarity.

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

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

The description 'Suggest dependency updates based on analysis' states a general purpose but lacks specificity about what kind of dependency updates (e.g., version upgrades, security patches, compatibility fixes) or what analysis method is used. It distinguishes from some siblings like 'check_outdated_packages' by implying suggestions rather than just checking, but the distinction is vague.

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

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

No explicit guidance on when to use this tool versus alternatives like 'check_outdated_packages' or 'check_vulnerabilities' is provided. The description implies analysis-based suggestions, but it doesn't specify prerequisites, context, or exclusions, leaving the agent to infer usage scenarios.

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

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

With no annotations, the description carries full burden but only states it 'suggests' based on analysis. It doesn't disclose behavioral traits such as whether it's read-only, what analysis methods it uses, if it requires specific permissions, or what the output format looks like, leaving significant gaps.

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

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.

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 single parameter with high schema coverage, the description is incomplete. It doesn't explain what refactoring opportunities are suggested, how they're presented, or any behavioral context, making it inadequate for effective use.

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

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

Schema description coverage is 100%, so the schema already documents the 'files' parameter. The description adds no additional meaning beyond implying code analysis, which is minimal value over 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.

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

The description clearly states the verb ('suggest') and resource ('refactoring opportunities'), specifying it's based on code analysis. It distinguishes from siblings like 'detect_code_smells' or 'find_duplicate_code' by focusing on suggestions rather than detection, but doesn't explicitly differentiate them.

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

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

No guidance on when to use this tool versus alternatives like 'detect_code_smells' or 'analyze_code_quality' is provided. The description implies usage for code analysis but lacks context about prerequisites, appropriate scenarios, or exclusions.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure but offers minimal information. It mentions HTTP request testing but doesn't describe what 'testing' entails (e.g., whether it validates responses, checks status codes, measures performance, or handles authentication). There's no mention of rate limits, error handling, or output format, leaving significant gaps for a tool that interacts with external systems.

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

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

The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's appropriately sized for a straightforward tool and front-loads the core purpose. Every word earns its place, making it easy for an agent to parse quickly.

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

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

Given the tool's complexity (interacting with external APIs via HTTP) and lack of annotations or output schema, the description is insufficient. It doesn't explain what constitutes a 'test', what the tool returns (e.g., response data, status codes, errors), or behavioral aspects like timeouts or retries. For a 4-parameter tool with no structured safety or output information, this leaves the agent under-informed.

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

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

The input schema has 100% description coverage, so parameters are well-documented in the schema itself. The description adds no additional semantic context about parameters beyond implying they're used for HTTP requests. This meets the baseline of 3 since the schema does the heavy lifting, but the description doesn't enhance understanding of how parameters interact or their practical use.

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

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

The description clearly states the tool's purpose as 'Test an API endpoint with HTTP request', which specifies the verb (test) and resource (API endpoint). It distinguishes itself from sibling tools like 'discover_api_endpoints' or 'extract_api_schema' by focusing on testing rather than discovery or extraction. However, it doesn't explicitly differentiate from potential overlapping tools like 'analyze_network_requests'.

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

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

The description provides no guidance on when to use this tool versus alternatives. While the purpose is clear, there's no mention of specific use cases, prerequisites, or comparisons to sibling tools like 'analyze_network_requests' or 'scrape_with_interaction' that might also involve HTTP requests. The agent must infer usage context 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.

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 ('validate syntax') but doesn't explain what validation entails (e.g., returns errors/warnings, requires specific file types, has performance implications). This leaves significant gaps for a tool with no annotation coverage.

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

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's appropriately sized and front-loaded, making it easy to understand at a glance.

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

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

For a tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the validation output looks like (e.g., success/failure, error details), what file types are supported, or how it differs from similar sibling tools, leaving too many contextual gaps.

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

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

The schema description coverage is 100%, with the 'files' parameter clearly documented as 'File paths to validate'. The description adds no additional parameter information beyond what's in the schema, so it meets the baseline for adequate but unenhanced parameter documentation.

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

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

The description clearly states the verb ('validate') and resource ('syntax of code files'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'lint_code' or 'check_typescript' that might perform similar syntax-related functions, 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.

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 'lint_code', 'check_typescript', and 'analyze_code_quality', there's no indication of what makes this tool distinct or when it should be preferred over those options.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions waiting and extraction but lacks details on error handling, what happens on timeout, whether it interacts with dynamic content, or the format of extracted content. For a tool with no annotations, 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.

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 understand at a glance.

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

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

Given the complexity of a web scraping/waiting tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'extract its content' means in practice, how errors are handled, or what the return value looks like, leaving too much ambiguity for effective use.

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

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

Schema description coverage is 100%, so the schema already documents all parameters (url, selector, timeout). The description adds no additional meaning beyond implying that the selector is used to wait for an element and extract content, but it doesn't clarify parameter interactions or usage nuances. Baseline 3 is appropriate given high schema coverage.

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

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

The description clearly states the tool's purpose with a specific verb ('wait for') and resource ('element'), and mentions the action ('extract its content'). However, it doesn't explicitly differentiate from sibling tools like 'scrape_by_selector' or 'extract_text', which appear to 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.

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 'scrape_by_selector', 'extract_text', and 'scrape_dynamic_content' that might handle similar tasks, there's no indication of specific scenarios, 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.