rankoracle

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions a subscription requirement, which adds some context about access needs. However, it lacks details on behavioral traits such as rate limits, response format, whether it's a read-only or mutating operation, or any side effects. For a tool with zero annotation coverage, this is a significant gap, making it minimally transparent.

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 appropriately sized and front-loaded: it states the core purpose in the first part and adds a crucial prerequisite in parentheses. Every sentence (or clause) earns its place by providing essential information without waste, 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 tool's complexity (a single-parameter check operation), no annotations, no output schema, and high schema coverage, the description is somewhat complete but has gaps. It covers the purpose and a key prerequisite, but lacks details on behavioral aspects and output. For a tool with no annotations or output schema, it should do more to compensate, making it only adequately complete.

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

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

The input schema has 100% description coverage, with the 'domain' parameter clearly documented as 'Domain to check'. The description doesn't add any additional meaning or semantics beyond what the schema provides. According to the rules, with high schema coverage (>80%), the baseline score is 3 when no extra param info is in the description, which applies here.

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: 'Check backlinks and referring domains for a domain'. It specifies the action ('check') and resource ('backlinks and referring domains'), making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'domain_overview' or 'health_check', which might also involve domain analysis, 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 includes a usage guideline: '(requires Backlinks subscription)', which indicates a prerequisite for using the tool. This provides some context on when it can be used. However, it doesn't offer explicit guidance on when to choose this tool over alternatives like 'domain_overview' or other sibling tools, nor does it specify exclusions or detailed scenarios, so it's only implied 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?

With no annotations provided, the description carries full burden but offers minimal behavioral context. It mentions 'Google top 100' which implies a search engine ranking check, but doesn't disclose rate limits, authentication needs, data freshness, or what happens if the domain isn't in the top 100. For a tool with no annotation coverage, this leaves significant gaps.

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

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 this tool's complexity and front-loads the 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 no annotations and no output schema, the description is incomplete. It doesn't explain what the return value looks like (e.g., rank number, 'not in top 100', error cases) or important behavioral aspects like rate limits or data sources. For a ranking check tool with 3 parameters, this leaves the agent with insufficient 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 all three parameters adequately. The description adds no additional parameter semantics beyond what's in the schema (e.g., format examples, constraints, or relationships between parameters). 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 with specific verb ('check') and resource ('domain ranking for a keyword in Google top 100'). It distinguishes from siblings like 'rank_tracker' (likely tracks over time) and 'serp_snapshot' (likely captures full SERP) by focusing on a single ranking check for a specific domain-keyword pair.

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 'rank_tracker' or 'serp_snapshot'. It doesn't mention prerequisites, limitations, or typical use cases. The agent must 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?

No annotations are provided, so the description carries the full burden. It mentions the tool's purpose but doesn't disclose behavioral traits such as whether it's read-only or mutative, rate limits, authentication needs, or what the output looks like. For a tool with no annotations, 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 waste. It's front-loaded with the core action and immediately states the goal. Every word earns its place, making it highly concise and well-structured.

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

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

Given no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns, how results are formatted, or any behavioral constraints. For a tool with 4 parameters and no structured output information, more context is needed to guide effective use.

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

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. The description doesn't add any meaning beyond what the schema provides—it doesn't explain how parameters interact or their impact on results. 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: 'Find keywords your competitor ranks for' specifies the verb and resource. It adds value by explaining the goal: 'identify content gaps and opportunities.' However, it doesn't explicitly differentiate from sibling tools like 'keyword_research' or 'check_ranking,' which might have overlapping functionality.

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

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

No guidance is provided on when to use this tool versus alternatives. The description mentions the goal but doesn't specify prerequisites, exclusions, or recommend other tools for related tasks. With multiple sibling tools in the SEO/ranking domain, this lack of differentiation is a significant gap.

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 'score, readability, technical checks, recommendations' but doesn't detail what these entail, such as whether it performs external requests, has rate limits, requires authentication, or returns structured data. This leaves significant gaps in understanding the tool's behavior beyond basic functionality.

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 ('Analyze URL for on-page SEO quality') followed by specific aspects. It avoids redundancy and wastes no words, though it could be slightly more structured by separating key components for 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 SEO analysis (involving multiple checks and outputs), no annotations, and no output schema, the description is incomplete. It lists aspects like 'score, readability, technical checks, recommendations' but doesn't explain what these return or how they're structured, leaving the agent uncertain about the tool's full behavior and 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 ('url' and 'keyword') with clear descriptions. The description implies URL analysis and keyword optimization but adds no additional meaning beyond what the schema provides, such as format requirements or interaction effects. Baseline 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: analyzing a URL for on-page SEO quality, including scoring, readability, technical checks, and recommendations. It specifies the verb 'analyze' and resource 'URL' with detailed aspects, though it doesn't explicitly differentiate from sibling tools like 'health_check' or 'heading_analysis' which might overlap in SEO 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. It doesn't mention sibling tools like 'health_check' (which might cover technical aspects) or 'heading_analysis' (which might focus on specific elements), leaving the agent to infer usage based on tool names alone without explicit context 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions what data is returned (keywords, traffic, position) but doesn't cover critical aspects like whether this is a read-only operation, requires authentication, has rate limits, or what the output format looks like. For a tool with no annotation coverage, this is a significant gap.

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

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

The description is appropriately concise and front-loaded, using a single sentence that efficiently lists the key outputs. There's no wasted verbiage, though it could be slightly more structured (e.g., clarifying it's a read operation).

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 (SEO analysis with 2 parameters) and lack of annotations or output schema, the description is minimally adequate. It specifies the core outputs but misses behavioral context and usage guidance. Without an output schema, it should ideally hint at the return format, but the description doesn't compensate fully for these 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?

Schema description coverage is 100%, so the schema already documents both parameters ('domain' and 'country'). The description adds no additional meaning beyond what the schema provides—it doesn't explain parameter interactions, default behaviors, or usage examples. The baseline score of 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose: providing a domain SEO overview with specific metrics (organic keywords, traffic estimate, position distribution). It uses specific verbs ('overview') and resources ('domain SEO'), though it doesn't explicitly differentiate from sibling tools like 'health_check' or 'serp_snapshot' which might offer 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 sibling tools like 'health_check', 'serp_snapshot', and 'rank_tracker' that might offer related SEO insights, there's no indication of this tool's specific context or exclusions, leaving the agent to guess based on the name alone.

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

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 'SEO recommendations' but doesn't specify what these entail (e.g., format, depth, actionable insights) or any operational traits like rate limits, authentication needs, or potential side effects. 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 directly states the tool's function without unnecessary words. It's front-loaded with the core purpose and avoids redundancy, 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 moderate complexity (SEO analysis with one parameter) and lack of annotations or output schema, the description is minimally adequate. It covers the basic purpose but misses details on behavior, output format, and usage context. Without an output schema, the description should ideally hint at return values, but it doesn't, leaving gaps in completeness.

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 'url' clearly documented. The description doesn't add any meaning beyond what the schema provides (e.g., it doesn't specify URL format requirements or constraints). Since schema coverage is high, 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 clearly states the tool's purpose: analyzing heading structure (H1-H4) and providing SEO recommendations. It specifies both the action ('analyze') and the resource ('heading structure of a page'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'title_optimizer' or 'content_score', which might also involve SEO analysis, 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 any prerequisites, exclusions, or specific contexts where this analysis is preferred over other SEO tools in the sibling list. This leaves the agent without clear direction on 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?

With no annotations provided, the description carries full burden but only states what information is returned, not behavioral traits. It doesn't disclose whether this is a read-only operation, if it requires authentication, potential rate limits, or what happens during server downtime. The description is purely informational about output content.

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 perfectly concise - a single comma-separated phrase listing exactly what information the tool provides. Every word earns its place by specifying distinct diagnostic components without any redundant or explanatory text.

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

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

For a zero-parameter diagnostic tool with no output schema, the description adequately covers what information is returned. However, without annotations and given the tool's potential importance for system monitoring, it could benefit from mentioning whether this is a lightweight check or has performance implications, and what format the health information is returned in.

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

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

The tool has zero parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, and the empty input schema is self-explanatory for a no-parameter diagnostic tool.

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

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

The description clearly states what the tool does ('Server health, version, tool status, and API connectivity') with specific components listed. It distinguishes from sibling tools by focusing on system diagnostics rather than SEO/ranking functions, though it doesn't explicitly name alternatives for similar health checks.

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, timing considerations, or relationship to other tools in the server's ecosystem. The agent must 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It lists what metrics are researched but does not describe how the tool behaves: e.g., whether it requires authentication, has rate limits, returns structured data, or handles errors. For a tool with no annotations, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence that lists all key metrics without unnecessary words. It is front-loaded with the core action ('Research') and directly enumerates the outputs, making it easy to parse and understand quickly. Every word serves a 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 keyword research (involving multiple metrics and potential data sources), no annotations, and no output schema, the description is incomplete. It does not explain what the return values look like (e.g., format of volume, difficulty scales), error handling, or any behavioral traits, leaving significant gaps for an AI agent to use the tool effectively.

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

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

The description does not add meaning beyond what the input schema provides. The schema has 100% description coverage for both parameters ('country' and 'keyword'), clearly documenting their purposes and requirements. Since the description does not elaborate on parameter usage or constraints, it meets the baseline score of 3 for high schema coverage.

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

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

The description clearly states the tool's purpose: researching keyword metrics (volume, difficulty, CPC, trends, related keywords). It uses specific verbs ('research') and identifies the resource ('keyword'), but does not explicitly differentiate from sibling tools like 'competitor_gap' or 'serp_snapshot', which might also involve keyword analysis. This makes it clear but not fully sibling-distinctive.

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 any context, prerequisites, or exclusions, such as when to choose 'keyword_research' over 'competitor_gap' or 'serp_snapshot' from the sibling list. This lack of usage instructions leaves the agent without clear 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'analyze and generate optimized' but doesn't specify what 'optimized' entails, whether it involves AI processing, rate limits, authentication needs, or output format. This leaves significant gaps in understanding the tool's behavior beyond basic functionality.

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 wasted words. It is front-loaded and appropriately sized, 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'optimized' means, the format of the generated meta data, or any behavioral traits like processing time or error handling. For a tool that generates content, more context is needed to guide effective use.

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

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 already documents both parameters ('url' and 'keyword') adequately. The description adds no additional meaning beyond implying the URL is analyzed and optimized with a keyword, which is already suggested by 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 clearly states the tool's purpose with specific verbs ('analyze' and 'generate') and resource ('meta title + description for a URL'), making it easy to understand what it does. However, it doesn't differentiate from sibling tools like 'title_optimizer' or 'content_score', which might 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 like 'title_optimizer' or 'content_score' among the siblings. It lacks explicit context, exclusions, or prerequisites, leaving the agent to infer usage based on 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 states the tool tracks ranking positions but doesn't describe what 'track' entails (e.g., real-time monitoring, historical data, frequency), output format, error handling, or any limitations beyond the keyword count. For a tool with no annotation coverage, this is insufficient to inform the agent about 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 with zero waste: 'Track ranking positions for multiple keywords at once (max 10)'. It front-loads the core purpose and includes a key constraint concisely, making it easy for the 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 (tracking rankings for multiple keywords), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., ranking data, timestamps, errors) or behavioral aspects like rate limits or data sources. For a tool with no structured output or annotation support, more context is needed to be fully 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?

The input schema has 100% description coverage, with clear documentation for 'domain', 'country', and 'keywords'. The description adds minimal value beyond the schema by implying the 'keywords' parameter supports multiple items ('multiple keywords at once') and has a constraint ('max 10'), but doesn't provide additional syntax or format details. 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 tool's purpose: 'Track ranking positions for multiple keywords at once' with a specific verb ('track') and resource ('ranking positions'). It distinguishes from siblings like 'check_ranking' by specifying batch capability ('multiple keywords at once') and a constraint ('max 10'), though it doesn't explicitly contrast with all alternatives. This is clear but lacks full sibling differentiation.

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

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 'check_ranking' or 'serp_snapshot'. It mentions a constraint ('max 10') but doesn't explain why to choose this tool over others for tracking rankings, nor does it mention prerequisites or exclusions. This leaves the agent without clear 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 mentions tracking changes over time, which implies historical comparison, but doesn't detail aspects like rate limits, authentication needs, data retention, error handling, or whether it's a read-only or mutative operation. For a tool with no annotations, 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 concise and front-loaded, consisting of a single, clear sentence: 'Check current SERP position and compare against previous check — tracks changes over time'. Every word contributes to understanding the tool's purpose, with no wasted information, making it efficient and easy to parse.

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

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

Given the complexity of SERP tracking and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., position numbers, change metrics, timestamps), how it handles missing data, or any dependencies. For a tool that likely involves data analysis over time, more context is needed to fully understand its operation and outputs.

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 input schema already documents all parameters (domain, country, keyword) with descriptions. The description adds no additional meaning or context beyond what the schema provides, such as explaining interactions between parameters or usage examples. Thus, it meets the baseline for high schema coverage without adding 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 tool's purpose: 'Check current SERP position and compare against previous check — tracks changes over time'. It specifies the verb ('check', 'compare', 'tracks') and resource ('SERP position'), making the function evident. However, it doesn't explicitly differentiate from sibling tools like 'check_ranking' or 'rank_tracker', which might have overlapping functions, 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 sibling tools like 'check_ranking' or 'rank_tracker', nor does it specify contexts, prerequisites, or exclusions for usage. This lack of comparative or contextual advice limits its utility for an AI agent in selecting the right tool.

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 getting results with SERP features, but doesn't cover critical aspects like rate limits, authentication needs, pagination, error handling, or whether this is a read-only operation. For a tool that likely makes external API calls, 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 that clearly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded with the core functionality.

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

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

Given the tool's moderate complexity (external search query) and lack of annotations or output schema, the description is minimally adequate but incomplete. It explains what the tool does but doesn't provide enough context about behavior, limitations, or results format to be fully helpful for an AI 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 already documents both parameters ('country' and 'keyword') adequately. The description doesn't add any meaningful parameter semantics beyond what's in the schema, such as format examples or constraints, but doesn't need to since the schema is comprehensive.

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 ('Get') and resource ('top 10 Google results for a keyword with SERP features'), making it easy to understand what the tool does. However, it doesn't explicitly distinguish itself from sibling tools like 'check_ranking' or 'rank_tracker' that might also involve search results, 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 siblings like 'check_ranking' and 'rank_tracker' that likely relate to search rankings, there's no indication of when this snapshot tool is preferred over those, leaving usage context unclear.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions analysis and generation but doesn't cover critical aspects like whether this is a read-only operation, if it requires internet access to fetch URLs, potential rate limits, or the format of output suggestions. For a tool with no annotations, 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 purpose without unnecessary words. Every part earns its place by clearly stating the action and outcome, 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 (SEO analysis tool with 3 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., list of suggestions, scores), behavioral traits, or usage context. This leaves the agent with insufficient information to fully understand the tool's operation.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters (url, keyword, draft_title) with descriptions. The description adds no additional meaning beyond what the schema provides, such as explaining how parameters interact (e.g., if both url and draft_title are provided). Baseline is 3 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: 'Analyze current title tag and generate SEO-optimized title suggestions.' It specifies the verb ('analyze' and 'generate'), resource ('title tag'), and outcome ('SEO-optimized title suggestions'). However, it doesn't explicitly differentiate this from sibling tools like 'meta_generator' or 'content_score,' which might have overlapping SEO functions, so it doesn't reach 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. It doesn't mention sibling tools like 'meta_generator' (which might handle meta tags) or 'content_score' (which could involve title analysis), nor does it specify prerequisites or exclusions. Usage is implied from the purpose but lacks explicit context.

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