SuperHero MCP Server

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 'Get detailed performance analytics,' which implies a read-only operation, but doesn't specify aspects like data freshness, rate limits, authentication needs, or what 'detailed' entails (e.g., metrics included, time ranges). 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 any fluff or redundancy. 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 analytics tools and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'performance analytics' includes (e.g., metrics, time periods), how data is returned, or any behavioral traits. For a tool that likely returns structured data, 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 the single parameter 'agent_id' documented as 'The agent ID to query.' The description doesn't add any meaning beyond this, such as where to find the agent ID or format requirements. Given the high schema coverage, the baseline score of 3 is appropriate.

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

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

The description clearly states the verb 'Get' and the resource 'detailed performance analytics for a specific AI agent,' making the purpose explicit. However, it doesn't distinguish this tool from potential sibling tools like 'list_agents' or 'url_analytics,' which might also involve agent-related data, so it misses full 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. For example, it doesn't specify if this should be used after 'list_agents' to get details for a specific agent, or if there are prerequisites like needing an agent ID first. This lack of context leaves usage 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 'automatically apply,' implying a write/mutation operation, but does not specify permissions needed, rate limits, whether applications are reversible, or what the response looks like (e.g., success/failure status). For a tool that likely modifies data, 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 functionality: 'Automatically apply to multiple jobs using a saved resume.' It avoids redundancy and wastes no words, making it highly concise and well-structured for quick comprehension.

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

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 with no annotations and no output schema), the description is incomplete. It does not explain behavioral aspects like error handling, return values, or dependencies on other tools (e.g., needing job IDs from 'search_jobs'). For a tool that performs automated applications, more context is needed to ensure proper agent usage.

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 'resume_id' and 'job_ids'. The description adds minimal value beyond the schema, only implying that 'resume_id' refers to a 'saved resume' and 'job_ids' are for 'multiple jobs.' This meets the baseline of 3, as the schema does the heavy lifting, but the description does not enhance parameter understanding significantly.

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: 'Automatically apply to multiple jobs using a saved resume.' It specifies the verb ('apply'), resource ('jobs'), and mechanism ('using a saved resume'), making the function evident. However, it does not distinguish this tool from potential siblings like 'search_jobs' or 'build_resume', which could be related but serve different purposes.

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

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 lacks context on prerequisites (e.g., needing a saved resume or job IDs from another tool), exclusions (e.g., not for single applications), or explicit comparisons to siblings like 'search_jobs' or 'build_resume'. This leaves the agent without clear 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the output ('downloadable PDF link and ATS score') but doesn't cover important aspects like whether this is a generative/mutation operation, authentication requirements, rate limits, processing time, or what happens with the user's data. The description is insufficient for a tool that likely creates personal documents.

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 two sentences that efficiently communicate the core functionality and output. Every word earns its place, and the information is front-loaded with the primary purpose stated first.

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 likely creates personal documents with user data, the description is incomplete. With no annotations and no output schema, it should explain more about the operation's nature (generative vs. analytical), data handling, and expected outputs. The mention of PDF and ATS score helps but doesn't provide enough context for safe and 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 three parameters. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions 'target role' which aligns with one parameter but doesn't provide additional context about format, constraints, or how parameters interact.

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 ('build') and resource ('ATS-optimized resume'), and distinguishes it from siblings by specifying the output format ('downloadable PDF link and ATS score'). It explicitly mentions the target role optimization, which differentiates it from generic content 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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, limitations, or compare it to sibling tools like 'optimize_content' or 'optimize_linkedin' that might handle similar resume-related tasks. Usage context is implied 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Generate a complete business website' suggests a creation/mutation operation, it doesn't specify what 'complete' entails (e.g., number of pages, features included), whether authentication is required, potential rate limits, or what the output format might be. For a tool that likely produces significant output 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 that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and includes all essential elements (verb, resource, inputs). Every part of the sentence earns its place.

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 generates a 'complete' website with no annotations and no output schema, the description is insufficient. It doesn't explain what 'complete' means, what the output includes (e.g., HTML files, deployment instructions), or any behavioral constraints. Given the complexity implied by website generation, 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%, with both parameters ('business_name' and 'industry') clearly documented in the schema. The description mentions these parameters but adds no additional semantic context beyond what the schema provides, such as examples of valid industries or naming conventions. The baseline 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 specific action ('Generate a complete business website') and the resources involved ('from a business name and industry'). It distinguishes itself from sibling tools like 'build_resume' or 'optimize_content' by focusing on website generation rather than other content creation or optimization tasks.

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, limitations, or compare it to similar tools like 'replit_deploy' or 'shopify_store_audit' that might also involve website-related functionality. The usage context is implied 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions estimating cost but doesn't specify whether this is a read-only operation, if it requires authentication, what the output format might be, or any rate limits. The description is minimal and lacks details on how the estimation is performed or any constraints.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without any fluff. It's front-loaded and appropriately sized, making it easy for an agent to quickly understand the core 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 the complexity of cost estimation and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the output will look like (e.g., cost breakdown, currency), any assumptions made in the estimation, or error conditions. For a tool with no structured output, more descriptive context is needed to guide the agent 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 all parameters (model, prompt, estimated_calls). The description implies the use of 'prompt' and 'estimated_calls' but adds no additional semantic context beyond what's in the schema, such as explaining how the prompt influences cost or what units estimated_calls uses. Baseline 3 is appropriate as the schema handles 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 tool's purpose: 'Estimate the cost of running a prompt across Claude models at a given call volume.' It specifies the verb ('estimate'), resource ('cost'), and scope ('Claude models'), though it doesn't explicitly differentiate from sibling tools like 'claude_prompt_optimize' or 'agent_analytics' which might have related but distinct functions.

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, ideal scenarios, or exclusions, leaving the agent to infer usage based on the purpose alone. For example, it doesn't clarify if this is for budgeting, comparison with other models, or pre-deployment planning.

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 optimizes prompts but doesn't explain how optimization works (e.g., techniques used, whether it's iterative or one-shot, potential side effects like changing prompt meaning, or response format). For a tool that modifies content with quality implications, this lack of detail is a significant gap.

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

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 with the core action ('Optimize a prompt') and includes key benefits. Every part of the sentence contributes to understanding, 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 prompt optimization (which can involve nuanced changes affecting model behavior), the description is incomplete. No annotations exist to clarify safety or behavior, and there's no output schema to explain return values. The description doesn't cover how optimizations are applied or what users can expect, leaving gaps in understanding 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?

The input schema has 100% description coverage, with clear parameter descriptions: 'model' specifies the target Claude model, and 'prompt' is the input to optimize. The description adds no additional semantic context beyond what the schema provides, such as examples of optimization outcomes or constraints on prompt length. 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 clearly states the tool's purpose: 'Optimize a prompt for Claude models to reduce token usage and improve output quality.' It specifies the verb ('optimize'), resource ('prompt'), and goals ('reduce token usage and improve output quality'). However, it doesn't explicitly differentiate from sibling tools like 'optimize_content' or 'optimize_linkedin', which might have overlapping optimization purposes but different 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. It doesn't mention prerequisites, context for optimization (e.g., before sending to Claude models), or comparisons with sibling tools such as 'optimize_content' or 'claude_cost_estimate'. Without this, users 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a connection operation but doesn't clarify if this requires admin permissions, whether it's idempotent, what happens on failure, or if it modifies existing connections. For a mutation 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 that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.

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

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

Given this is a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what happens after connection (e.g., success response, error handling), nor does it address behavioral aspects like permissions or side effects, which are critical for safe invocation.

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 ('service_name' and 'credentials'). The description doesn't add any meaningful context beyond what's in the schema, such as examples of valid service names or credential formats, so it meets the baseline but doesn't 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 clearly states the action ('connect') and the resource ('third-party service to a StartBiz website'), making the purpose understandable. However, it doesn't differentiate this tool from potential siblings like 'list_integrations' or other integration-related tools, which prevents a perfect score.

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

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 'list_integrations' or other sibling tools. There's no mention of prerequisites, conditions for use, or what happens if a service is already connected, 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 full burden. It mentions 'create and deploy' which implies a write operation with potential side effects, but doesn't disclose behavioral traits like authentication requirements, rate limits, deployment time, or whether the agent starts immediately. This is inadequate for a tool that creates and deploys resources.

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?

Single sentence, zero waste, front-loaded with the core action. Every word earns its place by conveying essential information without redundancy.

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 creates and deploys autonomous agents with no annotations and no output schema, the description is incomplete. It lacks information about what happens after deployment, error conditions, or the nature of the created agent. Given the complexity implied by 'autonomous AI agent,' 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 already documents all parameters. The description adds no additional meaning about parameters beyond what's in the schema, such as explaining the 'type' enum values or what 'config' might include. 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 action ('create and deploy') and resource ('autonomous AI agent'), with specificity about task types. However, it doesn't differentiate from potential sibling tools like 'list_agents' or 'replit_deploy' that might also involve agent management or deployment.

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 'list_agents' for viewing existing agents or 'replit_deploy' for deployment. The description implies usage for creating agents but lacks context about prerequisites, constraints, or comparison with 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 the full burden of behavioral disclosure. It mentions 'commission tracking' as a feature, but fails to explain key aspects like authentication requirements, rate limits, error handling, or what the generated link looks like. This is inadequate for a tool that likely involves user attribution and external services.

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 action and resource, 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 generating referral links with commission tracking, no annotations, and no output schema, the description is insufficient. It lacks details on return values, error cases, or behavioral traits, leaving significant gaps for the agent to infer how to use the tool effectively.

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

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 ('service' and 'user_id'). The description adds no additional meaning beyond implying these are used for referral generation and attribution, which aligns with the schema but doesn't provide extra context like format examples or constraints.

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 ('Generate') and resource ('cross-platform referral link for any SuperHero service'), specifying its purpose. However, it doesn't explicitly differentiate from sibling tools like 'shorten_url' or 'connect_service', which might have overlapping functionality, preventing a perfect score.

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

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, exclusions, or compare to sibling tools like 'shorten_url' or 'connect_service', leaving the agent with minimal context for decision-making.

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 doesn't describe how it behaves—e.g., whether it's read-only or mutative, what data sources it uses, potential rate limits, or error conditions. For a tool with zero annotation coverage, this leaves significant gaps in understanding its operational traits.

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

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

The description is a single, well-constructed sentence that efficiently conveys the core functionality without unnecessary words. It's front-loaded with the main action and includes all essential details (volume, difficulty, CPC data), making it highly concise and 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 lack of annotations and output schema, the description is insufficiently complete. It doesn't explain what the output looks like (e.g., format, structure of keyword ideas), potential limitations, or error handling. For a tool that generates data, more context is needed to guide effective use by 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?

The description mentions 'from a seed keyword,' which aligns with the single parameter 'seed_keyword' in the schema. Since schema description coverage is 100% (the parameter is fully documented in the schema), the description adds minimal value beyond what's already structured. The baseline score of 3 reflects 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: 'Generate keyword ideas with volume, difficulty, and CPC data from a seed keyword.' It specifies the action (generate), resource (keyword ideas), and key data attributes (volume, difficulty, CPC). However, it doesn't explicitly differentiate this from sibling tools like 'seo_audit' or 'optimize_content', which might also involve keyword-related 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. It doesn't mention prerequisites, ideal contexts, or exclusions, nor does it reference any sibling tools for comparison. The agent must infer usage based solely on the purpose statement.

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 mentions what information is returned (status and performance stats) but doesn't address critical behavioral aspects like whether this is a real-time view, if it requires specific permissions, how data is sorted/filtered, or potential rate limits. The description provides basic output context but lacks operational 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 clearly communicates the tool's function without unnecessary words. It's front-loaded with the core action and provides just enough detail about what information is included in the listing.

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 list tool with no annotations and no output schema, the description provides adequate basic context about what's being listed and what information is included. However, it lacks details about the return format, pagination, sorting, or data freshness that would be helpful for an agent to understand the complete behavior of this read 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?

The tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't waste space discussing parameters that don't exist, maintaining focus on the tool's purpose and output.

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 ('List') and resource ('all deployed AI agents'), specifying what information is included ('status and performance stats'). It distinguishes from some siblings like 'create_agent' or 'superhero_status', but doesn't explicitly differentiate from potential list-like siblings like 'list_integrations'.

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. While the description implies it's for viewing deployed agents, there's no mention of prerequisites, timing considerations, or comparison to other tools like 'agent_analytics' or 'superhero_status' that might overlap in functionality.

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 what the tool does but doesn't mention whether it's read-only, if it requires authentication, rate limits, pagination, or what the output format looks like. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

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

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

The description is a single, clear sentence with no wasted words. It's front-loaded with the core action and resource, making it efficient and easy to parse. Every part of the sentence contributes directly to understanding 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 simplicity (0 parameters, no output schema, no annotations), the description is adequate for a basic list operation. However, it lacks details on output format, pagination, or integration scope, which could be helpful for an agent. It meets the minimum viable standard but has clear gaps in behavioral 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?

The tool has 0 parameters, and the schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and it appropriately doesn't mention any. A baseline of 4 is applied since no parameters exist, and the description doesn't incorrectly imply any.

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 ('List') and resource ('all available third-party integrations for StartBiz websites'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'connect_service' or 'list_agents', which would require a more detailed comparison to achieve 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 like 'connect_service' or other integration-related tools. It lacks context about prerequisites, timing, or exclusions, leaving the agent with minimal usage direction beyond the basic purpose.

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

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 performs optimization with SEO improvements, implying a mutation or transformation of content, but doesn't describe what the optimization entails (e.g., structural changes, keyword density adjustments), whether it's reversible, potential side effects, or output format. For a tool that modifies content without 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: 'Optimize written content for a target keyword with SEO improvements.' It is front-loaded with the core purpose, avoids redundancy, and every word contributes meaning without waste. This exemplifies excellent 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 an SEO optimization tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns, how optimizations are applied, or any behavioral nuances. For a tool that modifies content, more context is needed to guide the agent effectively, making this description inadequate for the task's requirements.

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 both parameters ('content' and 'target_keyword'). The description adds no additional semantic information beyond what the schema provides, such as examples, constraints, or best practices. With high schema coverage, the baseline score of 3 is appropriate as the schema adequately handles 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 tool's purpose: 'Optimize written content for a target keyword with SEO improvements.' It specifies the verb ('optimize'), resource ('written content'), and goal ('SEO improvements'). However, it doesn't explicitly differentiate from sibling tools like 'claude_prompt_optimize' or 'optimize_linkedin', which appear to serve different optimization domains but share similar naming patterns.

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, context for SEO optimization, or how it differs from sibling tools like 'seo_audit' or 'claude_prompt_optimize'. The agent must infer usage based solely on the tool name and description without explicit 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 full burden for behavioral disclosure. While 'analyze and optimize' implies read and write operations, it doesn't specify what exactly gets optimized (e.g., profile text, keywords, images), whether changes are reversible, what permissions are needed, or what the output format might be. For a mutation 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 a single, efficient sentence that communicates the core purpose without unnecessary words. It's appropriately sized for a single-parameter tool, though it could be slightly more structured by separating analysis from optimization aspects. Every word earns its place, making it reasonably concise.

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 presumably performs profile optimization (a mutation operation), the description is incomplete. With no annotations, no output schema, and minimal behavioral transparency, the agent lacks crucial context about what changes will be made, what the output looks like, and any side effects. The description should provide more operational details given the tool's complexity.

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 'linkedin_url' clearly documented as 'URL of the LinkedIn profile.' The description doesn't add any parameter semantics beyond what the schema provides, such as URL format requirements or profile accessibility constraints. With complete 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 clearly states the tool's purpose: 'Analyze and optimize a LinkedIn profile for better visibility and recruiter reach.' It specifies the verb ('analyze and optimize'), resource ('LinkedIn profile'), and intended outcome. However, it doesn't explicitly distinguish this from sibling tools like 'optimize_content' or 'optimize_product_listing' which might have overlapping optimization 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. It doesn't mention prerequisites, when-not-to-use scenarios, or compare it to related sibling tools like 'build_resume', 'keyword_research', or 'optimize_content' that might serve similar career-related purposes. 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?

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions optimization for SEO/conversions but doesn't disclose whether this is a read-only analysis or a write operation, what permissions are needed, how changes are applied, or any rate limits/constraints.

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. It avoids redundancy and wastes no words, though it could be slightly more structured with bullet points for the dual goals (SEO/conversions).

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, no output schema, and parameters related to product optimization, the description is insufficient. It doesn't explain what 'optimize' entails operationally, what the output looks like, or how it interacts with Shopify's systems, leaving significant gaps for agent understanding.

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 adds no additional meaning about parameters beyond implying they relate to product listing optimization. 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 action ('optimize') and target resource ('Shopify product listing'), with the specific goals of improving SEO and conversions. It distinguishes from siblings like 'optimize_content' or 'optimize_linkedin' by specifying the Shopify context, though it doesn't explicitly contrast with them.

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

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 'optimize_content' or 'shopify_store_audit'. The description implies usage for Shopify products needing SEO/conversion improvements, but lacks explicit when/when-not instructions or prerequisite 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions deployment with automatic features but does not cover critical aspects like required permissions, whether deployment is reversible, rate limits, error handling, or what happens post-deployment. This is a significant gap for a deployment 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 front-loads the core action ('Deploy a project to Replit') and lists key features without unnecessary details. 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 the complexity of a deployment tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits, error scenarios, output format, and integration with sibling tools, making it inadequate for safe and effective use by 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?

The schema description coverage is 100%, so the schema already documents both parameters ('project_name' and 'framework'). The description does not add any additional meaning or context beyond what the schema provides, such as examples or constraints, 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 clearly states the action ('Deploy a project to Replit') and specifies key features ('automatic build, CDN, and health checks'), which provides a specific verb+resource. However, it does not explicitly differentiate from sibling tools like 'replit_optimize', which might handle optimization rather than deployment, leaving some ambiguity.

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 lacks explicit guidance on when to use this tool versus alternatives. It mentions deployment features but does not specify prerequisites, exclusions, or compare to other tools like 'build_website' or 'replit_optimize', leaving usage context implied rather than clearly defined.

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 analysis and suggestions, implying a read-only operation, but doesn't clarify if it requires specific permissions, has rate limits, or what the output format looks like. For a 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 ('analyze') and purpose ('suggest improvements'). There is no wasted language, and it directly communicates 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 analyzing a deployed project for multiple improvement types, the description is incomplete. With no annotations and no output schema, it lacks details on behavioral traits (e.g., whether it's safe, requires auth) and what the suggestions will include. For a tool with such responsibilities, 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 input schema has 100% description coverage, with the single parameter 'project_id' documented as 'Replit project ID'. The description doesn't add any semantic details beyond this, such as where to find the ID or format requirements. Since schema coverage is high, the 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 tool's purpose with specific verbs ('analyze', 'suggest') and resources ('deployed Replit project'), and it specifies the types of improvements (performance, cost, security). However, it doesn't explicitly distinguish this tool from sibling tools like 'optimize_content' or 'seo_audit', which also involve optimization but for different 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. It doesn't mention prerequisites (e.g., needing a deployed project), exclusions, or compare it to sibling tools like 'replit_deploy' or other optimization tools. Usage is implied 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool performs research, implying a read-only operation, but does not specify data sources, accuracy, rate limits, authentication needs, or output format. For a tool with no annotations, this leaves significant gaps in understanding its behavior and constraints.

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

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 front-loaded with the core purpose, making it easy to parse and understand quickly, with no wasted 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 lack of annotations and output schema, the description is incomplete. It does not explain what the research entails, the format or scope of returned data, or any behavioral traits like data freshness or limitations. For a tool with no structured support, the description should provide more context to aid the agent in 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 description mentions 'role and location,' aligning with the two parameters in the input schema. Since schema description coverage is 100%, the schema already documents both parameters adequately. The description adds minimal semantic context beyond the schema, such as implying these are for salary research, but does not provide additional details like format examples or constraints.

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: 'Research salary data for a specific role and location.' It specifies the verb ('research'), resource ('salary data'), and scope ('role and location'), making the function unambiguous. However, it does not differentiate from siblings like 'search_jobs' or 'keyword_research', which might involve similar data but different focuses.

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 lacks explicit instructions on when or when not to use it, prerequisites, or comparisons to sibling tools such as 'search_jobs' or 'keyword_research', leaving the agent to infer usage context 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('search for job listings') but doesn't describe what the search returns (e.g., list of jobs, pagination, error handling), whether it requires authentication, rate limits, or any side effects. For a search 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 a single, efficient sentence that front-loads the core purpose ('search for job listings') and includes key details ('matching keywords and optional location filters') without unnecessary words. Every part of the sentence contributes directly to understanding 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 tool's moderate complexity (search with two parameters), no annotations, and no output schema, the description is minimally adequate. It covers the basic purpose and parameters but lacks details on return values, error conditions, or integration context. It meets the bare minimum for a search tool but doesn't fully compensate for the missing structured data.

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 descriptions for both parameters ('keywords' and 'location') in the input schema. The description adds minimal value beyond the schema, mentioning 'keywords and optional location filters' but not providing additional context like format examples or usage tips. 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 ('search for') and resource ('job listings'), making the purpose immediately understandable. It specifies the matching criteria ('keywords and optional location filters'), which helps distinguish it from other job-related tools. However, it doesn't explicitly differentiate from potential siblings like 'salary_research' or 'keyword_research' that might overlap in job search 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 implies usage context through 'matching keywords and optional location filters,' suggesting this tool is for finding job postings based on search criteria. However, it provides no explicit guidance on when to use this versus alternatives like 'salary_research' or 'keyword_research,' nor does it mention prerequisites or exclusions. The guidance is functional but lacks comparative 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?

With no annotations provided, the description carries full burden for behavioral disclosure. While 'Run a comprehensive SEO audit' implies analysis rather than modification, it doesn't specify whether this is a read-only operation, what permissions might be required, whether it makes external requests, or what the scope of 'comprehensive' entails. The description provides minimal behavioral context beyond the basic action.

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

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

The description is perfectly concise - a single sentence that communicates the core functionality without any wasted words. It's front-loaded with the essential information and earns its place efficiently.

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 performs a 'comprehensive SEO audit' with no annotations and no output schema, the description is insufficient. It doesn't indicate what aspects of SEO are audited, what format the results take, whether there are limitations (e.g., site size, authentication requirements), or what 'comprehensive' actually means in practice.

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 has 100% description coverage, with the single parameter 'url' clearly documented as 'Website URL to audit'. The description adds no additional parameter semantics beyond what's already in the schema, so the baseline score of 3 is appropriate given 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 ('Run a comprehensive SEO audit') and target resource ('on a website URL'), making the tool's purpose immediately understandable. However, it doesn't differentiate this tool from potential siblings like 'shopify_store_audit' or 'url_analytics' that 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 about when to use this tool versus alternatives. With siblings like 'shopify_store_audit' and 'url_analytics' available, there's no indication of when this comprehensive SEO audit is preferable to more specialized audits or general URL 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. It states the tool performs an 'audit' but doesn't clarify what that entails—e.g., whether it's a read-only analysis, requires permissions, has rate limits, or returns specific outputs. 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 front-loads the core purpose without unnecessary words. It directly communicates the tool's function and scope, 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 lack of annotations and output schema, the description is incomplete for an audit tool. It doesn't explain what the audit returns, how results are structured, or any behavioral traits like execution time or error handling. 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 'store_url' documented as 'Shopify store URL'. The description doesn't add any parameter-specific details beyond what the schema provides, such as URL format examples or validation rules. 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 clearly states the tool's purpose: 'Audit a Shopify store for SEO, conversion, performance, and trust issues.' It specifies the verb ('audit'), resource ('Shopify store'), and scope (four audit categories). However, it doesn't explicitly differentiate from sibling tools like 'seo_audit' or 'url_analytics', 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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or exclusions, nor does it reference sibling tools like 'seo_audit' that might be relevant for similar tasks. Usage is implied but not explicitly defined.

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 'click tracking and QR code' as features, which adds some context beyond basic creation, but fails to address critical aspects like whether this is a read-only or mutation operation, authentication needs, rate limits, or what the output looks like. For a tool with no annotation coverage, this is insufficient.

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

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

The description is a single, efficient sentence that front-loads the core action ('Create a shortened URL') and adds key features without any wasted words. 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., the shortened URL format, tracking data, or QR code details), nor does it cover behavioral traits like error handling or side effects. For a tool with no structured data support, this leaves significant 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 single parameter 'long_url' documented as 'The URL to shorten'. The description doesn't add any additional meaning or context about this parameter beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage.

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

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 ('Create') and resource ('shortened URL'), and mentions additional features ('click tracking and QR code'). However, it doesn't differentiate from sibling tools like 'get_referral_link' or 'url_analytics' that might also involve URL manipulation, 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 comparisons to sibling tools such as 'get_referral_link' or 'url_analytics', leaving the agent with no context for 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 tool retrieves status and health information, implying a read-only operation, but does not disclose details like response format, error handling, rate limits, or authentication requirements. This leaves significant gaps in behavioral understanding.

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

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 redundant or unnecessary information. It is front-loaded and wastes no words, 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 simplicity (0 parameters, no output schema, no annotations), the description is adequate but not fully complete. It explains what the tool does but lacks details on behavioral aspects like response format or error conditions, which are important for a status-checking tool even without complex inputs.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on the tool's purpose. This aligns with the baseline expectation for zero-parameter tools.

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 'Get' and the resource 'current status and health of all SuperHero API services,' making the purpose specific and unambiguous. It distinguishes itself from sibling tools by focusing on API service monitoring rather than content optimization, analytics, or deployment tasks.

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 implies usage when checking API service status, but does not explicitly state when to use this tool versus alternatives or provide any exclusions. No guidance is given on prerequisites or contextual triggers, leaving usage inferred rather than clearly defined.

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 this is a read operation ('Get'), but doesn't cover critical aspects like authentication requirements, rate limits, error conditions, or what format the analytics data returns (e.g., JSON structure, time ranges). For a tool with no annotation coverage, this leaves significant gaps in understanding how it 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 front-loads the core purpose ('Get click analytics for a shortened URL') and adds specific details ('including referrers, geo, and device data') without unnecessary words. Every part of the sentence contributes directly to understanding the tool's function, 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 moderate complexity (analytics retrieval with one parameter), no annotations, and no output schema, the description is minimally adequate. It covers the basic purpose and data types but lacks details on behavior, output format, and usage context. This leaves the agent with enough to invoke the tool but insufficient for robust integration without trial or external knowledge.

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 'short_code' documented in the schema as 'The short code to look up'. The description adds no additional parameter semantics beyond implying the short code relates to a 'shortened URL'. Since the schema already fully describes the parameter, the baseline score of 3 is appropriate—the description doesn't compensate but doesn't need to 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 ('Get') and resource ('click analytics for a shortened URL'), including what data is retrieved ('referrers, geo, and device data'). It distinguishes itself from sibling tools like 'shorten_url' by focusing on analytics rather than URL creation. However, it doesn't explicitly differentiate from potential analytics-related siblings like 'agent_analytics', which slightly limits 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 doesn't mention prerequisites (e.g., needing a shortened URL from 'shorten_url'), exclusions, or comparisons to other analytics tools like 'agent_analytics'. Usage is implied only by the tool's name and purpose, leaving the agent to infer context without explicit direction.

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