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

Despite no annotations, the description provides a comprehensive 'Behavioral Transparency' section covering side effects (read-only, stateless), authentication, rate limits, error handling, idempotency, and data privacy. This fully compensates for missing annotations.

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

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

The description is well-structured with clear sections but contains redundancy (e.g., 'read-only and stateless' repeated) and a vague 'When to use' that could be more concise. Some sentences could be trimmed without losing meaning.

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 6 parameters, no output schema, and no annotations, the description covers all essential aspects: input explanation, return values (winner, z-score, etc.), side effects, rate limits, authentication, error handling, and data privacy. It is fully adequate for an AI agent to use correctly.

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

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

The schema has 0% coverage, but the description includes an 'Args:' block that explains each parameter except 'api_key', which is covered in the behavior section. This adds significant meaning beyond the raw schema, though the api_key parameter could be more explicitly linked.

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 it analyzes A/B test results with statistical significance testing and lists specific outputs (winner, z-score, p-value, etc.). This is a specific verb+resource and distinguishes itself from sibling tools focused on other marketing 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 includes explicit 'When to use' and 'When NOT to use' sections, providing context for appropriate usage. However, the 'When to use' statement is too generic and does not directly compare to sibling tools, though the siblings are clearly different in function.

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 provided, so the description fully covers behavioral traits. It details side effects (read-only, no modifications), authentication (no auth for basic, API key for pro), rate limits (10/day free, unlimited pro), error handling (structured errors), idempotency, and data privacy. This exceeds expectations.

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 well-structured with sections for args, behavior, when to use, and behavioral transparency. However, it is somewhat verbose with redundancy (e.g., 'Behavior' section then repeated in 'Behavioral Transparency'). Could be condensed without losing value.

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 6 parameters and no output schema, the description covers behavioral context well but lacks details on the output format or structure. The 'structured output' is not defined, leaving the agent uncertain about return types. Generic 'when to use' section also misses tool-specific 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 0%, so the description must compensate. It lists parameters but adds only minimal detail (e.g., defaults for tone, platform, cta). It does not explain allowed values for 'platform' or 'tone' beyond examples, nor does it describe the 'api_key' parameter. Names are self-explanatory, but more specifics would improve 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 'Generate ad copy variants tailored to a specific platform with proper character limits and best practices.' It specifies the action (generate), the output (ad copy variants), and constraints (platform-specific). The tool is distinct from siblings like ab_test_analyze or attribution_model.

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 'When to use' section is generic and not specific to ad copy generation (e.g., 'structured analysis or classification'). It lacks explicit guidance on when to choose this tool over alternatives or what prerequisites are needed (e.g., target platform knowledge). The 'When NOT to use' is helpful but brief.

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?

Despite no annotations, the description provides comprehensive behavioral details: read-only, stateless, idempotent, rate limits (10/day free, unlimited pro), and no auth required. This fully informs the agent about side effects 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 well-structured with clear sections (Args, Behavior, When to use/not use) and is concise without extraneous text. Every sentence adds value.

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 has 3 parameters, no output schema, and no annotations, the description covers key aspects: purpose, parameters, behavior, usage boundaries. It lacks explicit mention of output format but the behavior section implies analysis output. Overall, it is nearly 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?

With 0% schema description coverage, the description compensates well for two of three parameters: touchpoints (example format) and model (enum list). However, the api_key parameter is not mentioned at all, missing an opportunity to clarify its optional nature.

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 function: "Apply an attribution model to marketing touchpoints" and "Distributes conversion credit across channels based on the chosen model." It specifies verb (apply), resource (attribution model to touchpoints), and outcome. Siblings like 'ab_test_analyze' and 'campaign_roi' are distinct, so no confusion.

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 explicit 'When to use' and 'When NOT to use' sections, advising against real-time production use without review. However, the 'When to use' is generic ('structured analysis or classification') and does not contrast directly with sibling tools, which would improve guidance.

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 thoroughly discloses all behavioral traits: read-only and stateless, no side effects, idempotent, authentication requirements, rate limits, error handling, and data privacy. This fully compensates for the missing annotations.

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

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

The description is well-structured with clear sections (Args, Behavior, When to use/not use, Behavioral Transparency). It is front-loaded with purpose and parameters. Some redundancy exists between the 'Behavior' and 'Behavioral Transparency' sections, but overall it is efficient and organized.

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 7 parameters and no output schema, the description covers purpose, parameters, behavior, side effects, and error handling. It lists the computed metrics but does not detail the output format or calculation assumptions. Still, it provides sufficient context for an 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 schema has no property descriptions (0% coverage), but the description's 'Args' section provides meaningful explanations for each parameter (e.g., 'Total campaign spend in dollars', 'Total revenue attributed to campaign'). This adds significant value beyond the schema, though units and constraints could be more explicit.

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 calculates comprehensive campaign ROI with specific metrics (ROAS, CPA, CPC, CPM, CTR, conversion rate) and performance assessment. It distinguishes itself from siblings like ab_test_analyze and ad_copy_generator by focusing on ROI calculation.

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 explicit 'When to use' and 'When NOT to use' sections. While the when-to-use is generic, it provides context for ROI analysis. It lacks explicit alternatives or prerequisites but still offers clear guidance.

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

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

With no annotations, the description fully bears the burden of behavioral disclosure. It explicitly states the tool is read-only, stateless, idempotent, and safe to call repeatedly. It also mentions the free tier rate limit (10/day) and pro tier unlimited access, plus that no authentication is required for basic usage. This is comprehensive.

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 well-structured with clear sections: main purpose, Args, Behavior, When to use, When NOT to use. Every sentence adds value, and the text is concise without extra fluff.

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 (2 parameters, no output schema), the description covers behavior, parameters, and usage well. However, it does not specify the exact output structure (e.g., JSON format), only mentioning the output conceptually ('identify the biggest leak point with optimization recommendations'). This is a minor gap.

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 coverage is 0%, so the description must compensate. The 'stages' parameter is explained with a clear example: 'List of funnel stages as [{"name": "Awareness", "count": 10000}, ...]'. The 'api_key' parameter is implicitly addressed via the note on authentication, but its exact role (e.g., for pro tier) is ambiguous. Overall, the description adds significant meaning beyond the bare schema.

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

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

The description clearly states the tool's purpose: 'Analyze a conversion funnel and identify the biggest leak point with optimization recommendations.' It specifies the verb (analyze, identify) and resource (conversion funnel), and the output is well-defined. The purpose distinguishes it from sibling tools like ab_test_analyze or attribution_model, which focus on different aspects.

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

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

The description includes 'When to use' and 'When NOT to use' sections, providing context on appropriate scenarios. It advises against real-time production use without human review. However, it does not explicitly compare this tool to siblings, leaving room for ambiguity in tool selection.

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