ai-compliance-monitor

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 adequately describes what the tool returns (deadlines with enforcement dates, penalty info, obligations) but doesn't mention important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or how results are structured/paginated. The description adds value but leaves gaps in behavioral context.

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

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

The description is perfectly concise with two sentences that each earn their place. The first sentence clearly states what the tool does, and the second sentence provides usage guidance. There's zero waste or redundancy, and the information is front-loaded effectively.

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 3 parameters, no annotations, and no output schema, the description provides adequate but incomplete context. It clearly explains the tool's purpose and usage but doesn't address behavioral aspects like safety profile, authentication needs, or result format. Given the complexity and lack of structured data, the description should do more to compensate for these gaps.

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

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

The description doesn't mention any parameters, but the input schema has 100% description coverage with clear documentation of all 3 parameters. According to scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no parameter information in the description. The description doesn't add semantic value beyond what the schema already provides.

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 ('Returns') and resource ('upcoming and recent compliance deadlines') with detailed scope ('enforcement dates, penalty information, and related obligations'). It distinguishes from sibling tools like 'check_obligations' by focusing specifically on time-sensitive deadlines rather than general obligations.

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 clear context for when to use this tool ('to understand time-sensitive compliance requirements'), which helps differentiate it from siblings like 'compare_jurisdictions' or 'get_regulation_articles'. However, it doesn't explicitly state when NOT to use it or name specific alternatives, keeping it at a 4 rather than a 5.

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 adequately describes the core behavior (returns obligations with specific details) and scope (AI agent use cases, optional jurisdiction filtering), but doesn't address important behavioral aspects like rate limits, authentication requirements, error conditions, or response format details that would be valuable for an agent.

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

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

The description is perfectly concise with two well-structured sentences: the first states what the tool does with key output details, and the second provides clear usage guidance. Every word earns its place with no redundancy or unnecessary elaboration.

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

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

Given the tool's moderate complexity (2 parameters, regulatory domain), no annotations, and no output schema, the description provides adequate basic information but lacks completeness. It doesn't describe the return format, potential limitations, error handling, or how results are structured, which would be important for an agent to effectively use this compliance tool.

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 thoroughly with examples. The description mentions the parameters ('AI agent use case and optional jurisdiction') but doesn't add meaningful semantic context beyond what the schema provides, such as explaining why these particular parameters matter or how they 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 verb ('returns') and resource ('all applicable regulatory obligations'), including detailed output components (evidence requirements, enforcement dates, penalty information). It distinguishes from siblings by focusing on obligations for specific AI use cases rather than deadlines, comparisons, or articles.

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 clear context for when to use this tool ('to understand what compliance requirements apply to a specific type of AI agent'), but doesn't explicitly mention when not to use it or name specific alternatives among the sibling tools. The guidance is helpful but lacks explicit exclusions or comparisons.

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 describes what the tool does (comparative mapping) and the output format (showing equivalent, overlapping, stricter, weaker mappings), which is helpful. However, it doesn't mention important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or what happens when jurisdictions aren't comparable. The description adds value but doesn't fully compensate for the lack of 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 perfectly concise with two well-structured sentences. The first sentence clearly states the tool's function, and the second sentence provides the usage context. Every word earns its place with zero redundancy or wasted text.

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

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

For a tool with 2 parameters, no annotations, and no output schema, the description provides adequate but not complete context. It explains the comparative nature of the tool and its purpose, but doesn't describe the output format in detail (beyond mentioning the types of mappings) or address potential edge cases. Given the complexity of regulatory comparison and the lack of output schema, a more complete description would be beneficial.

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

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

The input schema has 100% description coverage, so the schema already documents both parameters thoroughly. The description doesn't add any additional parameter semantics beyond what's in the schema - it doesn't explain the format of jurisdiction codes beyond the examples in the schema, nor does it elaborate on the category options. 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 with specific verbs ('Side-by-side comparison', 'Shows equivalent, overlapping, stricter, and weaker mappings') and resources ('regulatory obligations across jurisdictions for a given compliance category'). It distinguishes itself from sibling tools like 'check_obligations' or 'get_regulation_articles' by focusing on cross-jurisdictional comparison rather than single-jurisdiction analysis or article retrieval.

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 clear context for when to use this tool ('to understand how requirements differ across borders'), which implicitly suggests it's for comparative analysis rather than checking specific obligations or deadlines. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools, which would be needed for a perfect score.

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 returns structured details but doesn't cover critical aspects like whether it's a read-only operation, potential rate limits, authentication requirements, error handling, or response 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 concise and front-loaded, with two sentences that directly address purpose and usage. Every sentence earns its place, avoiding redundancy. However, it could be slightly more structured by separating purpose from guidelines more clearly, but it's efficient overall.

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 regulatory details and the absence of both annotations and an output schema, the description is incomplete. It doesn't explain what the return values look like (e.g., structure of articles, risk levels), potential limitations, or how to interpret the results. For a tool with no output schema and no annotations, more context is needed to fully understand its operation.

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

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

Schema description coverage is 100%, so the schema already documents both parameters ('regulation_id' and 'category') with descriptions and options. The description adds no additional parameter semantics beyond what's in the schema, such as explaining how 'category' filtering works or providing examples. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose: 'Returns structured, machine-readable regulation details including specific articles, obligation classifications, risk levels, and evidence requirements.' It specifies the verb ('Returns') and resource ('regulation details'), and distinguishes it from siblings by focusing on detailed requirements rather than deadlines, obligations, or comparisons. However, it doesn't explicitly differentiate from 'check_obligations', which might overlap in scope.

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

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

The description provides some guidance: 'Use this to understand the detailed requirements of a specific regulation.' This implies usage when detailed analysis is needed, but it doesn't specify when to use this tool versus alternatives like 'check_obligations' or 'compare_jurisdictions'. No explicit exclusions or prerequisites are mentioned, 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.