TheArtOfService Compliance Intelligence

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

With no annotations provided, the description carries full disclosure burden. It adequately describes the response structure (total controls, mapped counts, percentages) but omits behavioral traits like read-only status, execution cost, caching behavior, or permissions required for this analytical operation.

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?

Core content is front-loaded and purposeful, but includes wasteful OpenAPI-generated noise in the '### Responses' section (HTTP 200 boilerplate) that adds no value for tool selection. Every sentence should earn its place; the Response section does not.

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?

Despite lacking an output schema, the description thoroughly documents return value structure (total controls, mapped control counts, coverage percentages per target framework), giving agents sufficient context to understand what data will be returned.

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% (parameter 'name' has no description). While the description implies this is a 'framework' name via 'for a framework', it does not explicitly document the parameter semantics, acceptable formats, or whether this is a framework ID versus display name. Minimal compensation for the schema deficiency.

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 uses specific verbs ('Get', 'Returns') and clearly identifies the resource as a 'cross-framework coverage report' showing how controls map between frameworks. It distinguishes from siblings like agent_cross_framework_map by emphasizing statistical coverage analysis ('coverage percentages', 'understand which frameworks overlap most') rather than just raw mappings.

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?

Includes explicit usage context ('Use this to understand which frameworks overlap most and plan multi-framework strategies'), providing when-to-use guidance. However, lacks explicit alternatives or exclusions—doesn't clarify when to use agent_cross_framework_map instead for detailed mappings versus this tool for high-level statistics.

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. It discloses the return structure ('complete control-to-control mapping', 'shows which source control maps to which target control(s)'), which is helpful given no output schema exists. However, it lacks critical behavioral context: it doesn't state this is read-only, doesn't describe error cases (e.g., invalid framework names), and omits any size/rate limiting information.

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 first two sentences are efficient and valuable. However, the description includes a '### Responses:' section with HTTP 200 status code documentation, which is irrelevant for MCP tool selection and wastes context window. This appears to be uncleaned OpenAPI copy-paste that should have been removed.

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 output schema and annotations, the description adequately explains the return value structure (mappings between controls). However, it is incomplete regarding error handling, pagination for large mappings, and authentication requirements. The inclusion of HTTP response codes falsely suggests completeness while adding no value for an AI agent.

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

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

Although the input schema has 100% description coverage (baseline 3), the description adds crucial semantic constraints: it specifies that parameters must be 'exact framework names as returned by agent_search_frameworks', providing validation logic not present in the schema. It also implies the directionality (source→target) which is reinforced by the schema.

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

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

The description clearly states the tool 'Map controls between two compliance frameworks' with a specific verb and resource. It explains the value proposition (multi-framework compliance) and implicitly distinguishes from siblings by noting it requires exact framework names from agent_search_frameworks. However, it does not clarify how this differs from agent_get_control_cross_references.

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 a clear prerequisite ('Use exact framework names as returned by agent_search_frameworks') and explains the use case (multi-framework compliance). However, it lacks explicit guidance on when to use this versus agent_get_control_cross_references or agent_get_framework_controls, which sound similar.

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. It discloses what fields are returned (title, description, domain, framework) and the lookup mechanism, but omits error behavior (e.g., what happens if the control code is not found) and any rate limiting or authentication requirements.

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 core description is efficient and front-loaded with key information. However, it includes wasteful boilerplate at the end ('### Responses: **200**: Successful Response...') that serves an OpenAPI specification format rather than an LLM tool selection context, reducing overall conciseness.

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

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

Given the tool has only one parameter, no annotations, and no output schema, the description adequately explains the input parameter semantics and the conceptual output structure. However, it is incomplete regarding error handling scenarios (e.g., invalid codes) and doesn't mention if this is a read-only operation (though implied by 'Get').

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% description coverage for the 'code' parameter. The description compensates effectively by explaining that codes are 'framework-specific' identifiers and providing concrete format examples ('A.5.1', 'AC-1'), giving the agent clear context for what valid inputs look like despite the schema's lack of documentation.

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

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

The description clearly states the verb (Get) and resource (control/detailed information) and specifies the lookup method (by code identifier). It distinguishes from sibling list/search tools by emphasizing 'single control' and specific 'code identifier' usage, though it doesn't explicitly name alternative tools like agent_search.

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?

Provides implicit usage guidance by explaining that control codes are framework-specific and giving concrete examples ('A.5.1' for ISO 27001, 'AC-1' for NIST 800-53), which signals to use this when you have an exact code. However, it lacks explicit when-to-use/when-not-to-use distinctions versus agent_search or agent_get_control_cross_references.

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. While it specifies the relationship type (MAPS_TO) and return payload (controls in other frameworks), it lacks critical details: error handling (what happens if the code is invalid?), data cardinality (pagination for controls with many mappings), or performance characteristics. The '### Responses' section is boilerplate cruft that adds no behavioral insight.

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 first two sentences are efficient and front-loaded with value. However, the inclusion of the '### Responses' OpenAPI boilerplate at the end degrades conciseness—it consumes tokens without aiding tool selection or invocation decisions. The structure is not optimized for an LLM agent.

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 simple input schema (single string parameter) and absence of output schema, the description conveys the conceptual purpose adequately. However, it leaves significant gaps: the parameter is undocumented, the return structure is only vaguely described ('returns all controls'), and without annotations, critical operational context (auth, safety, rate limits) is missing.

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

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

The input schema has 0% description coverage on the required 'code' parameter. The description references a 'given control' but never clarifies that the 'code' parameter expects a control identifier, nor specifies its format, case sensitivity, or examples. With zero schema documentation, the description fails to compensate by explaining the single parameter's 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 it retrieves cross-framework mappings via MAPS_TO relationships and specifies the resource (controls). It effectively distinguishes the operation as finding 'equivalent controls across different compliance frameworks' with concrete examples (NIST 800-53 to ISO 27001). However, it does not explicitly differentiate from the sibling tool `agent_cross_framework_map`.

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 an implied usage pattern through the specific example of finding NIST equivalents for ISO controls and labels this as the 'core cross-framework mapping capability.' However, it lacks explicit guidance on when not to use this tool or when to prefer alternatives like `agent_cross_framework_map` or `agent_search`.

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 description carries full burden. Compensates well by enumerating specific return fields (description, jurisdiction, version, domains, cross-framework mapping statistics) despite no output schema. Does not explicitly state read-only/safe nature, though implied by 'Get'.

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?

First three sentences are information-dense and front-loaded. However, the trailing '### Responses: **200**: Successful Response...' boilerplate adds no value for tool selection and wastes tokens, indicating the description wasn't cleaned from OpenAPI generation.

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?

Adequate for a low-complexity single-parameter tool. Covers input semantics (where to get the name) and output structure (specific fields returned) despite lack of output schema. Missing only edge-case handling (e.g., 'returns 404 if framework not found').

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 has 0% description coverage (parameter 'name' has only type/title). Description adds critical semantics: 'Use the exact framework name as returned by agent_search_frameworks,' clarifying both the expected format and valid source of the value, effectively compensating for the undescribed 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?

Clear verb ('Get') and resource ('compliance framework') with specific scope ('detailed information'). Mentions returning 'description, jurisdiction, version, domains with control counts,' which hints at metadata retrieval. However, it does not explicitly distinguish from sibling 'agent_get_framework_controls' (likely returns actual controls vs. framework metadata), 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?

Provides clear prerequisite: 'Use the exact framework name as returned by agent_search_frameworks,' indicating this should be called after search and specifying valid input format. Lacks explicit 'when not to use' guidance versus alternatives like the controls-specific sibling.

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 description carries full disclosure burden. Compensates partially by detailing response structure ('Each control includes: code, title, description, and domain'). However, omits safety profile (read-only vs. mutation), rate limits, or authentication requirements.

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?

Front-loaded with clear first sentence and practical filtering advice. However, includes wasteful OpenAPI generated noise ('### Responses', '200: Successful Response', Content-Type lines) that earns no value for LLM tool selection.

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?

Explains return payload structure adequately given lack of output schema. However, incomplete due to missing 'name' parameter documentation and safety behavioral disclosure expected for unannotated tools.

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 50% (domain has description, name does not). Description adds context for domain parameter ('filtered by domain', narrowing results). Fails to clarify 'name' parameter semantics (framework identifier? code?), leaving a critical parameter undocumented.

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?

States specific action 'Get all controls' with clear resource 'compliance framework'. Plural 'controls' distinguishes from singular sibling agent_get_control, though explicit contrast would strengthen this further.

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?

Provides valuable heuristic 'For large frameworks... use the domain filter to narrow results' indicating when/how to use filtering. However, lacks explicit guidance on when to use this bulk tool vs. single-control retrieval or search alternatives.

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 disclosure burden. It adds valuable context about authentication requirements and enumerates the return values. However, it omits rate limiting information, caching behavior, and error conditions that would help an agent handle responses robustly.

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 first two sentences are efficient and front-loaded with key information. However, the '### Responses:' section containing HTTP 200 status documentation is boilerplate cruft that does not help an AI agent select or invoke the tool, reducing the signal-to-noise ratio.

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 read operation without output schema documentation, the description adequately compensates by explicitly listing the four statistical fields returned in the response. This provides sufficient context for an agent to determine if this tool meets its data needs.

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 contains zero parameters with 100% coverage. Per evaluation guidelines, zero-parameter tools receive a baseline score of 4, as there are no parameter semantics to clarify beyond the schema itself.

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 retrieves 'platform statistics' and specifically enumerates the four metrics returned (framework count, control count, cross-framework mapping count, domain count). However, it lacks explicit differentiation from sibling tools like 'agent_coverage_report' that might sound statistically similar.

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 notes 'No authentication required,' which provides useful deployment context. However, it lacks guidance on when to use this versus alternatives (e.g., when to use agent_coverage_report instead) or any prerequisites/limitations.

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?

Without annotations, the description carries the full disclosure burden. It successfully notes the conditional behavior ('if authenticated') for usage data. However, it omits whether the endpoint itself is rate-limited, caching behavior, or what authentication mechanism is expected. Adequate but not rich 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 core description is concise, but includes OpenAPI-spec noise ('### Responses:', '**200**: Successful Response') that adds no value in an MCP context where output schemas are separate. The front-loaded purpose is good, but the markdown bloat reduces clarity.

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

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

With no output schema provided, the description must compensate by describing the return structure. It lists conceptual fields ('pricing tiers', 'monthly call limits') but provides no JSON structure, types, or examples. The 200 response snippet is unhelpful without schema details.

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

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

The tool has zero parameters, which establishes a baseline of 4 per the rubric. No parameter semantics are needed or provided.

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 uses a clear verb ('Get') and specific resource ('API pricing and rate limit information'). While it doesn't explicitly contrast with siblings (which focus on compliance frameworks/controls), the domain is sufficiently distinct that the purpose is unambiguous.

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

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

Provides explicit guidance on when to use: 'Use this to understand costs before making API calls.' This establishes the prerequisite checking pattern. Lacks explicit 'when not to use' or alternatives (e.g., vs. agent_platform_stats), but the single use-case is 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 states what fields are searched but fails to disclose idempotency (though implied by 'search'), result ranking behavior, pagination beyond the limit parameter, or the structure/format of returned results. It merely notes 'Successful Response' without describing what data is returned.

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 reasonably front-loaded with the core function explained first. However, it includes irrelevant HTTP boilerplate ('### Responses: **200**: Successful Response...') that wastes tokens without aiding agent comprehension. The compliance terms examples are useful but could be integrated more 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?

Given the absence of an output schema, the description should compensate by describing the return structure (e.g., list of controls/frameworks with relevance scores). It fails to do so. Additionally, it does not resolve the apparent overlap with agent_search_frameworks, leaving ambiguity about which search tool to use for framework-specific queries.

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 100%, with all three parameters (query, type, limit) fully documented in the schema. The description reinforces the schema by providing the same examples ('access control', 'encryption at rest') found in the parameter descriptions, but adds no additional semantic context about parameter interactions or advanced query syntax 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 identifies this as a full-text search tool across controls and frameworks, specifying it searches titles and descriptions. It implicitly distinguishes from sibling 'get' tools (agent_get_control, agent_get_framework) by emphasizing 'full-text search' versus ID-based retrieval. However, it fails to clarify the relationship to agent_search_frameworks, which appears to be a overlapping sibling.

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?

While the description provides helpful query construction tips (e.g., 'access control', 'encryption at rest') and explains the 'type' parameter options, it offers no guidance on when to use this tool versus agent_search_frameworks or the agent_get_* retrieval tools. The agent must infer usage from parameter names 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. It discloses return values (metadata fields) and empty-query behavior ('Without a query, returns all frameworks'), but omits details on authentication requirements, rate limiting, sorting behavior, or pagination beyond the limit parameter.

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 contains wasteful HTTP response documentation ('### Responses', '**200**: Successful Response', 'Content-Type: application/json') that consumes tokens without aiding tool selection. The opening 'Search and list compliance frameworks' is redundant with the first sentence of the actual description.

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

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

Despite lacking an output schema, the description adequately compensates by enumerating the specific metadata fields returned (name, description, jurisdiction counts, etc.). For a discovery-oriented tool with three optional parameters, the description provides sufficient context to understand the discovery workflow.

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?

While the schema has 100% description coverage (baseline 3), the description adds valuable context by mapping parameters to concepts ('compliance knowledge graph') and explaining the nullable query behavior ('Without a query, returns all frameworks'), which helps the agent understand the empty-state 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 searches and lists compliance frameworks with specific search dimensions (name, keyword, jurisdiction). It distinguishes itself from siblings like 'agent_get_framework' by positioning itself as the 'starting point to discover available frameworks,' though it could more explicitly contrast with the generic 'agent_search' sibling.

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?

Provides implied usage guidance by stating it should be used as the 'starting point to discover available frameworks.' However, it lacks explicit exclusions (when NOT to use) or named alternatives for specific use cases (e.g., when to prefer 'agent_get_framework' over this).

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