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

Annotations confirm readOnlyHint=true. The description adds valuable context about what specific information is returned (freshness, provenance) and the verification use case, going beyond the binary safety flags. Does not contradict 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?

Two sentences with zero waste. First sentence front-loads the core deliverables (metadata, statistics, freshness, provenance). Second sentence provides the usage context. Every word 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 simple zero-parameter metadata tool with safety annotations, the description is complete. It compensates for the missing output schema by enumerating the return categories (metadata, statistics, freshness, provenance), providing sufficient information for an agent to decide when to invoke it.

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?

Zero parameters present in the schema. Per the baseline rules for tools with no parameters, this scores 4. The description appropriately makes no parameter claims since none exist.

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 explicitly identifies the resource (server metadata, dataset statistics, freshness, provenance) and uses specific verbs. It clearly distinguishes this from siblings like get_article or search_regulations, which handle legal content rather than meta-information about the dataset itself.

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 when-to-use guidance ('Call this to verify data coverage, currency, and content basis before relying on results'), establishing it as a preliminary validation step. Lacks explicit 'when-not-to-use' or named alternatives, though the sibling list makes its unique meta-purpose obvious.

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?

Annotations declare readOnlyHint=true. Description adds valuable behavioral context about 'optimal response length' controlling output size. However, with no output schema provided, the description fails to disclose what the tool returns (e.g., list of regulations, applicability scores, compliance 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?

Two sentences with zero waste. First sentence establishes core purpose; second sentence adds the distinctive feature (tiered detail levels). Front-loaded with the most critical 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?

With 100% schema coverage and clear annotations, the description adequately covers inputs. However, given the absence of an output schema and the complexity of EU regulatory applicability, the description should specify the return format (e.g., structured list of regulations with applicability reasoning). Sibling context is also absent.

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%, establishing a baseline of 3. Description adds conceptual grouping ('sector and characteristics') and explains the purpose of detail_level ('optimal response length'), but doesn't clarify relationships between parameters (e.g., that subsector depends on sector) or provide format examples beyond schema enums.

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 'Determine' with specific resource 'EU regulations' and scope 'based on sector and characteristics'. Distinguishes from siblings like get_article or list_regulations by focusing on applicability determination rather than retrieval. However, lacks explicit contrast with search_regulations or list_regulations which also deal with regulation discovery.

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?

Implies usage through explanation of 'tiered detail levels' (suggesting when to use summary vs full), but lacks explicit guidance on when to choose this over siblings like search_regulations or list_regulations. No mention of prerequisites or required 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?

Adds valuable behavioral details beyond annotations: discloses the search mechanism ('full-text search with relevance ranking') and return format ('text snippets showing how they address the topic'). Consistent with readOnlyHint=true. Does not contradict 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?

Three tightly constructed sentences, each earning its place: (1) core purpose, (2) return value description, (3) search methodology. Front-loaded with the primary action and appropriately scoped 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 simple 2-parameter schema and presence of safety annotations, the description adequately explains the return values (matching articles with snippets) despite no output schema. Could mention pagination or result limits, but sufficiently complete for agent selection and 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 coverage is 100% with clear descriptions and examples for both parameters (topic and regulations). The description references these ('specific topic', 'multiple regulations') but does not add syntax or format details beyond what the schema already provides. Baseline 3 is appropriate given comprehensive schema 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?

Description uses specific verbs ('Search and compare') and clearly identifies the resource ('articles across multiple regulations'). It distinguishes from siblings like get_article (single retrieval) and diff_article (specific article comparison) by emphasizing 'across multiple regulations' and 'specific topic'.

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 context about the specific use case (comparing requirements across regulations on a topic), which implicitly guides selection over single-regulation or single-article tools. Lacks explicit 'when not to use' or named sibling alternatives, but the functional scope is distinct enough to guide 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?

Annotations confirm read-only/non-destructive status. The description adds valuable behavioral context not in structured fields: the output format ('unified diff and AI-generated change summary') and licensing requirements ('requires Ansvar Intelligence Portal'). No contradictions with 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?

Two sentences with zero waste. The first sentence front-loads the core purpose and output format. The second sentence provides critical licensing context. Every word 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?

Despite lacking an output schema, the description adequately describes the return value (unified diff + AI summary). Combined with complete input schema coverage, safety annotations, and the premium feature warning, the description provides sufficient context for a read-only diff 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?

With 100% schema description coverage, the baseline score is 3. The description implicitly reinforces the date parameters ('between two dates') and the article targeting ('specific article'), but does not add syntax details, examples, or semantics 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 uses specific verbs ('Show what changed') and clearly identifies the resource (a specific article between two dates). It distinguishes from siblings like get_article (current state) and get_recent_changes (broad recent changes) by specifying the diff functionality and output format ('unified diff and AI-generated change summary').

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 important prerequisite ('Premium feature — requires Ansvar Intelligence Portal'), but lacks explicit guidance on when to use this versus similar siblings like get_article_history or compare_requirements. Usage is implied by the date-range diff functionality, but no alternatives are named.

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?

While annotations declare the operation read-only/safe, the description adds critical behavioral context: variable token costs (500-70,000), automatic truncation thresholds (50,000 characters), and notification behavior when truncation occurs—essential for agent resource management.

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?

Three sentences total, each earning its place: purpose declaration, cost/limit warning, and workflow prerequisite. No redundancy or filler; information density is high with critical constraints front-loaded.

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 retrieving large legal texts, the description adequately covers cost risks, size limits, and prerequisites. Minor gap: does not explicitly describe the output format/structure (e.g., markdown vs JSON), though truncation behavior implies text content.

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 100% schema description coverage, the parameters (regulation ID, article number, include_recitals) are fully documented in the schema. The description aligns with these ('specific article from a regulation') but does not add syntax details beyond the schema, warranting the baseline score.

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 opens with a specific verb ('Retrieve') and clear resource ('full text of a specific article from a regulation'), immediately distinguishing it from siblings like search_regulations or get_recital.

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?

Explicitly prescribes a workflow ('Use search_regulations first to find relevant articles'), providing clear guidance on when to use this tool versus its search counterpart, and warns about token costs that would influence invocation decisions.

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?

Annotations declare readOnlyHint=true, confirming safe read operation. Description adds crucial behavioral context not in annotations: the premium/auth requirement and specific output format (amendments with dates and change summaries). No contradictions with 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?

Two sentences with zero waste: first sentence front-loads purpose and output description, second states the critical premium constraint. Every word earns its place; no redundant repetition of annotations or schema details.

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 simple 2-parameter read tool with full schema coverage, the description adequately compensates for missing output schema by describing return contents (amendments, dates, summaries) and notes the premium restriction. Could enhance by mentioning pagination or volume limits for articles with extensive histories.

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?

Input schema has 100% description coverage (regulation and article parameters fully documented). Description implies targeting a 'specific article' but does not elaborate on parameter formats beyond the schema, which satisfies the baseline for high-coverage schemas.

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?

Description uses specific verb 'Get' with resource 'full version timeline' and clarifies scope with 'showing all amendments with dates and change summaries.' Distinguishes from sibling get_article (current state) by emphasizing historical timeline and from diff_article by offering complete chronology rather than comparison.

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 prerequisite context with 'Premium feature — requires Ansvar Intelligence Portal,' indicating auth/licensing constraints. However, lacks explicit guidance on when to choose this over get_article (current text) or get_recent_changes (cross-regulation updates), requiring the agent to infer from 'full version timeline.'

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?

The annotations already establish readOnlyHint=true, so the description is not burdened with declaring safety properties. It adds value by specifying the data source ('definitions article'), which helps the agent understand the scope. It does not address edge cases (e.g., term not found) or pagination behavior 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 consists of two efficient sentences with zero waste. The first front-loads the core purpose; the second provides essential provenance context. No sentences could be removed without information loss.

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 read-only lookup tool with 100% schema coverage and clear annotations, the description is adequate. It specifies the domain (EU regulations) and source (definitions articles). Lacking an output schema, it could have described the return format (e.g., 'returns definition text'), but the tool's purpose is sufficiently clear for 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?

With 100% schema description coverage, the schema adequately documents all three parameters including examples ('personal data', 'incident'). The description does not add parameter-specific semantics (e.g., regex patterns, case sensitivity for 'term'), so it appropriately rests at the baseline for high-coverage schemas.

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 ('Look up') and resource ('official definitions of terms from EU regulations'). It implicitly distinguishes from sibling tools like get_article by specifying it retrieves definitions from 'definitions articles,' though it doesn't explicitly name alternative 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 second sentence ('Terms are defined in each regulation's definitions article') provides implicit context about when to use this tool—specifically for terms that appear in definitions sections. However, it lacks explicit guidance on when NOT to use it (e.g., for full article text) or how it differs from search_regulations.

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?

Annotations confirm read-only/destructive status, while the description adds valuable behavioral context about the return data domain—specifically mentioning 'retention periods and maturity levels' and the types of artifacts (documents, logs, test results). This helps the agent understand the semantic content of results without contradicting the safety 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?

Two well-structured sentences with zero waste. Front-loaded with the core action ('Get compliance evidence...'), followed by specific details about scope ('retention periods and maturity levels'). Every clause adds distinct 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?

For a query tool with 100% schema coverage and no output schema, the description adequately compensates by detailing what information is returned (evidence types, retention periods, maturity levels). It provides sufficient context for an agent to understand the tool's utility in a compliance 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?

With 100% schema description coverage, the parameters are fully self-documenting. The description mentions 'specific regulation requirements' which loosely maps to the regulation/article parameters, but does not add syntax guidance or usage notes beyond the schema. Baseline 3 is appropriate given schema completeness.

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?

Description uses specific verb 'Get' and clearly identifies the resource as 'compliance evidence and audit artifacts.' It effectively distinguishes from siblings like get_article (which retrieves legal text) by specifying this returns auditor-facing requirements including 'retention periods and maturity levels.'

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 (when preparing for audits or gathering compliance documentation) by specifying it shows 'what documents, logs, and test results auditors will ask for.' However, it lacks explicit when-to-use guidance or contrasts with alternatives like check_applicability or get_article.

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?

While annotations confirm read-only/non-destructive safety, the description adds essential behavioral context: the premium authentication requirement and the fact that results include 'change summaries' (hinting at output format). This goes beyond the safety profile provided by annotations without contradicting them.

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 three-sentence structure is perfectly front-loaded: core function first, optional filtering second, and critical premium constraint third. Every sentence earns its place with zero redundancy or filler.

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 3-parameter schema with full coverage and existing annotations, the description is appropriately complete. It covers the core query pattern, optional filtering, and premium access constraint. It lacks explicit sibling differentiation, which prevents a perfect score, but adequately describes the tool's scope.

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 100% schema description coverage, the schema already fully documents the since date, regulation filter, and limit parameters. The description aligns with this ('since a given date', 'filter to a specific regulation') but adds no additional semantic details about formats or validation rules beyond what the schema provides, warranting the baseline score.

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 'List[s] all articles that changed since a given date, with change summaries,' providing a specific verb, resource, and scope. However, it does not explicitly differentiate from siblings like get_article_history or diff_article, which also deal with article changes over time.

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 critical prerequisite information (Premium feature — requires Ansvar Intelligence Portal) but offers no guidance on when to choose this over similar history-tracking siblings like get_article_history or diff_article. The usage relative to alternatives remains implied rather than explicit.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, covering safety profiles. The description adds valuable domain context (what recitals are for) and specifies 'full text' retrieval scope, but does not address error handling, not-found behavior, or rate limits.

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?

Two sentences totaling 21 words. The first states the action and target; the second provides essential domain context. No redundancy or filler—every word 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?

Given the simple 2-parameter schema with 100% coverage and presence of safety annotations, the description adequately explains the domain concept (recitals) and retrieval scope. Minor gap: without an output schema, it could briefly characterize the return format (e.g., plain text vs structured).

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 regulation and recital_number fully documented in the schema. The description does not add parameter-specific details (e.g., format constraints, valid ranges) beyond what the schema already provides, warranting the baseline score.

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 specific verb ('Retrieve') with clear resource ('full text of a specific recital') and distinguishes from sibling get_article by explaining that recitals 'provide context and interpretation guidance for articles', clarifying the distinct domain object being accessed.

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 second sentence explains the functional role of recitals ('context and interpretation guidance'), which implicitly signals when to use this tool versus retrieving the articles themselves. However, it lacks explicit 'when not to use' guidance or named alternatives like get_article.

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?

While annotations declare readOnlyHint=true, the description adds valuable behavioral context: specific token estimates (~500 vs ~1500), detailed content taxonomy of returns (cross-regulation pointers, methodology hints), and the preparatory workflow role. Does not mention caching or idempotency, but otherwise rich.

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?

Two sentences total with zero waste. First sentence front-loads the action and complete return value taxonomy. Second sentence provides imperative usage guidance. Every clause conveys unique information not duplicated in structured fields.

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 comprehensively enumerates return components (delegated acts, tiers, provisions, pointers, hints) that would appear in output. Combined with explicit token budgets and workflow positioning, it fully compensates for the missing output specification.

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 100% schema description coverage, the schema fully documents both parameters (regulation IDs with examples, detail_level with token counts and enum descriptions). The description references 'specific regulation' but adds minimal semantic detail beyond the schema's comprehensive coverage, meeting the baseline expectation.

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 opens with the specific verb 'Get' and resource 'analysis guidance,' then enumerates exact return components (delegated acts, proportionality tiers, etc.). It clearly distinguishes this from sibling retrieval tools like get_article by framing the output as methodological guidance rather than raw regulatory text.

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?

Explicitly states the temporal sequencing constraint: 'Call this BEFORE analyzing any regulation.' It further explains the value proposition ('discover what data is available and how to use it effectively'), providing clear guidance on when to prefer this over direct content retrieval 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?

Annotations already establish this is read-only and non-destructive. The description adds valuable behavioral context about output structure: results are 'grouped by category' when listing all, and specifying a regulation 'shows chapters and articles.' This compensates somewhat for the missing output schema, though it omits details like pagination or rate limits.

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 optimally concise with two efficient sentences. It front-loads the primary action ('List available regulations') and immediately qualifies the filtering options. Every word earns its place; there is no redundant or tautological 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?

Given this is a read-only listing tool with good annotations and no output schema, the description adequately covers what the agent needs to know: the scope of regulations listed, the grouping logic, and the hierarchical detail available. It could be improved by mentioning pagination or result limits, but it is sufficient for tool selection.

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 100% schema description coverage, the baseline is 3. The description adds functional semantics by explaining the consequence of using each parameter: 'category' filters the list, while 'regulation' switches the output to show detailed structure (chapters/articles). This goes beyond the schema's basic type definitions.

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 'List[s] available regulations' with specific verb and resource. It effectively distinguishes two operational modes (listing all vs. showing structure), which implicitly differentiates it from siblings like 'search_regulations' or 'get_article'. However, it does not explicitly name alternative tools to use for different use cases.

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 guidance on parameter usage: 'Without parameters, lists all...' versus 'With a regulation specified, shows chapters and articles.' This explains when to use each parameter. It lacks explicit naming of sibling alternatives (e.g., 'use search_regulations for text search'), but the contextual guidance is strong.

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?

Annotations already declare readOnlyHint=true. Description adds mapping directionality (controls→requirements) and relationship type ('satisfy'), but omits pagination behavior, return structure, or filtering logic despite having a 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?

Two efficient sentences with zero waste. First sentence establishes core function; second clarifies the specific relationship (satisfy). Main purpose is front-loaded appropriately.

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 read-only mapping tool with complete parameter schema, description adequately conveys expected output ('which articles satisfy') despite missing output schema. Could improve by noting return structure, but sufficient for agent selection.

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 100% description coverage, establishing baseline 3. Description mentions 'security framework controls' and 'specific security controls' reinforcing the framework/control parameters, but adds no syntax details, format examples, or clarifications beyond schema definitions.

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?

Description uses specific verb 'Map' with clear resources ('security framework controls' and 'EU regulation requirements'). Distinct from retrieval siblings (get_article, search_regulations) by specifying cross-reference 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?

Implied usage is clear from purpose statement (use when correlating security frameworks to EU articles), but lacks explicit when-to-use guidance, prerequisites, or named alternatives (e.g., when to use compare_requirements vs this tool).

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

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

Beyond the readOnlyHint annotation, the description adds valuable behavioral context: it discloses the return format ('articles with snippets highlighting matches'), specifies the truncation behavior ('32-token snippets'), and explains the impact on context window ('safe for context'). It does not mention error states or pagination, but covers output characteristics well.

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?

Three sentences with zero waste: sentence 1 states purpose, sentence 2 describes output, sentence 3 explains efficiency constraints. Information is front-loaded and every 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?

Given the absence of an output schema, the description appropriately compensates by explaining what the tool returns (articles with snippets) and critical behavioral constraints (32-token limit). Combined with complete input schema and annotations, this provides sufficient context for 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?

With 100% schema description coverage, the structured fields adequately document all three parameters. The description mentions 'matching a query' and 'across all EU regulations' which loosely map to the 'query' and 'regulations' parameters, but does not add semantic details beyond what the schema already provides. Baseline 3 is appropriate 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 states a specific verb ('Search'), resource ('EU regulations'), and scope ('across all'). It distinguishes from siblings like 'get_article' (implies specific retrieval) by emphasizing 'matching a query' and from 'list_regulations' by targeting 'articles' within regulations.

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 through 'matching a query' (use when searching rather than retrieving known content) and mentions 'Token-efficient' (use when context limits are a concern). However, it lacks explicit when/when-not guidance or named alternatives compared to siblings like 'get_article'.

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