Stratalize AI Governance Intelligence

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

Annotations already mark readOnlyHint=true. The description adds context about the tool's scope (public vs. org) and what it returns, which is beyond the annotations. No contradictions.

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, front-loaded with key output details, no extraneous wording. 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?

Despite no output schema, the description is self-contained for a tool with 0 parameters. It explains what is returned and the limitation of public mode.

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

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

The input schema has 0 parameters with 100% coverage, so the baseline is 4. The description adds no parameter info, which is appropriate 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 clearly states the tool returns FS AI RMF Adoption Stage references (INITIAL, MINIMAL, EVOLVING, EMBEDDED) and distinguishes between public mode (framework stages only) and org-scoped classification. This specificity differentiates it from sibling tools.

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

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

The description explicitly says when to use the tool (public mode) and when not to (org-scoped), pointing to alternatives like 'Connect org MCP or the dashboard questionnaire'. This provides clear usage boundaries.

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 mark it as read-only; description adds content details (how returns obligations, example, source) without contradicting 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?

Description is relatively concise with front-loaded use cases, clear output listing, example, and source. Could be slightly more structured (e.g., bullet points) but no wasted sentences.

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?

No output schema, but description provides detailed return content and an example. Adequate for an agent to understand what it will receive.

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 parameter description. Tool description does not add further meaning to the single parameter, so baseline 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 returns specific obligations, criteria, rights, penalties, and comparisons for Colorado AI Act. It provides a concrete example, distinguishing it from generic AI regulation 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?

Explicitly states when to use (roadmap, advisory, board briefing) but does not mention when not to use or compare to siblings like 'get_us_state_ai_legislation' or 'get_eu_ai_act_coverage'.

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 indicate readOnlyHint=true and destructiveHint=false. The description adds valuable context: the tool queries an official database (FFIEC CRA Ratings Database) and explains rating categories and their regulatory weight. It does not mention rate limits or other limitations, but the transparency is good given the annotations already cover safety.

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 but includes an example and source, which add value. At about 100 words, it is informative without being verbose. Minor improvement could be trimming the example, but overall it is well-structured.

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

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

Given no output schema, the description should explain what the tool returns (e.g., rating string, exam date, etc.). It only implicitly suggests a rating but gives no structure. Also, the single parameter is not elaborated, leaving ambiguity. The completeness is insufficient for a tool with zero schema coverage and no output schema.

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 fails to explain the one parameter 'institution_name'—e.g., whether it requires a legal name, ticker, or exact format. The agent is left guessing how to fill in this required field, which is a critical gap.

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: evaluating a bank's Community Reinvestment Act (CRA) track record for specific regulatory contexts like mergers and acquisitions. It distinguishes itself from sibling tools by focusing on CRA ratings, a specific regulatory benchmark, and provides a concrete example (Heartland Community Bank).

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 explicitly lists use cases (merger application, charter acquisition, etc.) and explains the impact of CRA ratings on regulatory decisions. However, it does not mention when not to use the tool or provide alternatives among the many sibling tools, though the context is clear enough for an agent to select it appropriately.

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 indicate read-only and non-destructive nature. The description adds value by detailing what data is returned and highlighting that repeat violations predict class action exposure, which informs risk assessment.

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 informative with an example, but slightly lengthy. Key information is front-loaded, and each sentence adds value, though it could be trimmed.

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 no output schema, the description effectively explains return values (violations, back wages, employees affected) and includes an example. This covers the main information an agent needs to understand the tool's output.

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%, and the description does not explain the two parameters (employer_name required, state optional) beyond the purpose. The purpose implies employer_name use, but state is unexplained, which is a gap.

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 returns DOL Wage and Hour Division enforcement history, including specific violation types and metrics, distinguishing it from sibling tools focused on other compliance or benchmark data.

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 lists use cases like screening employers for compliance risk before contracts or acquisitions, providing clear context. It does not mention when not to use or alternatives, but the domain is specific.

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

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

No annotations are provided, so the description carries full burden. It fails to state whether the tool is read-only, requires authorization, has rate limits, or any side effects. The description only describes content, not behavior.

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

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

The description is a single sentence with no redundancy. Every word adds meaning. It is appropriately sized for the tool's simplicity.

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?

Lacking output schema and parameter explanation, the description does not adequately inform an agent about return values or usage details. For a tool covering a complex regulatory framework, more context (e.g., what 'coverage' means in practice) is needed.

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

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

The single parameter 'nistFunction' is undocumented. Schema coverage is 0%, and the description does not explain its role or how enum values affect results. The description adds no value beyond the schema's enum list.

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 the tool provides EU AI Act coverage with composite mode and implementation guidance. It is specific enough to distinguish from siblings like 'get_uk_fca_coverage' or 'get_category_ai_leaders', but the jargon ('composite mode', 'control library context') could be clearer.

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 offers no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, disclaimers, or context where this tool is most appropriate. Siblings cover various benchmarks, but without explicit when-to-use statements, an agent lacks selection criteria.

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 indicate read-only and non-destructive nature (readOnlyHint=true, destructiveHint=false). The description adds value by elaborating on the scope (active and historical federal dockets), source (CourtListener), and included data fields (case names, docket numbers, courts, etc.). No contradiction 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?

The description is a single paragraph that flows logically: use case, what it returns, and an illustrative example. It is not overly verbose but could be slightly more concise by omitting some details from the example. Overall, it is well-structured and informative.

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 adequately explains the return values (case names, docket numbers, courts, etc.) and provides context such as the data source and the number of documents. It is complete enough for an agent to understand what the tool provides.

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 3 parameters with only 33% coverage in descriptions. The description provides a detailed example but does not explicitly explain the meaning or usage of the 'court' and 'years_back' parameters beyond what the schema offers. It could be more helpful, e.g., noting that 'court' accepts identifiers like 'ca9' or 'ndca'.

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: screening parties for federal litigation exposure. It specifies the action (returns dockets) and the resource (federal court cases), and the context and intended use are distinct from its many sibling tools.

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

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

The description explicitly enumerates when to use the tool: 'Use when screening a company, executive, vendor, or counterparty for federal litigation exposure before a contract award, acquisition, investment, board appointment, or enterprise partnership.' It provides clear context and does not need to mention alternatives as it is unique among siblings.

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 indicate readOnlyHint=true and destructiveHint=false. The description adds value by explaining the nature of FTC consent orders (10-20 year duration, high penalties) and that restrictions survive acquisition, which is critical behavioral context beyond what annotations provide. No contradictions.

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 two compact sentences plus a source note, all front-loaded with the core use case. No fluff, every sentence adds value. The example efficiently illustrates the output and implications.

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 read-only tool with one parameter and no output schema, the description covers purpose, usage context, example output, and behavioral implications. It could clarify the exact response format, but the example gives a strong clue. Overall complete for its complexity.

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

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

With schema description coverage at 0%, the description bears the burden. It implies company_name is the target company but does not specify format or provide examples. However, the example 'Tech Platform Corp' gives a practical sense. The single parameter and straightforward purpose keep this at a 3.

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

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

The description clearly states the tool evaluates FTC enforcement history for antitrust, consumer protection, and data privacy risks for a company. It specifies the resource (FTC enforcement history) and provides a concrete example, distinguishing it from sibling tools like get_occ_enforcement_actions or get_cfpb_complaint_intelligence.

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?

It explicitly says 'Use when evaluating antitrust exposure... before an acquisition...' giving clear when-to-use guidance. It lacks explicit when-not-to-use, but the example and context are sufficient for tool selection. No direct alternatives are named, but the specificity compensates.

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 indicate readOnlyHint=true and destructiveHint=false, consistent with a read operation. The description adds value by describing the return content (requirements, deficiency rates) and including an example. No contradictions.

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 three sentences: usage context, return content, and a concrete example. No unnecessary text, front-loaded with purpose.

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

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

Given no output schema, the description adequately describes the output (regulatory requirements and deficiency rates). It includes a specific example and source attribution, making it complete for the tool's purpose.

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%, but the single parameter 'institution_type' has a clear enum. However, the description does not mention the parameter or explain how it affects results, leaving a gap despite the self-documenting enum values.

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 returns Federal Reserve SR 11-7 and OCC requirements for model risk management. It specifies the verb 'returns' and resource 'MRM requirements', and distinguishes from sibling tools by focusing on regulatory exam preparation and deficiency rates.

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 explicitly lists use cases: preparing for an MRM examination, building a compliant governance program, or assessing an MRM framework. It provides context but does not explicitly state when not to use or compare with 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?

Annotations show readOnlyHint=true and destructiveHint=false; description aligns by describing a read operation. Adds details about output structure (four functions, categories, subcategories, implementation guidance) and gives a concrete example (GOVERN). Provides value beyond 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?

Single paragraph front-loaded with use cases. Each sentence adds value: purpose, compliance alignment, output description, example, source. Slightly long but efficient; no waste.

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?

No output schema, but description explains what is returned (functions with categories, subcategories, implementation guidance) and provides regulatory context. Low complexity with one optional parameter; coverage is sufficient for the tool's purpose.

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 one optional enum parameter with 0% description coverage. Description implies filtering by mentioning the four functions but does not explicitly explain the function_filter parameter. Since parameter is optional and enum values are self-explanatory, some value is added, but not fully detailed.

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'), specific resource ('NIST AI RMF requirements'), and distinct purpose: returns all four functions with implementation guidance. Distinguishes from siblings like get_model_risk_management_standards by explicitly tying to NIST AI RMF and listing 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?

Explicitly lists four use cases (gap assessment, governance documentation, model risk examination, federal regulatory alignment) and gives regulatory context. Does not mention alternative tools or when not to use, so not a full 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?

Annotations (readOnlyHint: true) already indicate no mutation. The description adds value by detailing the types of records returned (formal agreements, consent orders, cease-and-desist orders, civil money penalties) and that they are the same records used by OCC examiners. This goes beyond 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 concise with three sentences plus an example. It is front-loaded with the purpose and usage context. The example is relatively long but informative. Every sentence serves a purpose, though the 'Source:' line is somewhat extraneous.

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?

There is no output schema, but the description lists the types of enforcement actions returned. For a simple tool with one parameter and clear annotations, the description provides sufficient context for an agent to use it correctly. It does not mention pagination or limits, but given the nature, it is not critical.

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% and the parameter 'institution_name' is clearly described in the schema. The description's example ('First National Bank of Springfield') adds context for how to use the parameter, increasing its value beyond the schema alone.

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 what the tool does: returns active and historical OCC enforcement actions. It specifies the resource (OCC enforcement actions) and the verb (returns). It also distinguishes from sibling tools by focusing on regulatory risk for national banks or federal thrifts, with a concrete example.

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 explicitly says when to use the tool: 'Use when assessing regulatory risk for a national bank or federal thrift before a merger, acquisition, partnership, correspondent banking relationship, or vendor engagement.' It does not explicitly state when not to use or provide alternatives, but the clear use case compensates.

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 indicate read-only and non-destructive. Description adds value by detailing returned data elements (counts, amounts, average sizes, top lenders, industry concentration) and citing source (SBA Public Loan Disclosure Data). Does not cover limitations 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?

Four sentences: first lists use cases, second details returned data, third gives concrete example, fourth cites source. Information-dense with no redundancy. Front-loaded with primary purpose.

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

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

Provides good context and example output, but no output schema exists. Description omits structure details (e.g., response format). Agent must infer from example. Adequate for simple query but incomplete for programmatic use.

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

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

Schema has 3 parameters with only 'industry' described. Description mentions state and NAICS sector in example but does not explain 'year' or format requirements. With 33% schema coverage, description insufficiently compensates for missing parameter documentation.

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

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

Description clearly states specific purpose: assessing small business lending opportunity, benchmarking SBA production, evaluating CRA lending, and identifying unmet capital needs. Returns specific data (counts, amounts, average sizes, top lenders, industry concentration) distinguishing it from sibling 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?

Explicitly lists when to use the tool (assess lending opportunity, benchmark, evaluate CRA performance). Provides an example but does not include exclusions or alternatives. Context is clear given distinct sibling names.

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 must cover behavioral traits. It states 'No auth required', which is a key behavioral detail. However, it does not disclose any other traits like caching, rate limits, or side effects. For a read-only catalog retrieval, this is adequate but not exhaustive.

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 two sentences, very concise, and every word contributes value. It is front-loaded with 'START HERE', immediately signaling the tool's purpose. No redundant or vague wording.

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?

Although there is no output schema, the description gives a solid overview of what is returned (tool catalog with role-based recommendations and listed categories). It does not detail the exact structure or pagination, but for a 0-parameter tool intended as an entry point, it is sufficiently complete for an agent to understand the output.

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 no parameters (0 params), so schema coverage is 100% and the description does not need to explain parameters. The description adds value by specifying the content of the return (catalog of 107 public and 69 org tools with role-based recommendations), which is meaningful for an agent.

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 returns the complete Stratalize tool catalog (107 public, 69 org tools) with role-based recommendations. It explicitly marks itself as the starting point, distinguishing it from the many sibling tools that return specific benchmarks or intelligence.

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 instructs 'Call this first to discover all available tools', providing clear guidance on when to use it. While it doesn't explicitly exclude other use cases or name alternatives, the context of sibling tools implies that this is the entry point for tool discovery.

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 and description omits behavioral traits (e.g., data source latency, side effects, permissions). Agent gets no insight into execution impact.

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

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

Single sentence is concise, but the density of technical terms reduces clarity. Could be restructured for better readability.

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, no annotations, and one unexplained parameter, the description fails to equip the agent with enough information to use the tool 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 single parameter 'nistFunction' is not explained in the description; its enum values (GOVERN, MAP, etc.) may be ambiguous without context. Schema coverage is 0%, and description adds no clarification.

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 mentions specific framework and context but lacks an explicit verb indicating what the tool returns or does. The jargon ("composite mode", "control library context") obscures the core operation.

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

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

No guidance on when to use this tool vs siblings like get_eu_ai_act_coverage. The description does not state prerequisites or typical use cases.

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. The description adds meaningful behavioral context: data freshness ('As of May 2026'), scope (national summary if state omitted), and source attribution (NCSL + Stratalize). This goes beyond what annotations provide.

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 information-rich but not overly verbose. It is structured with a clear lead use-case, context, example, and source. Each sentence adds value, though it could be slightly more concise by removing the example's specific cost figure.

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 list-style tool without an output schema, the description provides adequate context: explains the regulatory patchwork, gives a concrete example, and cites sources. It tells the agent what to expect without needing to specify return format.

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% coverage with clear description of the single optional parameter. The tool description adds value by explaining how the parameter is used in context (state vs. national summary) and what the output covers (compliance regimes, jurisdictions), complementing 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 implies the tool retrieves US state AI legislation information, with a use-case oriented statement ('Use when mapping...'). While it does not explicitly state 'This tool retrieves...', the purpose is discernible from the usage guidance and context. It distinguishes from siblings like get_colorado_ai_act_requirements by focusing on multi-state coverage.

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 explicitly states when to use the tool ('Use when mapping AI regulatory compliance obligations...') and provides a concrete example. It does not explicitly state when not to use it or directly compare to siblings, but the context and sibling list suggest it is for broad overviews.

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