Stratalize Governance

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

Discloses that the tool returns only reference data, not org-specific scores. Includes an example output showing stage classification, criteria, and remediation priorities. Does not contradict annotations (readOnlyHint=true, destructiveHint=false).

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 purpose, but includes an example and source reference which add value. Slightly longer than minimal but still efficient.

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 zero parameters and no output schema, the description fully covers what the tool does, when to use it, and includes an illustrative example. Source attribution adds credibility.

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?

No parameters exist (schema coverage 100%), so baseline is 4. Description adds context about what is returned, but no parameter details needed.

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 framework reference data about adoption stages, listing the four possible classifications. It distinguishes itself from siblings by clarifying it provides public reference data, not org-specific scoring.

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: 'assessing an organization FS AI RMF governance maturity stage' or 'preparing regulatory AI roadmap presentation'. Provides exclusion: 'not org-specific scoring' and alternative: 'Connect org MCP for org-specific scoring'.

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?

Annotations already declare readOnlyHint=true. The description adds transparency about the return payload fields (framework, deadline, controls, etc.) and the source of data, which is valuable beyond the 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 a source line, front-loading key information. The example sentence is slightly verbose but still effective.

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 one optional parameter and no output schema, the description covers all necessary context: usage scenarios, return structure, filtering option, and data source. It feels complete for the agent to decide and invoke 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?

With 0% schema description coverage, the description compensates well by explaining the optional nistFunction parameter, its purpose (filtering by NIST function), and providing an example (MAP). This adds significant meaning beyond the enum values in 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 explicitly states the tool returns a composite payload about EU AI Act compliance, with specific fields and optional filtering. It distinguishes itself from sibling tools by naming the EU AI Act context and deadline.

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 clearly indicates when to use the tool (assessing compliance readiness, board briefings) and provides an example. However, it does not explicitly state when not to use it or name alternatives, but the context is sufficiently clear.

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 already declare read-only and non-destructive. Description adds context: returns requirements and deficiency rates, plus source and AI/ML validation requirement. No contradictions, strengthens behavioral understanding.

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 a single paragraph that front-loads use cases, then details returned content, and provides an example. Information-dense but efficient; could be slightly more concise by separating parameter explanation.

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, so description explains return content: requirements across four areas and deficiency rates. Also addresses AI/ML specifics. Optional input parameter is simple and default provided. Complete for a read-only regulatory reference 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?

Schema has one parameter with enum and default, but schema description coverage is 0%. Description does not mention the parameter or explain how institution_type affects output. No value added over 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?

Description clearly states it returns Federal Reserve SR 26-2 and OCC requirements with exam deficiency rates. Verb 'returns' and resource are specific. Differentiates from sibling tools by targeting model risk management standards.

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 three use cases: preparing for exam, building compliant governance program, or assessing framework. Use cases are specific and contrast with sibling tools covering other 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 (readOnlyHint=true, destructiveHint=false) are consistent. Description adds value by detailing output structure (four functions with categories, subcategories, implementation guidance) and source, surpassing 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?

Single paragraph packed with useful information, front-loaded with use cases. Could be broken into clearer sections but remains informative without waste. Slightly dense but acceptable.

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 return value (four functions with categories, subcategories, implementation guidance) and provides a concrete example. Covers regulatory context and source, making the tool's output and purpose fully understandable.

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 only parameter (function_filter) has enum values GOVERN, MAP, MEASURE, MANAGE, which are mentioned in the description with context. This adds meaning beyond the schema. However, it could explicitly state that the parameter is optional and used to filter the output. Schema coverage is 0%, so description compensates but not fully.

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 multiple use cases (gap assessment, board governance, model risk examination, regulatory alignment) and explicitly names the resource (NIST AI RMF 1.0). It distinguishes from sibling tools by focusing on this specific federal standard, while siblings cover other 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?

Provides explicit when-to-use scenarios and gives an example (GOVERN as first control examiners check). Implicitly differentiates from siblings by context, though it does not list alternatives or when-not-to-use. However, the clarity of purpose and example suffice.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds value by detailing the tool's content, pricing models, and that no auth is required. It does not disclose any side effects, but none are expected for a read-only overview.

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 front-loaded with 'START HERE' and is packed with specific information (counts, namespaces, pricing, examples). Every sentence adds value, though it is slightly verbose; it earns its length given the 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?

For a discovery tool with no parameters and no output schema, the description is highly complete. It covers the tool's content, pricing, authentication requirements, and example outputs, providing everything an agent needs to decide to call it first.

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 zero parameters with 100% coverage, so the description need not add parameter details. It adequately explains what the tool returns without needing any input.

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 with specific counts, namespaces, and pricing tiers. It uses 'START HERE' to emphasize its role as an overview, distinguishing it from sibling tools that are specific to individual domains.

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 'START HERE' and 'Call this first,' indicating when to use it. It provides context on discovering C-suite briefs and tool categories, but does not explicitly mention when not to use it or provide alternatives beyond the implicit sibling list.

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, so the bar is lower. The description adds behavioral context: returns coverage, provides an example of control objectives, and cites the source. No contradictions. Could add details about pagination or response size but sufficient.

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 no wasted words. Front-loaded with use case and resource, followed by an illustrative example and source. 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?

The description explains what the tool returns (coverage across 13 control objectives) and provides an example of items covered. However, it lacks details on output format, and the undocumented parameter reduces completeness. With no output schema, more detail on return structure would be helpful.

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 parameter (nistFunction with enum) but schema description coverage is 0%. The description does not explain this parameter at all, failing to compensate for the lack of schema documentation. The enum values (GOVERN, MAP, etc.) are not described, leaving the agent without guidance on how to use them.

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 coverage across 13 control objectives from FCA Policy Statement PS7/24, with explicit use cases (assessing compliance readiness or benchmarking). It differentiates from sibling tools by specifying UK FCA context, distinct from other regulatory coverage 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 specifies when to use ('when assessing FCA model risk management compliance readiness or benchmarking an AI governance program against UK regulatory expectations'). It does not explicitly state when not to use, but the regulatory focus implies exclusivity. No alternatives mentioned, but clear context is provided.

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.