Stratalize Governance

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

Beyond the readOnlyHint=true annotation, the description discloses that this is 'Public mode returns FS AI RMF framework reference data only' and contrasts it with org-specific scoring. It also provides concrete output details with an illustrative example (gap to EMBEDDED is 18-24 months and 12-15 additional controls), giving agents a richer behavioral model than the annotation alone.

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 six sentences, each adding valuable information: limitation, usage, output, example, alternative, and source. It is front-loaded with the key 'not org-specific' caveat. The example sentence is somewhat detailed but illustrative; overall it is efficient without being padded, though not as terse as a two-sentence ideal.

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

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

For a zero-parameter, read-only tool with no output schema, the description fully covers the tool's return value (stage classification, criteria, priorities), use case, limitation (public vs org-specific), alternative path, and source. The example adds depth to the expected output, making it exceptionally complete for this tool's simplicity.

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

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

The tool has zero parameters, so the schema coverage is 100% vacuously. Per rubric, 0 params sets a baseline of 4. The description does not need to elaborate on parameters; the public/org distinction is contextual, not parameter-related. No additional param semantics are 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's function: it returns FS AI RMF adoption stage classifications (INITIAL, MINIMAL, EVOLVING, EMBEDDED) with stage criteria and remediation priorities. This is a specific verb+resource, and the focus on 'adoption stage' distinguishes it from sibling tools like get_nist_ai_rmf_requirements or get_model_risk_management_standards, which target different aspects.

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 gives explicit when-to-use guidance: 'Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation.' It also clearly states when not to use it ('not org-specific scoring') and points to an alternative ('Connect org MCP for org-specific scoring'), covering all three elements of strong usage guidance.

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) already declare the tool is safe. The description adds valuable behavioral context: it returns specific legal obligations, penalty amounts ($20,000 per violation), effective date, and a source citation. No output format is described, but the absence of an output schema makes the description's detailed content listing 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?

The description is longer than the minimal two-sentence example, but every sentence earns its place: usage guidance, key legal context, return contents, a concrete example, and source. It is front-loaded with 'Use when' and not bloated.

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 the tool (comprehensive state AI law) and no output schema, the description is complete: it explains what the tool returns (obligations, criteria, rights, penalties, EU comparison), provides a detailed example, and cites the source. It also clarifies the effective date, covering both usage and return value expectations.

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% for the single parameter 'system_type', which is fully described. The tool description adds a concrete example (AI-based loan underwriting) that illustrates how the parameter tailors the analysis, providing semantic context beyond the schema.

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

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

The description clearly states the tool's purpose: 'Returns developer and deployer obligations, high-risk AI system criteria, consumer rights, penalty structure... and comparison to EU AI Act.' It names the specific resource (Colorado SB 205) and differentiates from siblings like get_eu_ai_act_coverage and get_us_state_ai_legislation by focusing on Colorado's law with an EU 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?

Explicit 'Use when' guidance is provided: building an AI governance compliance roadmap, advising on high-risk AI deployment obligations in Colorado, or briefing boards on upcoming US state AI regulatory requirements. This clearly indicates when to select this tool over 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 already declare readOnlyHint=true and destructiveHint=false, establishing that this is a safe read operation. The description adds context about the significance of CRA ratings (e.g., a 'Needs to Improve' rating can delay mergers), but it does not disclose other behavioral traits such as return format, pagination, or rate limits. This is adequate but not 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?

The description is structured well, starting with usage guidance, then explaining the rating scale, a consequence, an example, and the source. All sentences contribute value, though the example sentence is slightly long and could be tightened. Overall, it is appropriately sized for the tool.

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 the use case, the meaning of the data, and gives an example of what results look like. The only notable gap is that it does not explicitly describe the return structure (e.g., rating, exam date, institution name), but the example implies it sufficiently for a low-complexity tool.

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

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

The schema has one parameter, institution_name, with no description (0% coverage). The description compensates partially by giving an example value ('Heartland Community Bank') and implying that the input is a bank name. However, it does not explicitly specify the expected format, such as full legal name vs. commonly used name, or whether an FDIC certificate number is acceptable.

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 indicates the tool provides CRA ratings for banks, listing the rating categories and mentioning the FFIEC CRA Ratings Database as the source. It also gives a concrete example. However, it never explicitly states the action (e.g., 'retrieves' or 'returns'), relying on the tool name and context to convey the purpose.

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 opens with 'Use when evaluating a bank's Community Reinvestment Act track record' and lists specific scenarios (merger application, charter acquisition, etc.). It does not mention alternatives or when not to use it, but the domain-specific use cases are clear and differentiate it from sibling 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 declare readOnlyHint=true and destructiveHint=false, so the agent knows it is a safe read operation. The description adds valuable behavioral context by detailing the return contents (back wages assessed, employees affected) and even provides an example output. It also cites the DOL WHISARD database as the source, which is a useful disclosure.

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 the usage context, then explains the output, and includes a concrete example. It is somewhat lengthy but every sentence adds value: usage guidance, return details, predictive insight, and an illustrative output. The structure is logical, though the example could be shortened.

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, the description takes on the burden of explaining return values, which it does by listing violation types and metrics (back wages, employees affected) and giving an example. It also covers 'why' to use the tool (predicts class action exposure). Missing details like pagination, ordering, or state filter behavior are minor given the tool's simplicity.

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%, so the description must compensate for the two parameters (employer_name and state). However, it does not explain either parameter explicitly. The example implies employer_name accepts an employer name, but there is no guidance on what 'state' means, whether matching is fuzzy/exact, or how to format names. This is a clear gap given the low structured 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 purpose with a clear verb and resource: 'Returns DOL Wage and Hour Division enforcement history' and lists violation types (FLSA overtime, minimum wage, child labor). It distinguishes itself from sibling compliance tools by focusing on DOL labor violations rather than sanctions, exclusions, or other regulatory 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 states when to use the tool: 'Use when screening an employer, vendor, or acquisition target for wage and hour compliance risk before a contract award, supply chain partnership, PE acquisition, or HR due diligence review.' It provides clear context but does not explicitly mention when NOT to use it or name alternatives, though the sibling list implies other 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?

The annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds valuable behavioral context by enumerating the exact return payload structure (framework, deadline, total_controls, controls[], hint, query timestamp), the optional NIST function filtering, and the data source (compliance_controls reference data). 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 four sentences, each serving a purpose: usage context, core function, example, and source. It is front-loaded with the 'when to use' clause, which might delay the primary function, but overall it is compact and free of fluff.

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 having no output schema and only one optional parameter, the description covers the tool's purpose, usage timing, return payload fields, filtering option, example use case, and data source. This is sufficient for an agent to select and invoke the tool correctly, especially with strong annotations.

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% (no property description for nistFunction), so the description must compensate. It explains that the parameter optionally filters by NIST function and provides an example (MAP) demonstrating its usage. However, it does not explain the default behavior when omitted or the meaning of each enum value, though the enum values are visible 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 clearly identifies the tool as returning EU AI Act coverage data, specifying the payload fields (framework, deadline, total_controls, controls[], hint, query timestamp) and optional NIST function filter. It distinguishes from sibling regulatory tools by explicitly naming the EU AI Act and its deadline, as well as the NIST function mapping.

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 opening sentence provides explicit when-to-use context ('assessing EU AI Act compliance readiness ahead of the August 2, 2026 enforcement deadline or preparing a board AI governance briefing'). It also gives a concrete example of filtering by MAP, but does not explicitly mention alternatives or when not to use this tool compared to sibling 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 declare readOnlyHint and destructiveHint, and the description adds beyond that: it names the data source (FEC electronic filings) and discloses the cryptographic attestation with a verification link. This is useful behavioral context.

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

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

Three concise, information-dense sentences: what the tool returns, when to use it, and attestation details. No filler or redundancy.

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 the tool's simplicity (one parameter, read-only), the description omits the meaning of the required parameter, which is essential for invocation. The source and attestation details are helpful but do not compensate for this critical gap.

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 is 'name,' and schema description coverage is 0%. The description never explains what 'name' refers to (committee? candidate? entity?), leaving the agent without enough information to populate the required field correctly.

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 specifies the tool's domain—Federal campaign finance—with concrete data elements (PAC committees, total political disbursements, receipts, footprint signal). This distinguishes it from sibling tools like get_ofac_sanctions_screening or get_federal_court_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 states 'Use for political risk monitoring and PAC compliance,' giving clear usage context. However, it does not mention alternatives or when-not-to-use, so it stops short of a 5.

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

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

Annotations already declare readOnlyHint=true, so the description adds significant context: it details the output (case names, docket numbers, courts, filing dates, nature of suit, active status), provides an illustrative example with estimated liability, and names the data source (CourtListener, 1M+ documents). This exceeds the baseline expected given annotation coverage.

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 the use case and returns information. The example is long but adds concrete value, and the source attribution is useful. It is somewhat verbose but every sentence earns its place; only minor trimming could improve conciseness.

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

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

The description covers purpose, output fields, use context, and data source, which is strong for a read-only screening tool. However, it lacks parameter semantics and does not mention any limitations (e.g., coverage gaps, rate limits), so it is not fully complete given the low schema coverage and absence of an 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?

Schema description coverage is only 33% (only 'court' has a description). The description does not explain 'party_name' or 'years_back', and no parameter details are given beyond the example. With low schema coverage, the description should compensate but does not, leaving the agent unclear on required format or defaults.

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: 'Use when screening a company, executive, vendor, or counterparty for federal litigation exposure' and specifies the resource: 'federal court dockets across all US district and appellate courts.' This distinguishes it from siblings like OFAC or SAM exclusion screening 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 opens with explicit 'Use when...' guidance, listing concrete scenarios (contract award, acquisition, investment, etc.). It does not explicitly mention when not to use or alternative tools, but the context is clear enough for an agent to differentiate from sibling 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 declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context about FTC consent orders (20-year restrictions, penalties, example of a real case), which helps the agent understand the nature and implications of the data. However, it does not describe tool-specific behaviors like output format or pagination, which would be useful for a lookup tool.

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 the 'Use when' statement and packs a rich example and source reference into four sentences. It is slightly longer than necessary but each sentence adds useful context. The structure is logical, though a direct statement of the tool's function would improve conciseness.

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

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

For a simple one-parameter lookup tool with no output schema, the description provides substantial context: usage scenarios, legal implications, an example, and a data source. It does not specify the return structure, but the example suggests the type of data available. Overall, it is sufficiently complete for the tool's simplicity.

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

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

The schema has one required parameter, company_name, with zero description coverage in the schema. The tool description does not explicitly define company_name or its format, though the example uses 'Tech Platform Corp' to imply it. Given the low schema coverage, more explicit parameter guidance would be needed to score higher.

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 resource as FTC enforcement history and the context of use, but never explicitly states the tool's action (e.g., 'retrieves' or 'returns'). The purpose is implied through the usage scenario and example rather than directly declared, which slightly weakens clarity.

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 specific scenarios ('antitrust exposure, consumer protection liability, data privacy enforcement history, or deceptive practices risk') and contexts ('acquisition, strategic partnership, or enterprise vendor selection'), making it clear when to use this tool. It also provides a concrete example that illustrates application, offering strong guidance versus 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 already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds substantial behavioral context by detailing what the tool returns (requirements across development, validation, monitoring, governance, plus deficiency rates) and includes an example finding and source citation. This goes beyond the annotations but doesn't describe return formatting or limitations, which is acceptable for a read-only report tool.

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 four sentences, front-loaded with use cases, followed by return content, an example, and source attribution. Each sentence adds distinct value with no wasted words, though it is slightly longer than necessary and could be tightened without losing meaning.

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 no output schema, the description richly covers the returned content and regulatory sources, satisfying most of the 'what does it return' question. However, the unaddressed institution_type parameter leaves a clear gap in understanding how output is customized, making the description incomplete despite its depth.

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

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

The schema has one parameter (institution_type) with enum values but no property description; schema description coverage is 0%. The description never mentions this parameter or how results vary by institution type, failing to compensate for the schema gap. The parameter name is somewhat self-explanatory, but the agent receives no guidance on how to set or use it.

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 specific use cases ('preparing for a model risk management examination') and a clear verb 'Returns' listing the content (Federal Reserve SR 26-2 and OCC requirements, exam deficiency rates). This clearly distinguishes the tool from sibling tools focused on other compliance topics, making the purpose unambiguous.

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

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

Explicitly states 'Use when' followed by three concrete scenarios: preparing for an MRM exam, building an SR 26-2 compliant program, or assessing an MRM framework. This provides strong context, but it does not mention exclusions or alternatives when the tool is not appropriate, so it falls slightly short of full guidance.

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 useful behavioral context by describing the returned content structure (four functions, categories, subcategories, implementation guidance) and providing a concrete example of GOVERN content. This goes beyond the 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 description is relatively long but well-structured, front-loaded with usage scenarios, followed by regulatory context, return content, an example, and a source citation. Every sentence adds value, but it could be trimmed slightly without losing essential 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?

The tool is simple (one optional param, no output schema), and the description covers use cases, regulatory alignment, the full structure of the return data, and an example. The only missing piece is clarification of the filter parameter's effect, which is already penalized under parameter semantics. Overall, it's quite complete for an agent to understand the tool's purpose and 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 description does not mention the function_filter parameter at all, despite schema description coverage being 0%. It says 'Returns all four functions' which could mislead the agent into thinking the filter is inoperative or unnecessary. The schema shows an optional enum filter, but the description fails to explain when or how to use it, leaving a significant 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 identifies the tool's function: returning NIST AI RMF 1.0 requirements with all four functions and implementation guidance. It names the specific resource (NIST AI RMF) and distinguishes it from sibling tools that cover other regulatory frameworks.

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 conducting an AI risk management gap assessment...') and provides multiple relevant scenarios. However, it does not mention when not to use it or explicitly compare to alternatives, so it misses the 'explicit exclusions/alternatives' threshold for a 5.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds meaningful transparency by disclosing that returns include both active and historical actions, the specific enforcement action types, and that the source is official OCC supervisory records, which goes 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 front-loaded with the use case and returns, includes a concrete example, and cites the source. It is slightly long but every sentence contributes useful context; the structure is logical and easy to scan.

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 one-parameter read-only tool with no output schema, the description covers the purpose, use cases, data scope (active/historical, action types), source authority, and a realistic example. It is complete enough for an agent to select and invoke the tool with confidence.

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 sole parameter institution_name already includes an example ('First National Bank of Springfield'). The description reinforces this example but adds little beyond what the schema provides, so the baseline of 3 is appropriate.

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

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

The description clearly states the tool 'Returns active and historical OCC enforcement actions' and specifies the resource (OCC supervision records). It distinguishes itself from sibling tools by naming the exact regulator and action types (formal agreements, consent orders, cease-and-desist orders, civil money penalties), making its unique purpose unmistakable.

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 frames 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 provides strong contextual guidance but does not explicitly state when not to use it or name alternatives, so it stops short of a 5.

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

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

Beyond the readOnlyHint and destructiveHint annotations, the description adds significant behavioral context: 'conservative fuzzy matching', 'match methodology metadata', 'list version binding for GSR verification', and 'Cryptographically attested with a post-quantum signed settlement receipt'. This discloses internal matching behavior and output characteristics 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 three sentences long, front-loaded with usage intent, and every sentence adds value: purpose, matching methodology, source, and verification. There is no fluff or repetition of schema 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?

For a read-only screening tool with only four parameters and no output schema, the description covers purpose, usage, source, matching behavior, and attestation. However, the lack of parameter semantics is a notable gap, and the description does not fully explain the result structure beyond metadata mentions, which prevents a perfect score.

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

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

The schema has 0% description coverage, and the description does not explain any of the parameters (alias, country, entity_type, entity_name). It only implies entity_name is screened via fuzzy matching, but provides no details on how the other parameters affect screening. The description fails to compensate for the lack of schema descriptions.

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 'Screens Treasury SDN and Consolidated lists' with a specific verb and resource. It also provides the source ('Source: Treasury OFAC synced lists') and differentiates from sibling screening tools like OIG and SAM by explicitly mentioning OFAC.

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 begins with 'Use when onboarding a vendor, counterparty, or individual requires OFAC sanctions screening with attested results', giving clear usage context. It does not explicitly exclude alternatives or name sibling tools, but the specific OFAC focus makes it clear when to use 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds valuable context: the source is a 'synced' HHS OIG LEIE list, and results come with a 'post-quantum signed settlement receipt' plus list version/as-of date in the attested payload. It does not describe edge-case behavior for missing inputs, but the attestation details go 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 three sentences and front-loaded with the primary use case. The source and cryptographic attestation details are relevant, though the 'Source: HHS OIG LEIE synced exclusions' sentence is slightly redundant with the first sentence but adds the 'synced' nuance.

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 covers purpose, source, and attestation, but there is no output schema and no mention of response structure, error conditions, or whether parameters are mutually exclusive. Since neither parameter is required in the schema, the tool's behavior when no query is provided is unclear, making the description incomplete for an exclusion screening 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 description coverage is 0%, so the description must compensate. It names both parameters ('provider name or NPI') and implies they are query options, which is useful. However, it does not clarify whether at least one is required, how both are handled together, or the matching behavior, leaving ambiguity that the schema (with no required fields) does not resolve.

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: 'Screens HHS OIG LEIE by provider name or NPI' and opens with a specific use case ('Use when credentialing a healthcare provider or verifying vendor eligibility'). This distinguishes it from sibling tools like get_ofac_sanctions_screening and get_sam_exclusion_screening by naming the exact federal exclusion list.

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 provides explicit guidance on when to use the tool: 'Use when credentialing a healthcare provider or verifying vendor eligibility against federal exclusions.' However, it does not explicitly mention when not to use it or mention alternatives, though the HHS OIG focus clearly differentiates it from similar exclusion screening 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 declare readOnlyHint=true and destructiveHint=false. The description adds useful behavioral details: conservative name matching, live attestation metadata, and cryptographic attestation with a verification link, which go beyond the basic read-only safety profile.

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, front-loaded with the usage statement, then functionality, then source/attestation. Every sentence earns its place with no wasted words.

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, the description hints at response features (live attestation metadata, cryptographic receipt) but doesn't explicitly describe the main result (e.g., exclusion status or list). It also omits a note that at least one parameter should be provided, though the OR phrasing implies it. Overall fairly complete for a read-only lookup 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 description coverage is 0%, so the description must compensate. It mentions all three parameters (entity name, UEI, CAGE) and implies OR semantics ('by entity name, UEI, or CAGE'), plus 'conservative name matching' gives additional meaning to entity_name. However, it doesn't clarify parameter combinations or constraints beyond the schema's maxLength.

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: verifying federal contractor or grantee eligibility against SAM.gov exclusions. It specifies the resource (SAM.gov Exclusions API) and the query keys (entity name, UEI, CAGE), distinguishing it from sibling screening tools like OFAC and OIG.

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?

Starts with 'Use when verifying...' providing explicit context for when to use the tool. However, it does not mention when not to use it or explicitly name alternatives, though sibling tools imply other screening sources.

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 useful behavioral context: the data content, granularity by state and NAICS, and a concrete example with source. This goes beyond a simple read-only declaration, though it lacks details on pagination or response format.

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, each earning its place: the first covers use cases, the second details return data, and the third provides an illustrative example and source. No filler or redundancy; information is front-loaded with the most important usage guidance.

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?

Without an output schema, the description fully explains return values by listing data points (counts, amounts, average sizes, top lenders, market share) and providing a concrete example with numbers. It also cites the data source, making it highly self-contained and complete for a data retrieval 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 description coverage is only 33% (year and state lack descriptions). The description compensates by indicating the tool filters 'by state and NAICS sector' and provides an example with specific values (Illinois, manufacturing, 2023), giving meaningful context to the parameters beyond the schema.

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

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

The description clearly states the tool 'Returns SBA 7(a) and 504 loan approval data' with specific metrics (counts, amounts, average sizes, top lenders, industry concentration). The verb 'Returns' is specific and the resource is well-defined, distinguishing it from sibling tools like CRA performance or FEC campaign finance.

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 opens with 'Use when assessing small business lending opportunity in a market, benchmarking a bank's SBA production against competitors, evaluating CRA lending performance by geography, or identifying industries with unmet capital needs.' This provides explicit, rich use-case guidance. However, it does not explicitly name alternative tools or state when not to use it, so it stops just short of the top score.

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

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

Beyond the readOnlyHint=true and destructiveHint=false annotations, the description adds significant behavioral context: it discloses access requirements (public MCP with no auth vs. x402 micropayments with pricing tiers) and notes that certain features (org intelligence, agent governance, role briefs) require OAuth. This helps the agent understand authentication and potential cost implications.

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 well-structured with 'START HERE' as an immediate attention-grabbing prefix, followed by the tool's function, access methods, and call instruction. It is somewhat dense, especially the detailed pricing breakdown, but each sentence contributes useful context, making it appropriately sized for a catalog overview tool.

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, no output schema, and a simple catalog-return function, the description fully covers the tool's role, access options, and usage order. It includes enough context for the agent to decide when to invoke it and what to expect, making it complete for its intended 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?

The input schema has zero parameters, so the description need not explain parameters. The baseline of 4 is appropriate because there is nothing to add, and the description focuses on the tool's purpose and access details rather than parameter syntax.

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 'START HERE' and states 'Returns the complete Stratalize tool catalog', clearly identifying it as an overview/discovery tool. This distinguishes it from the domain-specific sibling tools (e.g., get_adoption_stage, get_ofac_sanctions_screening) by positioning it as the catalog entry point.

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 explicit usage guidance with 'Call this first to discover tools by role or vertical.' It clearly says when to use this tool (as the initial step), but it does not explicitly name alternatives or state when not to use it, though the sibling list implies each specific tool is an alternative for targeted data.

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 useful behavioral context beyond that: it returns coverage across 13 specific control objectives and includes an example of PS7/24 requirements. This gives the agent a sense of what content to expect 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?

The description is three sentences and front-loaded with the 'Use when' directive. The example sentence adds concrete value, but the final 'Source: FCA Policy Statement PS7/24' is somewhat redundant since the second sentence already mentions PS7/24. Still, overall it is economically written.

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

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

Given the tool's simplicity (one optional param, no output schema), the description provides adequate context: regulatory source, number of control objectives, and a concrete example. It doesn't explain return format, but without an output schema, that may not be strictly necessary for a coverage-lookup tool. The missing parameter explanation slightly reduces completeness, but the core use case is well covered.

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 one optional parameter (nistFunction) with enum values, but schema description coverage is 0% and the description does not explain this parameter at all. The agent is left guessing what nistFunction means in the context of an FCA tool, how it filters the coverage, or why NIST functions are relevant to a UK FCA assessment. This is a significant 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 states a specific verb ('Returns coverage') and a precise resource ('13 control objectives from FCA Policy Statement PS7/24'), clearly distinguishing it from sibling tools targeting other jurisdictions/regulations. The use case context ('assessing FCA model risk management compliance readiness') further clarifies its scope.

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

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

It explicitly says 'Use when assessing FCA model risk management compliance readiness...' which provides a clear when-to-use signal. However, it does not explicitly mention when not to use it or contrast with alternatives like the EU AI Act tool, though the naming and purpose make the distinction fairly 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 already mark this as a safe read, and the description adds valuable context: an 'as of May 2026' date, a list of key states, and a data source. It does not describe the return format or edge cases, but the added context goes beyond the annotation baseline.

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 with a detailed example and cost estimate, making it informative but somewhat verbose. It front-loads usage scenarios rather than a direct purpose statement, which reduces conciseness.

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

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

The tool is simple (one optional parameter, no output schema), and the description provides rich domain context about the regulatory patchwork. However, it does not specify what the response contains (e.g., a list of laws, summaries), which is a gap given the absence of an 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?

The schema fully documents the optional 'state' parameter with 100% coverage, so the description does not need to add parameter details. The description adds no parameter-specific meaning, but the schema is sufficient, earning a baseline 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 positions the tool as covering US state AI legislation, with specific use cases like mapping compliance obligations and briefing teams. It distinguishes from siblings like get_colorado_ai_act_requirements by emphasizing the multi-state patchwork, though it never directly states what the tool returns.

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 'Use when' and lists three concrete scenarios, giving strong context for when to invoke this tool. It does not mention alternative tools or exclusions, so it stops short of full when-not guidance.

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