Stratalize Healthcare

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description does not need to restate safety. It adds value by specifying what is returned (cost per case medians, revenue mix percentages) and providing a concrete example with quartile analysis and source information (ASCA and CMS 2024 composite). This goes beyond simple read-only indication and helps the agent understand output interpretation.

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 about 70 words and front-loaded with usage context. The example sentence is a bit long and contains a confusing quartile statement, but it provides useful illustrative information. The structure is logical (usage, returns, example, source) and not overly verbose, though the example could be tightened.

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 and 0% schema coverage, the description needs to fully explain parameters and return structure. It gives a high-level output summary and an example, but does not mention the state parameter or explain how filtering works. It is adequate but has clear gaps, making it minimally acceptable.

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 (state, specialty). It mentions specialty in the output ('by specialty') and the example uses 'Orthopedic,' but it never explicitly explains that the specialty parameter filters or groups results. State is completely omitted. The description fails to adequately clarify parameter meaning.

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: 'Returns cost per case medians and revenue mix percentages by specialty.' This is a specific verb+resource combination, and the ASC context distinguishes it from sibling benchmark tools like get_cms_facility_benchmark. The use cases (benchmarking ASC financial performance, evaluating acquisitions, board reports) further clarify its 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 provides explicit 'when to use' guidance: 'Use when benchmarking ASC financial performance, evaluating an ASC acquisition, or preparing an administrator board report.' However, it does not mention when not to use it or name alternative tools, so it lacks exclusions. This fits the 'clear context, no exclusions' level.

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 value beyond annotations by explaining the tool's behavior: it compares inputs to national benchmarks and flags upcoding risk (as shown in the example). It also discloses data source (CMS and OIG composite), which 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?

The description is three sentences, front-loaded with usage guidance and output summary. The example is compact and illustrative. No redundant language; every sentence earns its place.

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

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

For a simple read-only tool with 3 optional parameters and no output schema, the description covers usage, outputs, a worked example, and data source. The only notable gap is the lack of explicit parameter explanations for annual_claim_volume, but the example covers most of the input semantics. Overall, sufficiently complete for an agent to invoke it 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?

Schema description coverage is only 33% (one property described). The description partially compensates by illustrating parameters through the example: 'Cardiology practice E/M mix at 67% level 4/5 visits' clarifies specialty and level_4_5_percentage. However, annual_claim_volume is not explained anywhere, and the description does not systematically map parameters to their roles.

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 E/M distribution benchmarks, upcoding risk signals, OIG audit priority themes, and RAC watchlist.' This goes beyond a simple verb+resource by listing specific outputs and providing a concrete example, making it distinctive from sibling benchmark 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 opens with when to use the tool: 'Use when assessing coding compliance risk before an OIG audit, preparing for a RAC review, or building a revenue integrity program.' While it doesn't name alternatives, the use cases are specific enough to guide selection. Missing exclusions (when not to use) but strong context.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the bar is lower. The description adds valuable context by listing return components (peer_group, benchmark_percentiles, metadata, source attribution, optional database_row detail) and the data source (CMS HCRIS cost reports). No contradiction or missing safety disclosure is apparent.

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 with no fluff. It front-loads the use case, lists outputs, provides a concrete example, and cites the source—all in a compact structure. Every sentence earns its place.

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

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

Given there is no output schema, the description must explain return values and does so by naming the key output components and source. It also provides an example for concrete understanding. It lacks detailed shape or format of the returned objects, but for a benchmark tool with good annotations and clear purpose, it is sufficiently complete for agent invocation.

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

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

Schema coverage is 0%, so the description bears the burden of explaining parameters. It does mention 'by bed size, state, and hospital type' and gives an example ('300-bed acute care hospital in Illinois'), which partially compensates. However, it does not specify formats (e.g., state as abbreviation vs full name, bed_size as number) or clarify which parameter controls the optional database_row detail, leaving meaningful gaps.

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 benchmarks hospital operating costs against CMS peer cohorts and returns specific outputs like peer_group context, benchmark_percentiles, metadata, and source attribution. The verb 'benchmarking' plus resource 'hospital operating costs against CMS peer cohort' distinguishes it from sibling tools focused on other benchmarks (ASC, supply chain, etc.).

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 it: 'when benchmarking hospital operating costs against CMS peer cohort or preparing a healthcare CFO board presentation.' It does not mention alternatives or exclusions, but the use case is clear and distinct from sibling tools, which earns it a 4.

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 safety. The description adds value by specifying the source database, providing an example with a concentration threshold, and clarifying the aggregate nature of the data. It does not contradict annotations.

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

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

The description is front-loaded with the use case, followed by return details and a concrete example. The example is somewhat lengthy but adds valuable context. Overall, each sentence contributes to understanding 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?

Given no output schema and only three simple parameters, the description provides a solid overview including use cases, return contents, an example, and the data source. It lacks explicit parameter guidance and error behavior, but for a read-only data lookup it is reasonably complete.

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%, so the description must compensate. It mentions 'by physician or manufacturer' and 'program year breakdown,' which maps to the three parameters. However, it doesn't clarify that recipient_name is required or detail parameter formats/constraints, and the phrase by manufacturer could mislead an agent into thinking manufacturer_name alone is sufficient.

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 CMS Open Payments aggregates by physician or manufacturer' with specific detail on payment amounts, types, and program year breakdown. It distinguishes itself from sibling tools by naming the CMS Open Payments Sunshine Act database and the specific use case.

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: 'Use when assessing physician payment transparency risk, evaluating manufacturer relationships, or preparing Sunshine Act compliance reporting.' It does not mention when not to use or alternatives, but the use case is clearly defined.

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

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

Annotations already mark the tool as read-only and non-destructive. The description adds valuable behavioral context by detailing the nature of the output (domain weights, national benchmarks, improvement priorities) and providing a concrete example with specific numbers and methodology source. This 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 concise and front-loaded with the use case. The example is detailed yet efficiently communicates typical output and improvement scenarios. Every sentence adds value without unnecessary 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?

Given the lack of an output schema, the description does a good job explaining the high-level return content and provides illustrative data. However, it leaves parameter usage ambiguous and does not clarify whether inputs are required or how they affect results. For a tool with 3 optional parameters, this is a notable 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?

Schema coverage is 0% and the description does not explain the three parameters (state, hospital_name, current_star_rating). The example indirectly references current_star_rating ('hospitals moving 3 to 4 stars'), but state and hospital_name are entirely unexplained, leaving the agent without guidance on how to populate 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's purpose: 'Use when advising on CMS Hospital Star Rating strategy or benchmarking a hospital quality performance trajectory.' It specifies what it returns: 'domain weights, national distribution benchmarks, and improvement priorities.' This distinguishes it from siblings like get_hospital_care_compare_quality by focusing on strategy and benchmarks rather than general quality 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?

Explicit 'Use when' guidance is provided, making the intended context clear. However, it does not mention when not to use this tool or suggest alternatives such as get_hospital_care_compare_quality, so it stops short of full exclusions.

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 read-only and non-destructive behavior. The description adds substantial context: the cost+15% pricing model, $0.02 USDC per call SLA, 503 error handling with no charge, and provenance disclosure via data_sources[] and synthesis.output_hash. This goes far 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 dense but well-organized, covering use case, return fields, example, cost, error handling, and provenance. Each sentence adds value without excessive fluff, though it could be marginally streamlined.

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 two-parameter read-only tool with no output schema, the description provides enough context: purpose, output fields, an example, cost, failure behavior, and data source. It is sufficiently complete, though quantity_units semantics could be more explicit.

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 zero description coverage, so the description must carry the burden. It explains that ndc_or_name takes an NDC (with example 42385096230) and implies quantity_units affects 'optional quantity quote.' However, it does not mention that ndc_or_name may accept a name, and quantity_units is not fully detailed.

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

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

The description clearly identifies the tool as providing Mark Cuban Cost Plus Drugs transparent retail pricing for a specific NDC, with expected return fields and an example. This distinguishes it from sibling tools like get_nadac_drug_benchmark, which target different pricing 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?

The description explicitly states when to use this tool: 'Use when a patient, benefits manager, or procurement agent needs... transparent retail pricing for a specific NDC.' It provides clear context but does not explicitly mention alternatives or when not to use it, though the niche is sufficiently 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 the tool as read-only and non-destructive. The description adds valuable context about the data source (FDA 510(k) database), the settlement receipt attestation, and a verification URL, giving extra trust and provenance details beyond the annotations. It does not cover pagination or error behavior, but the added context is meaningful.

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, front-loaded with the core function, then returns fields, use cases, and trust/verification details. Every sentence earns its place, and there is no redundant or filler content.

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 tool with no output schema, the description is complete: it explains what the tool returns, provides use cases, and includes trust/verification info. The annotations cover safety, so no further behavioral disclosure is necessary.

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, but the description states 'by device name,' which clearly identifies the sole parameter's semantic role. Since there is only one parameter and no enums, this compensation is sufficient, though it does not add format or syntax details beyond the schema's min/max length.

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

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

The description clearly states the tool retrieves FDA 510(k) device clearance history by device name, listing specific return fields (K numbers, applicants, decisions, receipt dates). This distinguishes it from sibling tools like get_fda_recall_history, which cover recalls rather than clearances.

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 use cases: medtech competitive intelligence, regulatory pathway research, and supplier qualification. It does not spell out when-not-to-use or name alternatives, but 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 declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds return content and provenance (FDA FAERS), plus an unusual attestation/receipt feature, but does not disclose rate limits, error handling, or permission requirements. With annotations covering the essential behavior, the extra detail is moderate 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 a single dense sentence that front-loads the main purpose but then appends pricing and attestation details, making it less structured. It is not overly long but could benefit from clearer separation of core purpose, use cases, and meta-information. It does not waste words, but the flow is a bit run-on.

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 two-parameter read-only tool with no output schema, the description covers the main purpose, return types, source, and even pricing/verification. It lacks explicit details on the `limit` parameter's effect on results, but overall it is reasonably complete for an agent to judge whether to invoke it.

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

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

Schema description coverage is 0%, so the description must compensate. It implicitly clarifies drug_name through 'by medicinal product name' but does not mention the `limit` parameter at all. The description adds some meaning for the required parameter but fails to explain the optional limit, 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 states the verb ('get') and resource ('FAERS drug adverse event reports from OpenFDA by medicinal product name'), and further details the specific return data (serious event counts, reactions, outcomes, chronology). This distinguishes it from siblings like get_openfda_adverse_events by its specific focus on product name and serious-event analysis.

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: 'pharmacovigilance monitoring, safety signal detection, and clinical risk agents.' This provides clear context for when to use the tool, though it does not mention when not to use it or name direct alternatives, stopping 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 and destructiveHint=false, so safety is covered. The description adds useful context: source (FDA labels), cost ($0.10), and cryptographic attestation. But it does not disclose return format, pagination, or limitations, so the added value is moderate.

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 purpose and outputs, followed by use cases and cost/verification details. Each sentence adds some value, though the attestation note could be trimmed for an AI agent.

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

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

For a simple 1-parameter read-only lookup, the description covers the key return fields, source, cost, and verification. It lacks any mention of drug_name format, but overall is reasonably complete given the lack of an output schema and low parameter 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?

Schema description coverage is 0% for the single parameter drug_name, and the description does not elaborate on its expected format (e.g., brand vs generic, case sensitivity). The param name and tool context imply it's a drug name, but the description fails to compensate for the missing schema detail.

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

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

The description clearly states it returns FDA drug label intelligence with specific fields (brand/generic names, manufacturer, route, indications, warnings). It distinguishes this from sibling tools like get_drug_adverse_events by focusing on label content, though it doesn't explicitly name alternatives.

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 use cases: formulary review, pharmacology research, and adverse event context. However, it lacks exclusions or alternative tool suggestions, so it doesn't fully meet the 'when not to use' bar.

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

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

Beyond the readOnlyHint annotation, the description discloses several crucial behaviors: match_confidence distinguishes exact NDC matches from name matches, the 'never silently returning' guarantee protects against misidentification, HTTP 503 (no charge) when upstream sources are down, and data_sources[] provenance with synthesis.output_hash. It also provides pricing (SLA cost) and an illustrative example, offering rich operational transparency.

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-dense yet compact. Every sentence adds value: use case, return fields, behavioral guarantee, example, source, SLA/cost, error behavior, and provenance. It is well-structured, starting with the primary use and flowing to operational details, with no fluff 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?

Given the minimal schema (one parameter, no descriptions) and no output schema, the description offers a comprehensive picture: what inputs are accepted, what outputs to expect, how errors surface (503), and how provenance is disclosed. It also explains the match_confidence semantics and gives a realistic example, making it fully self-contained for an agent to use effectively.

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 only a single string parameter with zero description coverage (0%), so the description must fully compensate. It does so by stating the parameter accepts either an NDC or drug name and provides a concrete example ('NDC 41163-703-10'), clarifying the expected format. This gives the agent exactly what it needs to invoke the tool correctly despite the bare 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 function: retrieving FDA drug recall status for an NDC or drug name. It specifies the target users (pharmacy, supply chain, compliance agents), the key return fields (status, classification, reason, dates), and the unique guarantee of not silently returning a different product's recalls. This goes beyond a generic statement and distinguishes it from siblings like get_fda_recall_history.

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 starts with 'Use when a pharmacy, supply chain, or compliance agent needs FDA drug recall status,' providing clear context for when to invoke the tool. It does not, however, explicitly mention when not to use it or name alternative tools, so it falls short of the highest bar for exclusionary 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 behavioral context beyond that: it describes the return type (benchmark per bed), the optional gap analysis, and provides a concrete example with a renegotiation trigger. It does not go into rate limits or data updates, but for a read-only benchmark tool this is 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 three sentences: the first sentence gives the use case, the second states the core functionality, and the third provides a concrete example and data source. Every sentence adds value, and it is front-loaded with the most important 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?

Given there is no output schema, the description adequately explains what the tool returns (benchmark cost per bed) and the optional gap analysis. It includes a real-world example and cites the data source, which gives a complete picture for a benchmark tool. It could be slightly more explicit about the required vendor parameter, but overall it is sufficiently complete.

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, so the description must compensate. It mentions 'actual cost and bed count' which maps to annual_cost and bed_count, and the example uses 'Epic' to hint at vendor_name. However, it does not explicitly define each parameter name, type, or format, leaving some ambiguity for an agent.

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

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

The description clearly states the tool's function: 'Returns benchmark cost per licensed bed with optional gap analysis.' It uses a specific verb ('Returns') and resource ('benchmark cost per licensed bed'), and the use case ('benchmarking EHR maintenance costs') distinguishes it from sibling benchmarks like get_asc_benchmark or get_gpo_contract_benchmark.

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 context: 'Use when benchmarking EHR maintenance costs before a contract renewal or evaluating health IT budget efficiency.' It also explains when the optional gap analysis applies ('when actual cost and bed count are provided'). However, it does not explicitly name alternative tools to use instead, so it stops short of a full 5.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable context beyond annotations: Class I recall implications (mandatory press releases, procurement review obligations) and an illustrative example that reveals output patterns. It does not mention pagination or date ranges, but the added context earns a solid 4.

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 moderately sized with several sentences, but each earns its place: use case, return types, Class I context, example, and source. It is front-loaded with the usage context and not overly verbose. Slightly long for a 1-5 scale, but 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?

The tool has no output schema, so the description carries the burden of explaining return values. It does so: recall classifications with descriptions and reasons, plus an example. It lacks parameter-level details and temporal scope, but for a simple two-parameter tool, it is largely complete.

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 mention company_name or product_type parameters at all. The example implies drug recalls, but there is no explicit guidance on how to set product_type or which values to supply. With only 0% schema coverage, the description needed to compensate but did not.

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 FDA recall classifications with product descriptions and recall reasons, and frames it for evaluating companies for product safety risk. This specific verb+resource distinguishes it from related tools like get_drug_recall_status, which focuses on a narrower drug recall status.

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: 'when evaluating a pharmaceutical company, medical device manufacturer, or healthcare vendor for product safety risk, supply chain exposure, or regulatory compliance standing.' It provides clear context but does not name alternatives or exclusions, which would be needed 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, and the description adds meaningful context beyond that: the type of data returned, example values (18% median savings, 22% leakage rate), an implied threshold behavior ('leakage above 30% triggers mandatory compliance programs'), and the source (HFMA and CMS composite). This enriches the agent's understanding of what 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 front-loaded with the use case and returns list, followed by a concrete example and source. It is concise but packs useful illustrative detail. The example sentence adds interpretive value (what leakage rate means, threshold implication) without being verbose.

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 benchmark tool with one optional parameter and no output schema, the description covers purpose, usage context, return fields, example values, and source. The main gap is the undocumented category parameter, but overall the description provides sufficient context for the agent to select and invoke the tool in many scenarios.

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 optional 'category' parameter with zero description coverage (0%). The description does not explicitly explain the parameter's meaning, valid values, or default behavior. It only hints via the example 'Acute care,' leaving the agent to infer that category might be a facility type. With such low schema coverage, the description needed to compensate but largely does not.

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 benchmarking GPO contract performance' and explicitly lists the returned metrics: 'typical GPO savings percentage, leakage rate, and top savings categories.' This specific verb+resource framing distinguishes it from sibling benchmark tools like get_hospital_supply_chain_benchmark or get_asc_benchmark by naming the GPO contract domain.

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 clear context on when to use the tool: 'when benchmarking GPO contract performance or building a supply chain cost reduction case for a hospital board.' This is specific and useful, though it does not explicitly mention when not to use it or name alternative tools, which would elevate it to 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, covering safety. The description adds useful behavioral context: it returns 'top recommended vendors, AI consensus narrative, and sample size' and identifies the data source ('Stratalize AI citation composite'). It does not disclose any edge cases or limitations, but given the simple read-only nature, the added context is 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 concise and well-structured. It leads with the usage context, states the output, then provides a concrete example. Every sentence adds value and there is no redundant or filler content.

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 tool with one parameter and no output schema, the description is quite complete: it explains the use case, return contents, and includes a realistic example with sample data. It does not detail a formal response schema, but given the absence of an output schema and the low complexity, the description covers most needed information. Slightly less complete because it doesn't specify category format or any conditions that might cause empty results.

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 'category' with 0% description coverage, so the description must compensate. It does by explaining that the parameter refers to a 'healthcare technology category' and gives an example ('EHR category'). This adds meaning beyond the bare schema type (string), though it stops short of enumerating valid category values or format.

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 researching which vendors dominate AI recommendations in a healthcare technology category or validating a health IT vendor selection.' It specifies the resource ('healthcare category intelligence') and the action ('researching', 'validating'). The example with EHR category and specific vendors distinguishes it from sibling tools that focus on benchmarks, pricing, or facility quality.

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

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

The description provides an explicit 'Use when' statement that defines the primary use cases. It does not explicitly mention alternatives or when NOT to use, but the context is clear enough. The example further illustrates a typical scenario, which helps an agent decide when to invoke this tool over others.

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 a safe read-only operation, so the bar for additional transparency is lower. The description adds value by specifying the data returned (market rates for categories and facility types), providing a sample output/interpretation threshold, and citing data sources. It does not mention response structure or pagination, but these are less critical for a benchmark 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 four sentences: use case, return value, example, and source. Every sentence provides useful information, and the example is concrete and instructive. It is slightly longer than needed, but the structure is logical and front-loaded with the 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?

For a two-parameter read-only tool with no output schema, the description covers the essential aspects: when to use, what data is returned, an illustrative example, and data provenance. It could benefit from explicit parameter mapping, but overall it is complete enough for an agent to 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?

Schema description coverage is 0%, so the description must compensate. It lists valid category examples (EHR, staffing, food service, waste management, med-surg), which effectively documents the 'category' parameter. However, it does not explain the 'vendor_name' parameter beyond implying it from the tool name and use case, nor does it clarify parameter formats or required/optional status. Partial compensation but not complete.

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 market rates for specific vendor categories (EHR, staffing, food service, etc.) by facility type. It also provides a precise use case ('benchmarking a healthcare vendor quote') and includes a concrete example, making it unmistakable what the tool does.

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 benchmarking a healthcare vendor quote or preparing a supply chain contract negotiation.' It does not explicitly name alternatives or exclude scenarios, but the context is clear enough that a model can infer when to choose this tool over 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 declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds value by specifying the data source ('CMS Care Compare synced data'), the domains covered, and an example benchmark. This goes beyond minimal annotation coverage and gives the agent useful context about data scope and freshness.

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: use-case guidance, data returned, and a concrete example with source attribution. It is front-loaded with the primary purpose, every sentence adds meaningful detail, and nothing is redundant or wasted.

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 single-parameter, read-only query tool with no output schema, the description adequately conveys what data is returned and at what granularity. It mentions the core measure categories and provides an illustrative example. It could be slightly broader on measure units or timeframes, but given the tool's simplicity, it is sufficiently complete.

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 already documents 'hospital_name' as 'Hospital or facility name' with 100% coverage. The description adds only that scores are 'by hospital,' which is implicit in the parameter. Since the schema fully explains the parameter, the description does not need to add more and meets the baseline.

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 CMS Hospital Compare scores including safety, readmissions, patient experience, mortality, and star rating by hospital. It uses a specific verb ('Returns') and resource ('CMS Hospital Compare scores'), and the example with Northwestern Memorial concretely illustrates the output. This distinguishes it from sibling tools like get_cms_star_rating by emphasizing a broader multi-domain quality dataset.

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 when to use: 'evaluating hospital quality for a referral network decision, acquisition target assessment, or competitive quality analysis.' It does not explicitly name alternatives or exclusions relative to sibling tools, but the use cases are clear and provide strong guidance on appropriate context.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds context about the data source (CMS HCRIS cost reports), the output structure (percentiles by bed size and state), and an illustrative example. This goes beyond the annotations by informing the user of the nature of the returned data and its provenance.

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, front-loaded with the usage trigger, and every sentence provides value: the use case, the return metrics, and a concrete example with source attribution. No fluff 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?

Given the low complexity (two parameters, read-only) and absence of an output schema, the description explains the return values (supply cost percentage at p25/p50/p75) and provides an example of interpretation. It also cites the data source. Minor gaps like optional state behavior are not addressed, but overall it is sufficiently complete for an agent to invoke the tool correctly.

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

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

The input schema has no descriptions (0% coverage), so the description must compensate. It clarifies that bed_size refers to the number of beds via the example (200-bed hospital), and state is used for stratification. However, it does not specify the format for state (e.g., two-letter abbreviation) or how bed_size is interpreted (exact value or range), leaving some ambiguity.

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 benchmarks hospital supply chain efficiency against a CMS peer cohort, with a specific resource (hospital supply chain) and metrics (supply cost as percentage of operating expense at p25/p50/p75). It distinguishes from siblings like get_cms_facility_benchmark by focusing on supply chain benchmarks specifically.

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 benchmarking hospital supply chain efficiency against CMS peer cohort or building a materials management cost reduction case,' giving clear context for when to use the tool. It does not name alternative tools or exclusions, but the usage guidance is specific and practical.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering safety. The description adds useful behavioral context: the data source (ProPublica Nonprofit Explorer) and the post-quantum signed settlement receipt with a verification URL, which informs the agent about the nature of the output and its trust features.

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 with a logical order: data fields, source, use case, and verification. Each sentence contributes relevant information, though the attestation sentence carries a marketing tone that might be less essential for selecting 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?

Given no output schema, the description compensates by enumerating the return fields (total revenue, expenses, net assets, etc.). It also covers source and verification, making the tool's purpose and output clear. The single-parameter design keeps it complete enough for an agent to invoke appropriately.

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 org_name parameter beyond implying it is the nonprofit's name. It fails to clarify expected format (e.g., legal name, EIN). With only one parameter, the agent would benefit from explicit guidance, but none is provided.

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

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

The description states exactly what the tool does: 'IRS Form 990 nonprofit financial data' and enumerates specific fields like total revenue, expenses, net assets, program expense ratio, executive compensation, revenue trend, and financial health signal. This clearly distinguishes it from sibling tools focused on other healthcare benchmarks or drug 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?

The description gives explicit context for when to use the tool, stating it is 'Essential for evaluating nonprofit health systems, universities, and foundations.' It does not name alternative tools or exclusions, but the use case is clearly scoped to nonprofit financial evaluation.

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 readOnlyHint=true and destructiveHint=false (safe read), but description adds valuable behavioral context: returns restatement_detected on republish, HTTP 503 no-charge behavior on upstream unavailability, the SLA cost, and data_sources[] provenance. This goes beyond annotations and is specific and useful.

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 dense but well-structured: use case first, then return fields, an example, source, SLA, error behavior, and data provenance. Each sentence adds value, though the pipe-separated SLA section is a bit of a stylistic shift. It's longer than most but every part earns its place.

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

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

For a simple one-parameter read tool with no output schema, the description covers almost everything: the exact return fields, an illustrative example with values and dates, the data source, cost model, error behavior, and provenance. It's fully adequate for an agent to invoke and understand the result.

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%, so description must compensate. The parameter name 'ndc_or_name' suggests it accepts an NDC or name, and the example shows an NDC. However, the description doesn't clarify the 'name' form, validation rules, or output format for the parameter beyond that single example. It's minimally adequate but not fully compensating.

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 the tool returns CMS NADAC drug acquisition cost benchmarks, lists specific fields (NADAC per unit, effective_date, etc.), and provides a concrete example. This distinguishes it from siblings like get_pharmacy_spend_benchmark or get_gpo_contract_benchmark by naming the exact resource (NADAC) and 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?

Explicitly states when to use: for pharmacy buyers, 340B program managers, or CFO agents needing CMS NADAC benchmarks for contract negotiation or payer comparison. This gives real context but doesn't explicitly mention alternatives or when not to use, just a clear use case.

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 description does not need to cover safety. It adds valuable context: it performs a 'live NPPES lookup,' cites the source (CMS NPPES Registry), and discloses that results are cryptographically attested with a post-quantum signed settlement receipt. 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?

Three sentences, each earning its place: the first states purpose and timing, the second lists return fields and source, the third adds trusted attestation details. No redundancy or filler; the information is front-loaded and scannable.

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 should explain return values; it partially does by listing enumeration status, taxonomy, license state, and identity fields. However, it omits parameter semantics, response format, and whether results are single or plural. For a tool with three optional parameters and no output schema, this leaves noticeable gaps.

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 should compensate by explaining parameter roles. It implies 'npi' is the identifier but does not clarify the purpose or usage of 'state' or 'provider_name.' The description mentions return fields but not how parameters influence the lookup, leaving significant ambiguity for a schema with no required parameters.

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 'Use when verifying a clinician or organization NPI against CMS NPPES,' naming a specific verb (verify), resource (NPPES), and use context (contracting/credentialing). It also lists return fields (enumeration status, taxonomy, license state, identity fields), distinguishing it from sibling tools focused on benchmarks or recalls.

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 ('before contracting or credentialing') and identifies the data source (CMS NPPES Registry). It does not explicitly name alternatives or when-not-to-use conditions, but the use case is clear and distinct from sibling tools, which are all get_* tools for other data domains.

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

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

While annotations already declare readOnlyHint=true and destructiveHint=false, the description adds meaningful behavioral context: it specifies the source (OpenFDA), the inclusion of a cryptographic attestation with a post-quantum signed receipt, and a verification URL. This goes beyond the annotations and informs the user about the response structure's unusual feature, which is valuable.

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, well-structured sentence that front-loads the main output, then adds source and attestation details. It is concise and every clause adds information, though the attestation detail could be considered tangential for some users.

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

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

Given the lack of an output schema, the description effectively lists the return components (reports, serious events, deaths, hospitalizations, reactions, signal level). It also mentions the source and verification method. However, it doesn't clarify the input format for drug_name or handle potential edge cases, leaving minor gaps.

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 only mentions 'for a drug' without specifying format (generic vs brand name), case sensitivity, or example values. The parameter name drug_name is self-explanatory but lacks the detail needed for correct invocation.

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 fetches FDA FAERS adverse event data for a drug and enumerates the specific outputs (total reports, serious events, deaths, hospitalizations, top reactions with percentages, signal level). This is a specific verb (get) with a specific resource (OpenFDA adverse events) and distinguishes itself from the sibling get_drug_adverse_events by mentioning the signal level and attestation.

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 no guidance on when to use this tool versus alternatives like get_drug_adverse_events. It doesn't mention use cases, prerequisites, or scenarios where this tool is preferred. The presence of a similarly named sibling makes this omission more significant.

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 behavior. The description adds valuable context by detailing the exact data points returned (denial rates, prior auth burden, payer mix commentary), providing an illustrative example with real numbers, and citing the data source. This goes beyond the annotation's simple 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?

The description is three sentences, front-loaded with use cases, and each sentence earns its place: purpose, output summary, and a concrete example with source attribution. No word is wasted, and the information density is high.

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 only two optional parameters and no output schema, the description provides sufficient detail about the expected content and source. It tells the agent what kind of data to expect and gives a realistic example. It does not describe potential response format or edge cases, but for a read-only intelligence tool with optional filters, the coverage is adequate.

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 parameter descriptions and no enums, so the description carries the burden. It implies that 'payer_name' filters by payer and 'specialty' filters by specialty, but the mapping is implicit rather than explicit. The example includes payer names but does not clearly explain how each parameter affects the output, leaving some ambiguity for an agent deciding whether to pass optional arguments.

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's purpose: benchmarking payer performance, denial management, and board reporting. It names specific outputs (denial rates by payer, prior authorization burden by specialty, payer mix commentary) and is clearly distinct from sibling benchmark tools focused on facilities, drugs, or providers.

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 benchmarking payer performance, building a denial management strategy, or preparing revenue cycle board reporting,' which gives clear, concrete scenarios for when to invoke this tool. It does not explicitly name alternatives, but the use-case framing is sufficient to guide selection among the many 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 declare readOnlyHint=true and destructiveHint=false, covering safety. The description adds behavior beyond annotations by noting it's a 'static model derived from published 340B savings ranges,' and the example illustrates the nature of outputs (e.g., $287/adjusted patient day). This adds context about data origin and output style, earning a 4.

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

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

The description is three sentences: usage, returns, and an illustrative example plus source. Each sentence carries useful information, but the example is somewhat lengthy and could be trimmed. Still, it's well-structured and front-loaded with the use case.

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 usage, outputs, an example, and data source, but it lacks parameter explanations and output structure. Given no output schema and 5 undocumented parameters, the description is not fully complete, though it provides a workable understanding for a read-only benchmarking 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 0% description coverage, so the description must compensate, but it does not describe any of the five parameters (state, bed_size, etc.). It mentions 'by bed size' only as an output dimension, not how the input is used. This leaves parameter semantics completely unexplained, so a low score is warranted.

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 drug cost per adjusted patient day, 340B savings opportunity from published savings ranges, specialty drug drivers, and GPO targets by bed size.' This specific verb+resource distinguishes it from sibling benchmark tools by focusing on pharmacy spend, and the context 'benchmarking hospital pharmacy costs' clarifies its niche.

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 an explicit use case: 'Use when benchmarking hospital pharmacy costs or building a pharmacy cost reduction strategy for a board presentation.' While it doesn't mention alternatives, it clearly establishes when to use the tool, achieving 'clear context, no exclusions' per the rubric.

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 known. The description adds the BLS OES 2024 data source and return metric, but does not disclose behavior for omitted state or other edge cases. 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 dense paragraph with the use case and return type front-loaded. The example adds useful context but increases length. All sentences are relevant, though the retention-risk insight could have been omitted without losing core 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 two parameters and no output schema, the description covers purpose, return metric, data source, and gives a concrete example. Gaps remain around parameter format and state-omission behavior, but the example partially compensates. The low complexity and available annotations make this reasonably complete.

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%, so the description must compensate. It names 'specialty and state' as the filters, and the example provides sample values. However, it does not clarify accepted formats (e.g., full state names vs. codes), that state is optional, or how specialty titles are normalized.

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

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

The description clearly states it 'Returns median total compensation by specialty and state from BLS OES 2024 data' and provides explicit use cases for employment agreements, recruitment, and compensation reports. The example with Illinois cardiologist illustrates the specific metric, distinguishing it from sibling benchmark 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?

Explicit 'Use when' clause names three scenarios, giving clear context for when to invoke this tool. However, it does not mention when not to use it or directly name alternative tools, so it stops short of fully differentiating from 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?

The description adds context beyond the readOnlyHint/destructiveHint annotations by revealing the data source (CMS NPI Registry synced data) and the nature of the output (counts and market structure). It does not contradict annotations, and the example provides concrete insight into the output format and interpretation. No annotation contradiction.

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, front-loaded with use cases, and every sentence adds value: the use context, the data returned, and a concrete example with interpretative guidance. No fluff or repetition.

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 is complete enough for a simple query tool with two required parameters. It explains the data source, the return type, and provides an example that illustrates the output and its business interpretation. There is no output schema, so the example helps fill that gap, though it doesn't detail edge cases or error conditions.

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 explains the 'specialty' and 'state' parameters by noting the output is 'by specialty and state' and gives an example ('Illinois cardiology'). However, it does not mention the optional 'city' parameter at all, leaving its semantics unspecified. The description partially 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 the tool's function: 'Returns NPI registry physician counts and market structure by specialty and state.' It provides a specific verb ('Returns'), a specific resource (NPI registry physician counts), and scope (by specialty and state). This distinguishes it from siblings like get_npi_provider_verification (individual verification) and get_physician_group_benchmark (group-level benchmarking).

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 when to use the tool: 'assessing physician supply in a market, evaluating a healthcare network expansion, or benchmarking provider density for population health strategy.' It gives clear context but does not explicitly mention when not to use it or name alternative tools, so it's not a full replacement for sibling differentiation.

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 read-only and non-destructive, lowering the bar. The description adds valuable context: the data source ('Stratalize SIA 2024-style composite'), example output values, and the note about specialty premiums. It does not contradict annotations and provides more than just a safe-read indication.

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, then states the return value, provides a concrete example, and cites the source. Every sentence serves a purpose, and the example is illustrative without being verbose.

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 explains the core return (median markup, low/high band) and gives example values. It also covers source and use context. Minor gap: it doesn't describe the exact response structure or edge cases (e.g., what happens if no filters are provided), but it is sufficient for a read-only analytics tool with simple parameters.

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 indirectly ('by agency and specialty type') and gives concrete examples (AMN Healthcare, ICU), making it clear these are filters. However, it doesn't specify whether parameters are optional or how to format values, leaving some ambiguity.

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 median markup percentage with low/high bands by agency and specialty type, with a specific use case (evaluating staffing agency pricing or negotiating travel nurse/locum contracts). It distinguishes itself from siblings like get_travel_nurse_rate_benchmark by focusing on markup rather than rates.

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

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

The description opens with 'Use when evaluating staffing agency pricing or negotiating a travel nurse or locum contract,' providing explicit context for when to invoke the tool. It does not explicitly mention when not to use it or name alternative tools, but the use case is clearly differentiated 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: access methods (public MCP, x402 micropayments with specific pricing tiers, and OAuth requirement for certain features). This goes beyond annotations and helps the agent understand auth/coverage constraints. However, it doesn't describe return format or pagination, but for a catalog tool that's less critical.

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-dense but efficient, front-loaded with 'START HERE' and each sentence adds value: scope, access methods, pricing, and usage guidance. No filler.

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

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

For a zero-parameter discovery tool, the description is complete: it covers purpose, scope, access options, pricing, auth requirements, and how to use it. The sibling list further contextualizes it as an entry point.

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 default baseline is 4. The description adds no parameter-specific detail, but none is needed; the schema is empty and fully consistent.

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 'Returns the complete Stratalize tool catalog' with a specific verb and resource, and distinguishes itself from sibling tools by marking 'START HERE' and 'Call this first' — this is a discovery/overview tool, not a data lookup tool.

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 instructs 'Call this first to discover tools by role or vertical' and 'START HERE', giving clear when-to-use guidance. Also provides access context (public vs x402 vs OAuth) that helps agents decide if this tool is suitable.

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

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

With readOnlyHint=true and destructiveHint=false already provided, the description adds valuable behavioral context: it specifies the output (medians and bands), includes a concrete example (ICU median $95/hr, p75 $108/hr), and mentions the data source (BLS and Stratalize SIA-style composite). 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 concise, front-loaded with the usage guidance, and packs useful details into a compact format. The example and source attribution add value without unnecessary filler, though the long dash-separated sentence could be slightly easier to parse.

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 annotations and two parameters, the description covers the key elements: when to use, what it returns, an example, and the data source. Since there is no output schema, the description's mention of 'medians and bands' gives sufficient context, though it doesn't detail the exact response structure.

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

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

The input schema has no parameter descriptions (0% coverage), but the description partially compensates by mentioning 'by specialty and state' and providing an example using 'ICU' and 'Illinois'. However, it doesn't clarify exact input formats (e.g., state code vs full name), leaving some ambiguity for the agent.

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

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

The description clearly states the tool's function: 'Returns bill-rate medians and bands by specialty and state' and specifically for travel nurse contract benchmarking. It uses a specific verb ('benchmarking') and resource (travel nurse rates), and distinguishes itself from sibling tools by its focus on travel nurse rates and negotiation use case.

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 usage guidance: 'Use when benchmarking travel nurse contract rates or negotiating with a staffing agency.' It provides clear context for when to apply the tool, though it doesn't explicitly state when not to use it or mention 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. The description adds valuable context beyond this: the specific metrics returned, an illustrative example with concrete values, and the data source (CMS VBC program data composite). It does not contradict annotations.

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

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

The description is three sentences long, front-loaded with the primary use case, followed by the output specifics and a concrete example. Every sentence adds value, and the example is illustrative without being redundant.

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 gives some sense of return values (savings rates, episode costs, quality medians) and an example, but it lacks details on how the optional parameters filter the results or affect the output. For a tool with four parameters and no output schema, this is a notable 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?

Schema description coverage is only 25% (only one parameter documented). The description does not explain how parameters like bed_size, specialty, or program_type affect the output. It only implies program_type via the mentioned programs (MSSP, BPCI, MIPS), but does not clarify how to use the parameters to shape the query.

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 VBC performance metrics (MSSP ACO savings rates, BPCI episode costs, MIPS quality signal medians) with a specific verb ('Returns') and resource ('VBC contract performance'). It distinguishes from sibling tools by focusing on value-based care performance.

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 use cases: benchmarking VBC contract performance, assessing FFS-to-VBC transition readiness, and preparing population health strategy presentations. It does not mention alternatives or exclusions, but the stated scenarios are clear and distinct from sibling tools.

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