Healthparse Healthcare Data Gateway

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

No annotations are provided, so the description must disclose behavior. It mentions the data source and a price of $0.01/call, but does not specify if the call is read-only, whether it modifies data, or any other side effects. The description is partially transparent but lacks explicit behavioral details.

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 informative sentence followed by a brief price note. It is concise, front-loaded, and contains no 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?

For a simple single-parameter tool with no output schema, the description provides sufficient context (source, price). It lacks details about the response format or potential errors, but given the tool's simplicity, it is fairly 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 100% coverage for the single parameter (ccn), so the description does not need to add much. The description does not clarify the parameter beyond what the schema provides, and no additional semantics are given.

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 quality ratings and performance measures for a dialysis facility by CCN, citing CMS Care Compare. It is distinct from sibling tools like 'carecompare_dialysis_search' which is a search 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?

The description implies using this tool when you have a specific CCN for a dialysis facility, but it does not explicitly compare with alternatives or provide when-not-to-use guidance. No mention of prerequisites or fallback 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?

No annotations are provided, so the description carries the full burden. It mentions data source and refresh frequency but lacks details on pagination, sorting, rate limits, or any constraints. The read-only nature is implied but not confirmed.

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

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

The description is two sentences plus a source/price note, with no redundant words. It is front-loaded with the tool's 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?

Without an output schema, the description should hint at return fields. It answers the question of which facilities meet thresholds, implying quality metrics and names, but does not specify columns or data structure. Adequate but not thorough.

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%, meaning no parameter descriptions. The description explains four of seven parameters (city, state, zip_prefix, rating_min/max) but omits limit and offset, and does not clarify how rating range works or default values.

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

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

The description clearly states the verb 'Search' and resource 'dialysis quality data', specifies search criteria (state, city, ZIP, star rating), and answers a concrete question. It is well-differentiated from sibling carecompare search tools for other provider types.

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

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

The description provides clear context (search by location/rating to find quality thresholds) but does not explicitly mention when to avoid using it or compare with sibling carecompare_dialysis_byCcn for CCN lookups. Still, the purpose is well-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?

No annotations are provided, and the description does not disclose any behavioral traits such as read-only nature, auth requirements, rate limits, or side effects. It implies a read operation but fails to explicitly state so, leaving a significant gap for the agent.

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

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

The description is concise with two sentences plus source and price. It is front-loaded with the core purpose. However, the price tag, while potentially useful, is arguably extraneous and slightly reduces conciseness.

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

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

Given no output schema, the description explains the tool returns 'quality ratings and performance measures', which gives a general idea but lacks detail on structure. For a simple parameter set, it is adequate but not comprehensive.

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 100% coverage, describing the single parameter 'ccn' as 'Path parameter: CMS Certification Number'. The description adds no additional meaning beyond reiterating 'by CCN', so it meets the baseline without exceeding it.

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

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

The description clearly states it retrieves CMS Care Compare quality record by CCN for home health, answering quality ratings and performance measures. It distinguishes from sibling tools by specifying 'home health' and 'by CCN', implying others are for different facility types or endpoints.

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 implicitly suggests using this tool when you have a CCN for a home health facility, but does not explicitly state when to use it versus alternatives like carecompare_home-health_search or other facility-specific tools. No exclusions or when-not guidance provided.

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

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

No annotations exist, so the description carries the burden. It discloses monthly data refresh and cost ($0.02/call), but does not mention any constraints like rate limits, pagination behavior, or response format. This is adequate but not rich.

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

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

The description is short and front-loaded with the tool's purpose. Every sentence adds value (purpose, question, source, pricing). Could be slightly improved but is efficient.

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

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

Given 7 parameters, no output schema, and no annotations, the description provides a basic understanding of input criteria but lacks details on output structure, pagination, or filtering behavior. Adequate for a simple search tool but incomplete.

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

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

The description lists searchable fields (state, city, ZIP, rating) but does not explain each parameter in detail (e.g., zip_prefix vs full ZIP, rating_min/max semantics, limit/offset usage). With 0% schema description coverage, the description adds minimal value beyond naming the 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 clearly states it searches home health quality data by state, city, ZIP, or CMS star rating, and answers a specific market question ('which home health meet quality thresholds?'). This distinguishes it from sibling tools like byCcn lookups and other specialized searches.

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

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

The description implies usage for location or rating-based searches but does not explicitly say when to use this tool over alternatives (e.g., byCcn for direct lookup). No when-not-to-use guidance or exclusions are provided.

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

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

No annotations are provided, and the description only adds source and cost info. It does not disclose rate limits, authentication needs, error handling, or output format, leaving a significant gap in behavioral 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 extremely concise: one sentence, a clarifying question, and source/pricing. It is front-loaded with purpose and contains no unnecessary words.

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

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

For a simple single-parameter query tool with no output schema, the description is adequate but incomplete. It fails to describe the return format, potential errors, or data freshness, which would aid an agent.

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

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

Schema description coverage is 100% (one parameter fully described). The description adds no new meaning beyond 'by CCN' and 'Path parameter: CMS Certification Number', which is already in the schema.

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

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

The description clearly states the tool retrieves a CMS Care Compare quality record for a hospice by CCN, and explicitly answers what quality ratings and performance measures are. It distinguishes from sibling search tools by specificity of a single facility by CCN.

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

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

The description implies the tool is used when you have a CCN and need quality data for a hospice, contrasting with search siblings. However, it lacks explicit when-not or alternative 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?

The description notes the data source, refresh frequency, and cost, which are useful. However, it lacks details on pagination, authentication, rate limits, or how parameters combine. With no annotations, more behavioral context would be beneficial.

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 extremely concise: three sentences front-loading the core action, source, and cost. No fluff, every sentence provides value.

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

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

Given 7 optional parameters and no output schema, the description should cover pagination (limit/offset) and combination logic. It mentions only a subset of parameters, leaving critical usage details unexplained.

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

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

The description maps to 4 of 7 parameters (state, city, zip, rating), adding meaning beyond the schema. However, it omits limit and offset, and does not explain parameter interaction. Schema coverage is 0%, so description partially compensates.

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 searches hospice quality data by location or star rating, and it answers a specific business question. It implicitly differentiates from sibling tools like carecompare_hospice_byCcn which is for lookup by CCN.

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 context (search by location/rating) but does not explicitly state when to use this tool versus alternatives like the CCN lookup. No exclusion criteria or prerequisites are mentioned.

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

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

No annotations are provided, so the description must cover behavioral aspects. It states the tool returns quality records, ratings, and performance measures, implying read-only behavior. It transparently mentions the price ($0.01/call). However, it does not disclose error handling, rate limits, or what happens if the CCN is invalid. The description is adequate for a simple read tool but lacks depth.

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, consisting of one sentence and two lines for source and price. It is front-loaded with the core purpose. The price note is relevant but could be integrated more seamlessly. Every sentence serves a purpose, though the sentence could be more structured (e.g., 'Retrieves quality ratings and performance measures for a hospital by its CMS Certification Number').

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

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

Given the tool's simplicity (one parameter, no output schema, no nested objects), the description adequately covers the main purpose and the nature of the response (quality ratings and performance measures). It also notes the data source and cost. While it could detail the specific measures returned, the lack of an output schema reduces the burden. The description is sufficient for an AI agent to select and invoke this tool.

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

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

The schema has 100% coverage with a clear description for the single parameter 'ccn' ('Path parameter: CMS Certification Number'). The description adds context by stating the tool is 'by CCN' and explains the output, but does not provide additional semantics like format, length, or examples. Baseline 3 is appropriate as the schema already documents the parameter effectively.

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 quality ratings and performance measures for a hospital given its CCN. The verb is implied, and the resource is specific ('CMS Care Compare quality record'). It distinguishes from sibling tools like 'hospitals_search' by specifying it is by CCN. However, it could be more explicit about the action (e.g., 'get' or 'retrieve').

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

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

The description implies use when a CCN is available, but does not explicitly state when to use this tool versus alternatives like 'carecompare_hospitals_search' or other facility types. No exclusions or alternative recommendations are provided, relying on the user to infer from the tool naming.

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

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

No annotations are provided, so the description carries full burden. It mentions data source and refresh frequency but does not disclose behavioral traits like read-only nature, authentication needs, rate limits, or any side effects. For a search tool, the lack of safety/permissions context is a significant gap.

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

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

Two sentences plus a price note, front-loaded with purpose and supported with a concrete question. Every element earns its place with zero waste.

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

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

The tool has 7 parameters with no schema descriptions and no output schema. The description does not explain return values, pagination, or result format. Given moderate complexity, the description leaves significant gaps for an agent to correctly use the tool.

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

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

Schema description coverage is 0%, yet the description only vaguely mentions state, city, ZIP, and 'star rating' filters without explaining any parameter semantics (e.g., limit/offset pagination, rating min/max meaning). It adds marginal value over the parameter names but insufficiently compensates for the missing schema descriptions.

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

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

The description clearly states the verb 'Search' and the resource 'hospitals quality data' with specific filtering dimensions (state, city, ZIP, CMS star rating). It answers a concrete user question and distinguishes from sibling tools like carecompare_hospitals_byCcn by emphasizing search across location and rating criteria.

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 a clear usage context ('which hospitals meet quality thresholds in a market?') but does not explicitly exclude cases like looking up a specific hospital by CCN or mention alternative tools. The omission of when-not-to-use costs a point.

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

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

No annotations are provided, so the description carries the full burden. It mentions the data source (CMS Care Compare) and cost, but fails to disclose behavioral traits such as rate limits, error handling for invalid CCNs, or whether the operation is read-only. Minimal beyond purpose.

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

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

The description is concise with one main sentence, a clarifying question, and a cost note. It is front-loaded with the core purpose. Minor improvement could separate metadata (cost) from the functional description.

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 lookup tool with no output schema, the description adequately explains what the tool returns (quality ratings and performance measures). It is sufficiently complete for its simplicity, though it could mention output format.

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

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

With 100% schema description coverage, the description adds context by clarifying that ccn is a 'CMS Certification Number,' which is already stated in the schema. The added value is marginal; it confirms the purpose but does not provide format or constraints beyond the schema.

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

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

The description clearly states the tool retrieves a CMS Care Compare quality record for a SNF by CCN, answering specific questions about quality ratings and performance measures. It distinguishes itself from sibling search tools and other provider-type 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 implies use when a CCN is known and quality data is desired, but lacks explicit guidance on when to use alternatives (e.g., carecompare_snf_search). No when-not-to-use or prerequisite information is provided.

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

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

No annotations are provided, so the description carries the full burden. It mentions data source and price but does not disclose behavioral traits such as whether the tool is read-only, authentication requirements, rate limits, or pagination behavior. For a search tool, this is insufficient.

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

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

The description is two sentences, both front-loaded with core purpose. The second sentence adds context on source and pricing without extra fluff. However, it could be better structured with parameter details.

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

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

Given no output schema and 7 parameters, the description lacks information on return fields, pagination, or behavior when no results are found. It does not address how filters interact (AND vs OR). The context of sibling tools is provided but not leveraged to guide usage.

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%, yet the description only groups parameters by category (state, city, ZIP, star rating) without explaining individual parameters like limit, offset, or how multiple parameters combine. The description adds minimal meaning beyond the schema itself.

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 searches for skilled nursing facility (SNF) quality data by state, city, ZIP, or CMS star rating, distinguishing it from sibling tools like carecompare_snf_byCcn which searches by CCN. The verb 'Search' and resource 'snf quality data' are specific.

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 answers the question 'which snf meet quality thresholds in a market?' indicating when to use it. However, it does not explicitly state when not to use it or mention alternative tools like carecompare_snf_byCcn for specific facility lookups. The source and refresh note provide useful context but do not fully guide on 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?

No annotations are provided, and the description does not disclose behavioral traits beyond 'search'. It lacks information on read-only nature, destructive potential, rate limits, or return format. For a tool with no annotations, this is insufficient.

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 extremely concise with two sentences plus a price tag. It front-loads the purpose and adds relevant context like data source and cost. Every word adds value.

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

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

For a search tool with 5 parameters and no output schema, the description should explain return values, pagination, or limits. It provides source and price but omits behavioral and output details, making it only somewhat 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%, but the description adds meaning for three parameters: 'state', 'CCN', and 'name' (mapped to 'name_search'). However, 'limit' and 'offset' are not mentioned, leaving gaps. The description partially compensates for the schema's lack of description.

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 searches ambulatory surgical centers (ASCs) by state, CCN, or name. The resource (ASCs) and verb (search) are specific, and the scope is distinct from sibling tools like hcris_hospital_search.

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

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

The description implies usage by answering 'which ASCs are in a given market?' but does not provide explicit when-not-to-use or alternative tools. Context from sibling tools helps, but direct exclusion guidelines are missing.

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 no annotations, the description fully carries behavioral disclosure. It details the output (identity, beds, discharges, revenues, net income, operating margin across fiscal years), giving a clear picture of what the tool does. However, it does not specify read-only nature, but for a data retrieval tool this is acceptable.

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

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

Two concise sentences plus a price tag. Every word serves a purpose: identifies the record type, lists key output fields, and communicates cost. No 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?

For a simple single-parameter tool with no output schema, the description sufficiently explains what the tool returns. It mentions 'across fiscal years' implying time series, but lacks details on number of years or response format. Nonetheless, it is complete enough for an agent to decide.

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

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

Schema coverage is 100% for the single parameter 'ccn', which is described. The tool description adds context that the CCN identifies a hospital cost-report record, but does not add new meaning beyond the schema's 'CMS Certification Number'. Baseline score of 3 is appropriate.

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

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

The description clearly states it returns a hospital cost-report record identified by CCN, listing specific metrics (beds, discharges, revenues, etc.). It distinguishes from siblings by emphasizing the CCN identifier, though it could be more explicit among similar hospital tools like hcris_hospital_financials.

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

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

Implies use when CCN is known, but no explicit when-not or alternative tools mentioned. Sibling tools like hcris_hospital_search exist for name-based lookup, but the description does not guide selection.

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

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

No annotations are provided, so the description carries the full burden. It discloses the data source (CMS cost reports), maximum years (15), and price ($0.05/call), but does not mention pagination, rate limits, or authentication requirements.

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 extremely concise at one sentence, with additional source and price info. Key information is front-loaded, and every element serves a purpose.

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

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

Given the single parameter and no output schema, the description adequately covers what data is returned, source, and cost. It could mention format or pagination, but is largely complete for its complexity.

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

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

Schema description coverage is 100% for the single parameter ccn, which already describes it as 'Path parameter: CMS Certification Number'. The description only mentions 'by CCN' without adding new meaning, so baseline 3 is appropriate.

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

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

The description clearly states the tool returns multi-year financial history for one hospital by CCN, listing specific data fields (beds, discharges, revenues, net income) and a limit of 15 years. This directly distinguishes it from sibling tools like hcris_hospital_byCcn which likely provide different 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 implies use when needing financial history for a specific hospital via CCN, but lacks explicit when-not or alternatives. However, the specificity (financial vs general hospital info) provides sufficient 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?

No annotations are provided, so the description carries full burden. It only discloses the source and pricing, but does not mention pagination behavior (though limit/offset are in schema), rate limits, or that this is a read-only operation. Minimal behavioral context beyond the schema.

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

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

Two sentences plus a source and price note. Front-loaded with the purpose. No redundant information. Every sentence adds value.

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

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

Given 17 optional parameters and no output schema, the description covers the core use case but omits details like default limit (25), how to combine parameters, and the structure of results. Adequate but not fully complete for a search tool of this complexity.

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

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

With 0% schema description coverage, the description must compensate. It lists key filters (state, city, type, control, beds, discharges, revenues, operating margin) which adds semantic context, but does not explain parameter formats (e.g., state as two-letter code) or constraints beyond what's in the schema. Partial compensation.

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 'Search US hospitals by...' with specific filter dimensions and answers a concrete question: 'which hospitals in a market match a financial or operational profile?' This distinguishes it from sibling tools like hcris_hospital_byCcn (search by CCN) and carecompare 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 implies usage for hospital search by profile and mentions the data source (CMS cost reports), but does not explicitly state when to use this tool over siblings, such as when to use hcris_hospital_financials instead, or provide exclusion criteria.

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

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

With no annotations provided, the description must convey behavioral traits. It mentions the source and price, and correctly implies a read-only search. However, it omits details about pagination (offset/limit), ordering, error behavior, or what happens with no results. The price note adds some 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 extremely concise (two sentences plus metadata), with key information front-loaded ('Search Medicare providers...'). Every element adds value—verb, criteria, purpose, data source, and pricing. No wasted words.

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

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

Given the tool has 11 parameters and no output schema, the description is insufficient. It does not explain return format (what fields are returned for providers?), how offset/limit affect results, or how the min/max range parameters interact. The source and price are helpful, but the description does not cover the full complexity of a search tool with extensive filters.

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 criteria groups (NPI, state, specialty, volume, beneficiaries, payments) but does not explain the min/max variants for numeric fields or the behavior of limit/offset. The mapping to schema parameters is partial and lacks detail, leaving significant ambiguity for an 11-parameter tool.

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

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

The description uses a specific verb ('Search Medicare providers') and lists multiple criteria (NPI, state, specialty, service volume, beneficiaries, payments), clearly distinguishing it from sibling search tools like hcris_hospital_search or nppes_enumeration_search. It also specifies the source (CMS Medicare claims), which adds context.

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

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

The description implies usage for finding providers matching certain criteria but does not explicitly state when to use this tool versus alternatives (e.g., nppes_enumeration_search for NPI lookups, or other provider-specific searches). No exclusions or comparative guidance are provided.

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

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

With no annotations provided, the description carries full burden. It explains the tool is an 'observational marker' of a 'reported step-up', discloses the cost ($0.1/call), and notes pagination (keyset cursor). No destructive or hidden behaviors are omitted, though response format is not detailed.

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

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

The description is a single paragraph of about 5 sentences, efficiently front-loading the key purpose. Almost every sentence contributes value (data source, thresholds, question, filters, pagination, price). A slight condensing could improve, but it's already concise.

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

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

Given the tool has 5 parameters, no output schema, and no annotations, the description covers the input semantics and data source thoroughly. It lacks explanation of the output structure or examples, but for a non-critical tool this is 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?

The description adds significant meaning beyond the input schema: it explains the conceptual thresholds ($1M, 2x prior year), defines 'min_amount' as movable-equipment dollars, and clarifies 'cursor' as keyset pagination. Schema coverage is 80%, but the description contextualizes the parameters effectively.

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 provides 'Hospital capital-expenditure jump signals' and answers 'which hospitals just bought equipment?', specifying the data source (CMS cost reports) and threshold ($1M and 2x prior year). This distinguishes it from all siblings, which focus on other healthcare facilities or data types.

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 lists filtering options (state, min_amount, since) and pagination, providing clear context for use. However, it does not explicitly state when to avoid using this tool or mention alternatives, though sibling tools are sufficiently different to imply appropriate usage.

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 no annotations, the description carries full burden. It discloses that the response shape varies (flat row vs. stats_by_type array) and how to differentiate them, plus the cost per call. No destructive or authentication information is needed given the tool's read-only nature.

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 verbose paragraph. It includes rhetorical questions ('Answers: what is a fair price?') which add clarity but could be trimmed. It is still effective but not maximally concise.

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 lacks an output schema, so the description must explain the return format. It does so by describing two possible response shapes. It also covers the data source and cost. Given the complexity, this is sufficient.

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

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

Schema coverage is 100% for the single parameter. The description adds context by naming the billing code types (CPT/HCPCS/MS-DRG) and implying the parameter is a specific billing code, which goes slightly beyond the schema's generic 'billing code' description.

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 specifies the tool retrieves national price statistics for a billing code from hospital price-transparency files, with a distinct scope (median and spread of payer-negotiated rates). It differentiates from siblings like hpt_rates_search by focusing on a single code's stats.

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 explains when to use the tool (to determine a fair price for a procedure) and provides guidance on interpreting results (checking for stats_by_type for ambiguous codes). It does not explicitly mention when-not-to-use or alternatives, but the context is clear.

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

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

No annotations are provided, so the description must carry the full burden. It mentions a cost ($0.03/call) but does not disclose other behavioral traits such as read-only nature, side effects, rate limits, or required permissions. This is insufficient for a search 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 two sentences long, immediately stating the core purpose and parameters, then providing a typical use case, source, and cost. It is front-loaded and free of 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?

With 8 parameters, no output schema, and no annotations, the description is incomplete. It explains the general purpose but not parameter details, output structure, or usage constraints. Important aspects like pagination, rate limits, or error handling are absent.

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

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

Schema description coverage is 0%. The description lists some parameters (billing code, state, payer, hospital CCN, rate type) but does not explain their usage, relationships (e.g., billing_code and billing_code_type), or constraints. Limit and offset are omitted. The description adds minimal value over the schema.

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

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

The description clearly states the verb (search) and resource (hospital price-transparency negotiated rates) and lists parameters. It answers a specific question. However, it does not explicitly distinguish from the sibling tool hpt_code_stats, which may be similar but for statistics.

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 a use case query ('what do payers actually pay?') and indicates the data source. But it lacks explicit guidance on when to use this tool vs. alternatives, or when not to use it. No exclusions or prerequisites are mentioned.

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

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

No annotations provided, so description carries full burden. It mentions price ($0.03/call) and implies read-only search, but does not disclose pagination behavior, rate limits, or authentication needs. The description adds some context but is missing behavioral details beyond what is inferred.

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 plus a price note—concise and front-loaded. Every sentence adds value: purpose, use case, filters, and pricing. No wasted words.

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

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

Given 8 parameters and no output schema, the description is adequate but incomplete. It explains the tool's purpose and filters but does not describe the return format, pagination details, or error behavior. It covers the main context but leaves 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 75% (6 of 8 params have descriptions). The description adds contextual meaning, e.g., 'has_npi' for joining provider graph, but does not cover limit and offset. It provides some added value but does not fully compensate for the uncovered 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 clearly states it searches HRSA 340B OPAIS covered entities and child sites by state, entity type, or name. It distinguishes from siblings by specifying '340B' and mentioning entity type codes like FQHC/DSH/CAH. The purpose is precise and unambiguous.

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

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

The description answers the use case: which 340B-eligible entities operate in a territory and which have NPIs. It lists filters but does not explicitly contrast with sibling tools or state when not to use. The context is clear but lacks exclusion 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?

With no annotations, the description carries the burden of behavioral disclosure. It states that each site is entity-resolved into NPPES (matched_npi/match_method), mentions the added_since parameter as a new-site trigger, and includes the price per call. It does not explicitly confirm read-only status, but the search nature and pricing imply no destructive effects.

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

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

The description is two sentences plus a list, covering purpose, entity resolution, use cases, filters, and pricing. It is information-dense but not overly long; every sentence earns its place. However, the list of filters could be integrated more concisely.

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 10 parameters, no output schema, and no annotations, the description is fairly complete. It explains the core functionality, entity resolution, and available filters. However, it omits pagination details and what fields are returned in the response, which would improve completeness.

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 80%, so baseline is 3. The description adds semantic context for added_since ('new-site trigger') and mentions several filters by name, but does not add much beyond what the schema already describes for standard parameters like sort, limit, and offset.

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 discovers FQHC/Look-Alike health-center sites from the HRSA roster, and lists specific filter criteria (state, type, county, name, added_since). It distinguishes itself from sibling tools like hrsa_coveredEntities340b_search and hrsa_hpsa_search by focusing on health center sites and entity resolution.

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

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

The description provides clear context for when to use the tool: for pharma-sales, medical-device, and staffing targeting involving FQHCs. It lists concrete filters but does not explicitly mention when not to use this tool or suggest 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?

No annotations are provided, so the description carries the full burden. It discloses the default status filter ('Designated'), the sort fields, and includes a price note. However, it does not mention pagination behavior (despite offset/limit in schema), rate limits, authentication requirements, or error handling. The description adds some context (e.g., 'mapped staffing-demand signal') but lacks comprehensive behavioral disclosure for a tool with no 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 that front-loads the purpose and key use case. Every sentence adds value: purpose, answers, filters, sort, default behavior, and price. There is no fluff. However, the price note could be considered extraneous for a description (better suited to metadata). Still, it is well-structured and efficient for an 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?

Given the tool's complexity (10 parameters, no output schema, no annotations), the description covers the essential aspects: what the tool does, typical filters, sort options, default behavior, and a use case. It lacks details on return format or pagination (though limit/offset are in schema). It also doesn't explain the meaning of HPSA scores beyond min/max. However, it provides enough context for an agent to select and invoke the tool correctly in most 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?

Schema description coverage is 80% (8 of 10 parameters have descriptions in the schema). The tool description summarizes key filters (state, discipline, min_score, min_fte, rural_status, designated_since, status) and sort options, adding context like 'minimum HPSA score (0-26; higher = greater shortage)'. However, most of this is already present in the schema. The description does not clarify the meaning of 'sort' values beyond listing them. Baseline 3 is appropriate; the description adds marginal value.

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 finds HRSA Health Professional Shortage Areas by discipline, state, and shortage metrics. It specifies the verb 'Find', the resource 'Health Professional Shortage Areas', and lists key fields (Primary Care/Dental/Mental Health, HPSA score, FTE shortage). The use case is explicit: 'Powers medical & travel-nurse staffing.' This effectively distinguishes it from siblings like hcris, carecompare, and other healthcare 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 explains what the tool does and lists filters (state, discipline, min_score, min_fte, rural_status, designated_since, status) and sort options. It mentions defaulting to currently-designated areas. However, it does not explicitly compare to sibling tools or provide when-not-to-use guidance. For example, it doesn't clarify when to prefer hrsa_healthCenters_search or carecompare tools. Still, the context is clear enough for typical use.

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

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

No annotations are provided. The description mentions the data source (Form 990 Schedule J) and the cost per call, but does not disclose behavioral aspects like pagination (though limit/offset are in schema), ordering, or what happens on no results. As a search tool, it is likely read-only, but this is not stated.

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

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

The description is concise with two sentences plus a pricing note. It is front-loaded with the purpose, efficient, and every sentence adds value. No unnecessary 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 no output schema and no annotations, the description adequately covers the tool's purpose, input sources, and search criteria. It is mostly complete for a simple search tool but omits details about return structure and error behavior, which slightly reduces completeness.

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 describes search by EIN, name, and compensation range, covering ein, name_search, total_compensation_min/max. However, it does not explain limit and offset (pagination parameters), leaving their meaning implicit. The description adds value for most parameters but is incomplete.

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 searches for nonprofit hospital executive compensation, specifically officers and key employees. It specifies searchable dimensions (EIN, name, compensation range) and answers the core question of who the executives are and what they earn. This distinguishes it from sibling tools like irs990_org_search which searches organizations.

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?

Usage is implied by the description (use when you need executive compensation data), but there is no explicit guidance on when to use vs. alternatives or when not to use. Sibling tools exist but are not referenced for comparison.

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 no annotations, the description must carry the behavioral burden. It discloses the data source (IRS Form 990 e-filings) and price, but does not mention limitations (e.g., only health organizations? data freshness, error handling, read-only nature). It adds value but lacks depth.

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

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

Two sentences plus a price note, front-loaded with the core action. No wasted words; every sentence adds value.

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

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

For a simple one-parameter tool with no output schema, the description covers purpose, parameter, and source. However, it leaves ambiguity about whether it only covers health organizations and does not describe return format. Adequate but not fully comprehensive.

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

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

Schema coverage is 100% and the schema already describes 'ein' as 'IRS Employer Identification Number (EIN)'. The description adds context that the EIN is for a 'Nonprofit health organization' and that results are filing history, but this is marginal beyond the schema.

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

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

The description clearly states the tool provides 'Form 990 filing history by EIN' and specifies it answers questions about revenues, expenses, and trends. It distinguishes itself from sibling tools like irs990_org_search and irs990_officers_search by focusing on filing history for a specific EIN.

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

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

The description implies usage when you have an EIN and need filing history, and mentions the data source. However, it does not explicitly state when not to use this tool or suggest alternatives, though the context of sibling tools makes it clear.

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

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

No annotations are provided, so the description carries the full burden. It discloses the data source (IRS Form 990) and cost ($0.02/call), but does not mention read-only nature, rate limits, or potential side effects. The tool is a search, so it's likely safe, but the description does not confirm this.

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

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

The description is two sentences, front-loaded with key criteria, and includes a sample question, source, and pricing. Every word adds value with no 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 9 parameters and no output schema, the description provides a reasonable but incomplete picture. It covers the main search dimensions but lacks details on output format, pagination (offset/limit behavior), and error handling. The financial metrics are not fully clarified (revenue vs net income).

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 add meaning. It mentions state, NTEE code, revenue or expense range, and name, mapping to some parameters but omitting limit, offset, and net_income parameters. The term 'expense range' is ambiguous as the parameters use net_income, not expense. This lack of precision reduces helpfulness.

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 searches nonprofit health organizations by specific criteria (state, NTEE code, revenue/expense range, name). It distinguishes from sibling tools that focus on other provider types (dialysis, hospitals, etc.) and provides a sample question for context.

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

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

The description implies usage for financial profiling of nonprofit health organizations and mentions the data source, but does not explicitly state when not to use or contrast with alternatives like carecompare or hcris tools. The context from sibling names helps, but the description itself lacks explicit 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?

No annotations provided, so description carries full burden. It describes aggregation behavior ('rolled up to paying company') and defaults, but doesn't explicitly state read-only nature or other behavioral traits. Adequate but not exceptional.

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

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

Description is a single paragraph of ~80 words, front-loaded with purpose, followed by filters, sorting, and price. Every sentence is informative with no 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?

Covers purpose, filters, sorting, and defaults despite no output schema. Missing explicit return format, but context implies columns (manufacturer, spend, physicians). Overall sufficient for a complex tool.

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

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

Schema description coverage is 67%, and description adds meaning to parameters like max_total_spend (small/emerging) and default performance_year. It compensates for missing schema descriptions, scoring above 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 'Which manufacturers pay a given physician specialty in a territory, and how much' with specific verb 'pay' and resource. It provides examples like 'which pharma/device companies already reach cardiologists in Texas', distinguishing it from sibling tools focused on other healthcare 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?

Description explicitly lists filters and sort options, and shows query context. It doesn't explicitly state when not to use, but provides clear context, earning 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?

Although no annotations are provided (bar is high), the description excellently discloses a critical behavioral trait: it can only truly compare if prior_year data is loaded, and returns total_matched=0 (never fabricated) otherwise. It also clarifies pricing ($0.1/call). These are exactly the kind of behavioral disclosures beyond what schema/annotations provide.

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

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

The description is dense but efficient—two sentences accomplish a lot: purpose, semantics, defaults, filters, data caveat, pricing. It's front-loaded with the core semantic point before details. Slightly long but every clause earns its place; no wasted words.

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

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

For a filtered search tool with no output schema and no annotations, this description is remarkably complete: it states purpose, semantic distinction, filter behavior with defaults, comparison-year handling, data-availability caveat, and price. Despite 8 parameters, the agent has enough to invoke correctly in almost any scenario.

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 75%, which raises baseline to 3. The description adds meaningful semantics by clarifying the semantics of min_increase_usd/min_pct_change as AND-type thresholds (defaults 10k/10%) that apply to the per-manufacturer delta. However, it doesn't add detail for year vs prior_year beyond what the schema provides. Strong value but one step below full compensation.

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 is highly specific: verb 'search' + resource 'Manufacturers/GPOs', and crucially clarifies the semantics ('genuinely increased year-over-year' = per-manufacturer delta, not magnitude threshold). This distinguishes it clearly from the sibling manufacturers_openPayments and openPayments_recipients_yoyIncrease_search tools. Specifies filtering dimensions and defaults.

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

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

Explicitly states the default year pair (2023→2024), what filters exist (specialty, state, min_increase_usd, min_pct_change), and importantly warns about the data caveat (op_manufacturer_payments holds only PY2024 today). This is explicit when/context guidance plus a critical data-availability warning that helps the agent decide when this tool will return empty results.

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

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

No annotations provided, so the description must carry the burden. It discloses entity resolution (DBA/subsidiary merging) and the price per call. However, it does not explicitly state read-only nature, data freshness, rate limits, or other behavioral traits. Adds some value but could be more transparent.

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 paragraph that front-loads the main purpose and key filters. It includes a brief price note without being verbose. Two sentences at the end summarize the query intent. Concisely provides essential information.

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

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

With 11 parameters and no output schema or annotations, the description covers the tool's purpose and key filters but does not describe the output format or fields returned. It explains the entity resolution but lacks completeness for all parameters (e.g., performance_year, min_total_spend). Adequate but not fully comprehensive.

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 11 parameters with 55% description coverage. The description adds meaning for key parameters like name, max_total_spend, first_seen_year_min/max, specialty, state, connecting them to the small/emerging filter. However, parameters like sort, limit, offset, min_total_spend, performance_year are not mentioned, leaving 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 it discovers manufacturers from CMS Open Payments with a specific focus on emerging/small brands using filters like name, total-spend band, first appearance year, specialty, and state. It distinguishes from siblings like manufacturers_openPayments by emphasizing the small/emerging filter and entity resolution.

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

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

The description provides clear context on when to use the tool: to find small or new manufacturers paying physicians in a given specialty/state. It lists applicable filters but does not explicitly mention when not to use or provide alternatives. The implied usage is well-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?

With no annotations provided, the description carries full burden. It explains the data source (CMS NPPES full monthly file joined to directory), mentions 'precise new-provider dating', and lists filters and sort options. No destructive behavior is indicated.

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

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

The description is two sentences, front-loaded with the core purpose and value proposition. It includes the price and filter options concisely. Every sentence adds value with no waste.

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

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

Given no output schema and no annotations, the description covers the essential purpose, filters, sort, and pricing. It does not describe output format or pagination behavior, but the parameters (limit, offset) imply pagination. Overall, it is sufficiently complete for an agent to understand the tool's capability.

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 80%, so baseline is 3. The description adds context about the overall query purpose (new providers since a date by taxonomy and state) but does not significantly elaborate on individual parameter meaning beyond what's in the schema. It provides useful context without repeating schema details.

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 searches for newly-enumerated NPIs with exact enumeration date and taxonomy detail, using specific verbs and resources. It differentiates from sibling tools focused on other healthcare provider data (e.g., hospitals, dialysis) by specifying NPPES enumeration.

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 answers the question 'which providers/organizations of a given taxonomy were newly issued an NPI in my state since a date?' and lists filters. It provides context for when to use this tool, though it doesn't explicitly state when not to use it or name 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?

No annotations are provided, so the description carries full burden. It mentions the data source and cost but does not disclose pagination behavior, result limits, error handling, or whether the operation is read-only. Key behavioral traits like offset/limit usage and response format are omitted.

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

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

The description is two sentences plus a price tag, highly concise and front-loaded with the essential purpose and data details. Every word adds value, with no 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?

With 7 parameters, no required fields, no output schema, and 0% schema description coverage, the description is too brief to fully enable correct tool usage. It lacks information on required combinations, parameter formatting, and expected output 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 schema has 0% description coverage for 7 parameters. The description hints at occupation_code (SOC) and area_type/area_code (national/state/metro) but does not explain year, limit, offset, or state. This is insufficient compensation for the lack of schema descriptions.

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

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

The description clearly identifies the tool as providing healthcare wage benchmarks by SOC occupation and area, including specific statistics (median, mean, percentiles) and employment data. It answers the question 'what does this role pay in this market?' and distinguishes from sibling tools like carecompare or hcris, which cover different healthcare domains.

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

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

The description implies usage for wage data queries but does not explicitly state when not to use or list alternatives. Given the sibling tools cover distinct areas (dialysis, hospitals, etc.), the context is clear enough for selection, though explicit exclusions would improve 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?

No annotations exist, so the description carries the full burden. It discloses data origin (NPI join to Part D file), notes that the data is public and not a consumer report, and mentions the price. This provides valuable context about behavior and data quality.

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 compact, front-loaded with the core purpose, and every sentence adds value. No wasted words.

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

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

Given 7 parameters and no output schema, the description covers purpose, data source, key filters, and cost. It lacks explicit output format info, but is fairly complete for a search 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 coverage is high (71%), but the description adds value by explaining ordering by total payment amount, which is not in the schema. It also reiterates key filters, reinforcing their 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 screens physicians receiving payments from the CMS Open Payments file, ranked by total reported amount. It distinguishes the recipient-focused search from sibling tools like 'manufacturers_openPayments' and others.

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 lists filters but does not explicitly state when to use this tool over alternatives or provide usage exclusions. It implies usage for screening recipients but lacks clear 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?

With NO annotations provided, the description carries the full burden of behavioral disclosure. It does well: states what defines a 'real' increase (per-NPI delta, both-year payment requirement, dual thresholds), defaults, and public data provenance. It also discloses pricing. Minor gap: it doesn't disclose pagination behavior or whether results are sorted, but the tool's core behavioral contract (inclusion/exclusion criteria) is well specified.

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 that packs substantial value: purpose, delta semantics, defaults, filters, inclusion criteria, data provenance, and pricing. It's well front-loaded with the key differentiator ('real per-NPI delta'). Slightly long but every sentence earns its place — the only minor inefficiency is some repetition of the increase-threshold concepts.

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 8 params, no output schema, and zero annotations. The description provides strong coverage: purpose, threshold semantics, defaults, filters, inclusion/exclusion criteria, data source, and price. For a query tool with a moderately complex two-threshold comparison logic, this is well covered. The main omission is return-format/pagination details and sorting, but for a filtered list query the description 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?

Schema description coverage is 75%, so the schema documents most parameters. The description adds meaning by explaining how min_increase_usd and min_pct_change interact (both must be cleared) and clarifies year/prior_year pairing semantics. It doesn't describe every param, but the ones not covered by the schema are addressed in prose or leave the schema to carry the load. Adds value beyond the schema.

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

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

The description clearly identifies the tool's purpose: finding physicians whose TOTAL CMS Open Payments industry payments increased year-over-year, distinguishing real per-NPI deltas from magnitude thresholds. It names the specific resource (CMS Open Payments physician data) and the operation (yoy increase search), and explicitly differentiates from a generic magnitude threshold filter. It also clarifies sibling distinction — this is the recipients variant among openPayments and manufacturers_yoyIncrease 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 provides explicit when-to-use guidance: defaults to 2023→2024 but any year pair is selectable, and it states filters (specialty, state, min_increase_usd, min_pct_change) and the inclusion criterion (paid in BOTH years with a real rise clearing both thresholds). It even disclaims it's not a consumer report, which guides appropriate use. This clearly distinguishes from siblings like openPayments_recipients_search and the manufacturers variants.

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

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

No annotations provided, so description carries full burden. It explains matching by name prefix, rolling up latest-year Part D claims, and including drug class flags with new-to-class signal. Pricing is disclosed. No contradictions.

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

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

Moderately sized, front-loaded with purpose. Each sentence adds value (inputs, outputs, data source, pricing). Could be slightly more concise but overall 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?

Tool has 6 params and no output schema. Description explains inputs and output content (identity, summary, flags). Lacks exact return format, but is sufficiently complete for selection among sibling tools.

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 67% (4 of 6 params described). Description adds meaning for 'name' (free-text, examples), 'state' (2-letter, comma-separated), 'specialty' (substring). 'limit' and 'offset' are common pagination; description could add more but still adds value beyond 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 it looks up a prescriber by name or NPI and returns identity plus a Medicare Part D prescribing summary. It lists specific outputs (top drug, distinct drugs, beneficiaries, class flags) and distinguishes from siblings by mentioning NPPES directory matching and Part D claims rollup.

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 basic guidance: 'Provide name or npi' and optional state/specialty narrowing. Notes data source and pricing, but does not explicitly state when not to use this tool versus sibling tools like prescribers_partDByDrug_search or prescribers_partDClassWhitespace_search.

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 no annotations provided, the description carries the full burden. It mentions the data source (public CMS provider-level data) and pricing, but does not disclose output format, pagination behavior, or error handling. It adds some context but leaves gaps in behavioral traits.

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 at about 75 words, front-loads the main purpose, and every sentence adds value (purpose, example, filters, data source, price). No redundancy or unnecessary detail.

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 7 parameters, no output schema, and moderate complexity, the description covers the main use case but omits details about return format or typical output fields. It could be more complete by describing what each result entry contains.

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

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

The description adds meaning beyond the schema by explaining the purpose of drug_class with examples, clarifying that typical_specialties overrides the built-in set, and noting state accepts comma-separated codes. With 71% schema coverage, the description provides valuable additional context.

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 finds Medicare Part D prescribers outside typical specialties, using terms like 'off-specialty early adopters' and providing an example. It distinguishes from sibling tools like prescribers_partDBrandShareShift_search by focusing on atypical specialty rather than brand share or year-over-year delta.

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 explains when to use the tool (to find off-specialty prescribers) and lists filtering options, but does not explicitly state when not to use it or suggest alternatives. The context is clear, but exclusions are missing.

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 no annotations provided, the description carries the full transparency burden. It discloses the data source (public CMS data) and pricing ($0.1/call) but lacks details on rate limits, authentication requirements, data freshness, or behaviors like empty result handling. Some disclosure is present but incomplete.

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: two sentences plus a parameter list. It front-loads the purpose with an example, then lists parameters and closes with data source and pricing. Every sentence adds value with no 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?

The tool is moderately complex with 9 parameters. The description explains the concept and key parameters but lacks information about the output format (e.g., list of prescribers with shift values) and pagination details. Without an output schema, the description could more fully set expectations.

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

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

The input schema has 78% description coverage (high), so baseline is 3. The description adds value by explaining the share calculation (molecule claims ÷ class claims) and providing concrete examples for key parameters like drug_class and molecule. This goes beyond the schema's basic descriptions.

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

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

The description uses a specific verb 'Find' and identifies the resource 'prescribers with within-class molecule mix shift year-over-year'. It provides an example (GLP-1/Tirzepatide) and lists parameters, making the tool's purpose clear and distinct from sibling tools like prescribers_partDClassYoyDelta_search which focus on class-level shifts.

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 a concrete example but does not explicitly state when to use this tool versus alternatives. It implies usage for molecule-level share shifts but leaves the agent to infer when not to use it. No exclusion criteria or alternative tool mentions are provided.

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

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

With no annotations, the description correctly identifies the tool as a search/read operation and mentions output fields (claim/cost volume, specialty/state/city) and data source (public CMS). However, it lacks details on pagination, error handling, or what happens when no results are found.

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, starting with the core purpose and efficiently listing filters and output characteristics. Every sentence adds value without redundancy, though the price note could be seen as minor.

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 tool with 11 parameters and no output schema, the description covers the essential context: what it finds, how to filter, data source, and cost. It does not detail return structure beyond a brief mention, but schema descriptions handle most parameter details.

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 82%, so parameters are mostly documented already. The description adds thematic context (e.g., 'brands resolve to the canonical generic') and overall purpose, but does not significantly expand on individual parameter meanings beyond the schema.

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

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

The description clearly states the tool finds Medicare Part D prescribers by drug or therapeutic class at the NPI level, with the phrase 'core pharma targeting query' distinguishing it from sibling tools that focus on brand share shifts or class year-over-year changes.

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 lists filterable criteria (drug_class, drug, specialty, state, etc.) and provides examples like 'Ozempic' and 'GLP-1', giving agents clear context for when to use the tool. However, it does not explicitly state when to avoid this tool or list alternative tools for different scenarios.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool performs an 'anti-join of by-drug prescribers to Open Payments recipients', mentions the data source ('Public CMS data; not a consumer report'), and includes pricing. It does not discuss mutation or side effects, but the operation is a read-only search.

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: two sentences and a note. It front-loads the core purpose, then explains the mechanism and data source. Every sentence adds value without 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 no output schema, the description adequately covers the tool's logic, data source, and optional parameters. It explains the anti-join and the 'whitespace' concept. For a search tool, it is reasonably complete, though it could mention return fields or pagination.

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

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

The description adds meaning beyond the schema by explaining the overall concept of 'whitespace' and the 'engaged' parameter for inverting results. With 75% schema coverage, the description compensates for the remaining parameters by contextualizing the anti-join logic, though it doesn't detail every parameter.

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 'Find' and the resource 'high-volume Medicare Part D prescribers' with the specific condition of having 'NO reported CMS Open Payments'. It further explains the use case as a 'greenfield reachable before a competitor' audience, effectively distinguishing it from sibling tools like 'prescribers_partD_search' or 'openPayments_recipients_search'.

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 mentions the intended use case (finding prescribers without Open Payments for marketing outreach) and provides context for when to use it. However, it does not explicitly state when not to use it or mention alternative tools, but the sibling list implies 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?

With no annotations, the description carries full burden. It discloses the tool reads public CMS data, is not a consumer report, and costs $0.1/call. It explains the behavioral logic for new_entrant (max_prior_claims) and de_adopter (min_pct_drop). No destructive behavior is implied, and transparency about pricing and data source is helpful.

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

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

The description is concise, with five sentences that each add essential information. It front-loads the purpose, then explains directions, filters, data source, and pricing in a logical order. No redundant or extraneous 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?

Given the tool has 10 parameters and no output schema, the description covers the core behavior, filtering options, and data source. It mentions that the tool returns NPIs for each direction. However, it does not describe the output format or pagination details (limit/offset), which would be helpful for completeness.

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 80%, so baseline is 3. The description adds significant value beyond schema by explaining the 'switchable moment' concept, detailing the criteria for each direction (e.g., ≤max_prior_claims for new_entrant), and providing example values for drug_class. This enriches the context for parameter usage.

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 finds prescribers entering or leaving a Part D therapeutic class year-over-year (PY2023→PY2024). It specifies the two directions (new_entrant and de_adopter) and contrasts with sibling tools focused on other prescriber analytics, making the purpose distinct and unambiguous.

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

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

The description provides clear context ('switchable moment for a pharma team') and explains when to use each direction parameter. It lists filterable attributes (drug_class, state, specialty) and notes data source and pricing. However, it lacks explicit when-not-to-use guidance or alternative tool recommendations.

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 no annotations, the description carries full burden. It explains the drug_category selection logic, the return structure (one row per NPI with claim rollups), and the non-consumer-report nature. It does not detail pagination but that is schema-specified.

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, front-loaded paragraph with no wasted words. It covers purpose, usage, key parameters, and pricing efficiently.

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

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

The description explains the core logic and return format, but could mention ordering (implied by ranking) and that pagination is via limit/offset. Given no output schema, it is fairly 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 71%; the description adds meaning beyond schema by explaining how drug_category drives min_claims and sorting, and that performance_year defaults to 2024. It clarifies the state parameter but doesn't elaborate on limit/offset.

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

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

The description clearly states the tool screens Medicare Part D prescribers by specialty, state, and claim volume, targeting a pharma/device team. It distinguishes from sibling tools by focusing on Part D prescriber roster, but does not explicitly contrast with similar prescribers_partD* 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 implies usage for targeted outreach but does not explicitly state when to use or avoid this tool, nor does it mention alternatives. The pricing and data type hint at appropriate contexts.

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

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

No annotations exist, so description carries full burden. Discloses data volume (85k+ events), refresh frequency (twice-weekly to quarterly), pricing ($0.1/call), and pagination via cursor. Could mention idempotency or rate limits, but sufficient for a feed 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?

Single dense paragraph, does not waste words. Front-loaded with main purpose, then lists event types, usage context, filters, data size, refresh, pricing. Every sentence provides value.

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

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

Covers purpose, filters, data size, refresh, and pricing. However, missing description of output structure (fields per event) since no output schema exists. Slight gap for complex tool with 7 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 57%, but description enriches parameters: lists event types with names, explains 'since' format, 'cursor' as keyset. Adds context for NPI and CCN parameters implicitly. Compensates for lower schema coverage.

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

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

States specific verb+resource: 'Provider change-event feed over 7 federal sources'. Lists defined event types and explicitly answers a clear user question. Clearly distinguishes from sibling tools which focus on static searches.

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?

Clear context: 'Answers: what changed in my territory since I last looked?' and filter options provided. Does not explicitly state when not to use or name alternatives, but the use case is well-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?

With no annotations, the description discloses data sources, the nature of records ('as filed; not a consumer report'), handling of absent data, and pricing per call. This goes beyond basic functionality, though it could mention whether the call is read-only or idempotent.

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

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

The description is a single paragraph that front-loads the core purpose and lists contents efficiently. It includes necessary disclaimers and pricing without redundancy, though it could be slightly more structured.

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

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

Despite no output schema, the description thoroughly explains what data categories are included and notes that missing items are explicitly stated. It lacks details on pagination, rate limits, or result size, which would enhance completeness.

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

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

The single parameter 'npi' is fully described in the schema as a 10-digit NPI. The tool description does not add additional context or constraints beyond the schema, so baseline 3 applies.

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

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

The description uses a specific verb phrase 'One-call dossier' and identifies the resource 'provider NPI', listing distinct data categories. It clearly distinguishes from sibling tools that focus on specific datasets or searches.

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

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

The description implies usage when a comprehensive provider overview is needed, but does not explicitly state when to use this tool versus alternatives, nor provide exclusions or when-not-to-use 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?

No annotations exist, so the description carries full burden. It discloses monthly refresh and a per-call price, but does not mention destructive behavior, authentication requirements, rate limits, or pagination details. Useful but not comprehensive.

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

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

Two sentences with no wasted words: states action, answers question, mentions refresh and price. Front-loaded with the key purpose.

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

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

Given 7 parameters, no annotations, and no output schema, the description is too brief. It omits output format, interaction between search fields, and pagination behavior. Adequate for a simple query but not fully 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%, so the description must compensate. It lists searchable fields (name, NPI, state, exclusion type), but the schema has 7 parameters including offset, limit, last_name_search, business_name_search. The description adds meaning for 4 but leaves offset, limit, and the distinct name fields unexplained.

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 searches the OIG List of Excluded Individuals/Entities (LEIE) by various criteria, answering a specific question about provider exclusion. It distinguishes from sibling tools like sanctions_screen by focusing on the LEIE database.

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

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

The description implies usage for checking provider exclusion status but does not explicitly state when to use this tool versus alternatives like sanctions_screen. No exclusions or when-not-to-use guidance is provided.

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

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

Discloses return of an attested verdict with per-list counts, mentions price ($0.15/call), and explains coverage limitations for NPI-only screens. No annotations, so description carries full burden and does so thoroughly.

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

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

Three sentences with no redundancy. First sentence front-loads the core action. Each sentence adds value: what it screens, how results look, guidance on inputs, intended users, and cost.

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 no annotations, the description covers purpose, parameters, usage context, and behavior. References output fields (lists_checked, lists_not_applicable) but doesn't describe full output structure, which is acceptable for a screening tool.

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

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

With 0% schema description coverage, the description adds meaning by explaining that name and NPI are used together or separately, and that NPI-only covers 4 of 6 lists while name is needed for full coverage. Lacks explicit data types or constraints, but sufficiently clarifies usage.

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

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

The description clearly states the tool screens a provider against multiple federal and state exclusion lists, listing specific databases (OIG LEIE, GSA SAM, etc.) and returns a clear/flagged verdict. It distinguishes from sibling tool sanctions_leie_search, which covers only one list.

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

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

Explicitly says 'For hiring, credentialing, and vendor-onboarding agents.' Also explains when NPI-only is insufficient (OFAC SDN and FDA debarment need a name) and directs to output fields for details.

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