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

Beyond the annotations, the description discloses that invoking this tool starts a bound Apify Actor, uses the caller's APIFY_TOKEN, may consume Apify usage, waits up to 60 seconds, caps results at 1,000 rows, and does not modify government records. These side effects, latency expectations, and limits are exactly the kind of behavioral context annotations alone do not convey.

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

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

The description is three sentences with no filler, and the most decision-relevant information is front-loaded: scope first, alternates second, behavioral side effects last. Every sentence earns its place.

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

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

The description plus the fully documented schema is enough for an agent to decide to call the tool and fill in parameters correctly. However, there is no output schema and 'source-linked rows' only broadly describes the return, so a bit more detail about returned fields would make it 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 100%, so all seven parameters, including enums, defaults, and filter semantics, are already documented in the schema. The description adds no parameter-level detail beyond the schema, so the 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 opening sentence is an explicit directive: use for Florida statewide DBPR vacation-rental and lodging-license evidence. It then names sibling tools for adjacent cases (municipal permits and code violations), so an agent can distinguish it from str-permit-registry and property-violations immediately.

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 states the exact scope (Florida statewide DBPR vacation-rental/lodging licenses) and gives explicit 'use X instead' routing for municipal STR permits and code violations. This satisfies both when-to-use and when-not-to-use criteria in a compact way.

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 discloses that it starts an Apify Actor using the caller's APIFY_TOKEN, may consume usage, waits up to 60 seconds, and caps results at 1,000 rows without changing the source list. It also reveals operational details like NPI lookups and SAM.gov queries. This goes well beyond the annotations (readOnlyHint=false, destructiveHint=false, openWorldHint=true) and provides substantial behavioral context.

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

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

The description is compact yet information-dense: purpose, constraints, and operational limits are front-loaded, with no fluff. Each sentence earns its place, making it easy for an agent to parse quickly.

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 an 11-parameter tool with no output schema, the description gives essential operational expectations (timeout, usage, row limits, data immutability) and clarifies its limitations (not identity adjudication, not licensing). It does not explicitly explain the screen vs filter modes, but the schema covers that thoroughly, so the description is otherwise complete for safe and correct invocation.

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

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

Schema description coverage is 100%, so all parameters are already well-documented. The description adds only marginal parameter context (mentions name, NPI, state, specialty as search keys) but does not elaborate on modes, maxRecords, or enrichment fields beyond what the schema provides. Baseline 3 is appropriate because the schema bears the load.

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 review candidates in the HHS-OIG LEIE by name, NPI, state, specialty, or exclusion type. It distinguishes from sibling tools that target unrelated registries (permits, rentals, violations, licensing), making its purpose unambiguous.

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

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

It explicitly says not to use for identity adjudication or facility licensing, providing clear boundaries. While it doesn't name alternative tools, the siblings are unrelated, and the description gives a clear 'when to use' context (screening/filtering).

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 that it starts the bound Apify Actor with the caller's token, may consume usage, waits up to 60s, returns up to 1,000 rows, and does not modify government records. This goes beyond the sparse annotations and provides clear expectations.

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: the first covers purpose and sibling differentiation, the second covers operational behavior. No redundancy or fluff.

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

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

Given no output schema, the description sufficiently explains purpose, scope, behavior, and constraints. It notes cross-state vs Texas, usage consumption, timeout, and row limit, which are essential for an agent to decide and invoke correctly.

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

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

Schema description coverage is 100% (all 7 parameters have detailed descriptions). The tool description itself does not add further parameter details, so baseline of 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?

States a specific verb ('use for ... research') and resource ('childcare licensing, inspection, deficiency') and explicitly names the sibling tool it is not ('texas-childcare-licensing'). This clearly differentiates the tool's scope.

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

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

Explicitly instructs when to use: 'Use for cross-state...' and when not: 'For Texas-only ... use texas-childcare-licensing instead.' Also notes operational constraints like 'may consume Apify usage' and 'waits up to 60 seconds.'

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?

Despite annotations, the description adds crucial behavioral context: starts an Apify Actor with the caller's APIFY_TOKEN, may consume Apify usage, waits up to 60 seconds, returns at most 1,000 rows, and does not modify government records. This goes well beyond the annotation hints and informs the agent of side effects and limitations.

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

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

Three concise sentences: purpose, exclusions, and behavior. Every sentence earns its place, and the most important scoping and usage constraints are front-loaded.

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 no output schema, the description covers all necessary aspects: what it does, when to use it, what it returns, its side effects, and its constraints. Nothing an agent needs to call it correctly is missing.

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%, with each parameter (cities, status, maxRecordsPerCity) already described in the schema. The tool description itself adds no parameter details beyond that, so the baseline of 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 clearly states the tool's purpose: 'Use for municipal building, property, and code-violation research in supported jurisdictions.' It names a specific verb (use for) and resource (building/property/code-violation research), and differentiates from siblings by explicitly excluding STR licensing and restaurant health inspections.

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

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

Provides explicit when-to-use and when-not-to-use guidance: 'Use for municipal...' and 'Do not use for STR licensing or restaurant health inspections; choose the corresponding permit or inspection tool.' This directs the agent to alternatives without ambiguity.

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

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

Beyond the annotations, it discloses side effects: starts an Apify Actor, may consume usage, waits up to 60 seconds, and returns at most 1,000 rows. It also clarifies non-destructive behavior with 'without modifying government records,' which is not fully captured by the annotations.

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

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

The description is concise and well-structured, with the purpose and exclusions first, followed by execution details. It uses three sentences and avoids unnecessary fluff, though it could be slightly tighter.

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

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

Given the lack of an output schema and the tool's complexity, the description covers key operational aspects: data source, limits, timing, and read-only nature. It does not detail the exact return structure but mentions 'source-linked rows,' which is sufficient for basic use.

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

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

The description does not add parameter-specific details beyond the schema, which already has comprehensive descriptions for all 7 parameters (e.g., 'Inclusive ISO date YYYY-MM-DD.'). Since schema coverage is 100%, the baseline score is 3.

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

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

The description clearly states the tool is for official restaurant inspection scores, violations, and facility-history research. It explicitly distinguishes from siblings by stating 'Do not use for general property-code violations or childcare inspections,' making it immediately identifiable.

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

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

It gives explicit positive usage ('Use for official restaurant inspection...') and negative guidance ('Do not use for...'), which directly tells an agent when to choose this tool over alternatives.

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

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

The description discloses meaningful behavioral traits beyond the annotations: it 'Starts the bound Apify Actor with the caller's APIFY_TOKEN, may consume Apify usage, waits up to 60 seconds, and returns at most 1,000 source-linked rows without modifying government records.' This covers side effects, cost, timeout, row limit, and non-destructiveness. It does not contradict the annotations.

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

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

The description is two sentences, front-loaded with the primary use case and the sibling alternative, then packing operational constraints (token, usage, timeout, row cap, non-modification) into a single dense sentence. Every phrase earns its place; there is no filler.

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

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

Given the lack of an output schema, the description does a good job of conveying the broad result shape ('address, owner, permit-ID, or jurisdiction research' and 'source-linked rows') and key limits. It could be more explicit about the fields returned per row or error/timeout behavior, but it is complete enough for selection and invocation.

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

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

Schema description coverage is 100%, so the schema already documents every parameter in detail, including enum options for cities, the status filter, snapshot behavior, max records per city, and jurisdiction resolution. The tool description adds no further parameter-level semantics, so the baseline of 3 is appropriate.

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

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

The description opens with a specific verb and resource: 'Use for address, owner, permit-ID, or jurisdiction research across supported US short-term-rental permit sources.' It clearly states the domain and scope, and it names the closest sibling (fl-dbpr-vacation-rentals) as the alternative, which lets an agent distinguish the tool without opening schemas.

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

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

The description explicitly says when to use this tool and when not to: 'Use for address, owner, permit-ID, or jurisdiction research' and 'For Florida statewide DBPR lodging licenses, use fl-dbpr-vacation-rentals instead.' This direct exclusion of the most likely confusable sibling is strong usage guidance.

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

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

Annotations declare readOnlyHint=false, destructiveHint=false, and openWorldHint=true, which is incomplete without context. The description adds critical behavioral details: it 'starts the bound Apify Actor with the caller's APIFY_TOKEN,' 'may consume Apify usage,' 'waits up to 60 seconds,' and 'returns at most 1,000 source-linked rows without modifying government records.' This explains the side effects (Apify usage) and safety (no modification) far beyond annotations. It loses one point because 'may consume Apify usage' is vague about cost/limits, but the core transparency is strong and contradicts nothing.

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

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

The description is three sentences. The first front-loads purpose, the second routes to a sibling alternative, and the third summarizes side effects and constraints. Every sentence earns its place, with zero fluff. The most important scoping (Texas-only) is first, and the alternative is mentioned immediately after, making it easy to parse quickly.

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 that returns data without an output schema, the description covers the key aspects: scope (Texas), the kind of data (childcare operation, inspection, deficiency evidence), the side effects (Apify usage), the timeout (60 seconds), the cap (1,000 rows), and the safety (no modification of records). With all 4 parameters having 100% schema coverage and enums not applicable, the description is complete for an agent to decide and call the tool. The lack of an output schema is compensated by describing what the rows represent ('source-linked rows').

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%, so each of the 4 parameters is documented in the schema itself. The description reinforces the Texas-specific context and adds the behavioral context for maxRecords (the default behavior of capping at 1000 source-linked rows) and minHighDeficiencies (compliance screening). It doesn't repeat type/schema info but adds usage nuance, so a 4 is appropriate — a 5 would require description-specific clarification not already in the schema, but the description handles the main gaps well.

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's for 'Texas-only childcare operation, inspection, and deficiency evidence,' distinguishing it from the multistate sibling. It specifies a concrete scope and action. This clearly differentiates from siblings like 'restaurant-inspection-scores' or 'leie-exclusion-screening' without needing to open the schema.

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 names the alternative tool, 'multistate-childcare-licensing,' and provides the condition for choosing between them: use this for Texas-only, use the other for multi-state. This gives an agent clear routing logic with no inference needed.

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