CivicDataForge Government Evidence

Florida DBPR Vacation-Rental Licenses

civicdataforge--fl-dbpr-vacation-rentals

Use for Florida statewide DBPR vacation-rental and lodging-license evidence. For municipal STR permits use str-permit-registry; for code violations use property-violations. 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.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`city`	No	Filter by property location city (e.g. Kissimmee, Orlando, Destin). Leave empty for all.
`county`	No	Filter to one Florida county by property location (e.g. Orange, Osceola, Dade, Monroe, St. Johns). Leave empty for statewide.
`status`	No	Current = up to date with DBPR; Delinquent = license not renewed on time (expiry date has passed).	all
`maxRecords`	No	Cap total records returned (statewide is ~174k).
`licenseType`	No	DBPR licenses vacation rentals as Condos (type 2006) or Dwellings (type 2007, incl. single-family homes & townhouses).	all
`nameContains`	No	Case-insensitive substring match on the business name or licensee name.
`proxyConfiguration`	No	The state site blocks plain datacenter traffic; Apify Proxy is used by default. Switch to residential if datacenter IPs get blocked.

TDQS

A4.8/5.0

Behavior5/5

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

The description discloses operational behavior beyond annotations: it starts an Apify Actor with 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. This is important side-effect context not present in 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.

Conciseness5/5

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

Three front-loaded sentences: purpose, sibling routing, then operational behavior. Every sentence earns its place, and no schema field descriptions are redundantly repeated.

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

Completeness4/5

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

The description covers selection context, side effects, timeouts, result caps, and non-destructiveness, which is strong for a 7-parameter tool with no output schema. It stops short of describing the exact output fields or pagination beyond the 1,000-row cap, leaving a minor completeness gap.

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

Parameters4/5

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

The input schema already covers all parameters with rich descriptions, so the baseline is high. The tool description adds a meaningful global constraint absent from the schema—at most 1,000 source-linked rows returned—which affects expectations for maxRecords and overall output volume.

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

Purpose5/5

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

The description opens with a specific use case: Florida statewide DBPR vacation-rental and lodging-license evidence. It explicitly names sibling tools to distinguish scope, so an agent can immediately tell this from str-permit-registry and property-violations.

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

Usage Guidelines5/5

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

It explicitly directs the agent to use str-permit-registry for municipal STR permits and property-violations for code violations, providing clear when-to-use and when-not-to-use guidance. The main use case is also stated upfront.

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

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TDQS

A3.9/5.0

Disambiguation3/5

Most domain tools are well-scoped with explicit cross-references (e.g., FL DBPR vs. STR registry, Texas vs. multistate childcare). However, the evidence-gateway overlaps with EPA, U.S. property, and other specialized tools by describing similar intake categories, creating ambiguity about when to use the router versus the domain-specific tool.

Naming Consistency3/5

The specialized tools consistently use the civicdataforge-- prefix with descriptive noun phrases, while the generic actor tools use imperative verb_noun style. The naming is readable and predictable within each subgroup, but the mixed conventions and the awkward doubled prefix in civicdataforge--civicdataforge-evidence-gateway prevent full consistency.

Tool Count4/5

Fourteen tools is reasonable for a broad government-evidence server covering many data domains plus an async run lifecycle. The count is not excessive, though the gateway and several overlapping domain-specific tools add some redundancy.

Completeness4/5

The tool set covers a wide range of evidence domains and provides complete async workflow coverage: launch queries, check run status, fetch dataset items, read KVS records, and abort runs. Minor gaps remain, such as no explicit way to enumerate supported jurisdictions or sources, and the gateway's broad categories are underspecified.

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