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CivicDataForge Government Evidence

Restaurant Inspection Scores

civicdataforge--restaurant-inspection-scores

Use for official restaurant inspection scores, violations, and facility-history research in supported jurisdictions. Do not use for general property-code violations or childcare inspections. 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
NameRequiredDescriptionDefault
sinceNoInclusive ISO date YYYY-MM-DD.
untilNoInclusive ISO date YYYY-MM-DD.
citiesNoOfficial inspection feeds to query. Every output and run receipt discloses source age, row grain, and source status.
resultContainsNoCase-insensitive substring on the jurisdiction's published result or grade. A source with no such field returns no matches; scores are never guessed into grades.
socrataAppTokenNoOptional caller-owned app token for a dedicated Socrata rate-limit pool. It is sent only as X-App-Token and never returned.
maxRecordsPerCityNoBounded newest-first query limit per jurisdiction (1-5,000).
businessNameContainsNoCase-insensitive establishment-name substring. Applied server-side where supported and verified client-side for every source.

TDQS

A4.6/5.0
Behavior5/5

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

The description goes well beyond annotations by disclosing execution traits: 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 adds cost, timeout, row-limit, and non-mutation specifics that annotations do not cover, and it does not contradict the provided 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?

The description is three sentences, tightly written, with the purpose and exclusions front-loaded, followed by execution behavior and constraints. Every sentence earns its place; there is no fluff or repetition of schema content.

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?

Given the tool has 7 parameters, no output schema, and moderate complexity, the description covers the key contextual needs: purpose, exclusions, cost, timeout, row limit, and non-modification. It could have described the output structure in more detail, but 'source-linked rows' plus the schema-level promise of run receipts in the cities parameter provides sufficient context for an agent to call it correctly.

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

Parameters3/5

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

Schema coverage is 100%, with every parameter having a meaningful description (e.g., date patterns, defaults, enums). The tool description does not elaborate on individual parameters, but it does add overall context about row limits and non-modification. Per the rubric, with high schema coverage, the baseline is 3, and the description does not need to add per-parameter detail.

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 clearly states the tool's purpose: 'official restaurant inspection scores, violations, and facility-history research in supported jurisdictions.' It also explicitly excludes 'general property-code violations or childcare inspections,' which differentiates it from sibling tools like property-violations and childcare-licensing. This is a specific verb-resource pairing with clear boundaries.

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?

The description gives explicit when-to-use and when-not-to-use guidance: 'Use for official restaurant inspection scores... Do not use for general property-code violations or childcare inspections.' It also clarifies the supported jurisdictions are those listed in the schema, giving agents clear selection criteria without needing to inspect siblings.

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.

Resources