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Vaquill-AI/vaquill-mcp

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by Vaquill-AI

get_sections_batch

Batch-fetch metadata for up to 50 legal sections per call, preserving order and reporting missing IDs, to enrich search results with fewer round trips.

Instructions

Metadata for up to 50 sections in one call.

Cost: 2 credits per section returned. Identifiers with no match are refunded, so a batch of 50 ids that resolves 47 costs 94 credits, not 100. Batching is a round-trip and latency win, not a discount.

Exists because enriching a search page previously meant one HTTP call per result: a 50-result page was 50 round trips. The response preserves the order of actIds and reports misses separately in notFound, so results can be zipped back onto the ids that produced them.

Use /us/statutes/section/{actId}/body for full text; this endpoint returns metadata and source links only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actIdsYesSection identifiers from a prior `/us/statutes/search` response. Up to 50 per call. Duplicates are collapsed, and order is preserved in the response. Each id encodes the citation's hierarchy: `USC_T42_C21_S1983` is Title 42, Chapter 21, Section 1983. The chapter is not derivable from a citation, so take ids from search results rather than assembling them.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description carries full burden. It discloses cost per section, refunds for unmatched IDs, order preservation, separate notFound reporting, duplicate collapse, and the distinction from full-text endpoints. This is rich behavioral context beyond the bare schema.

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 well-structured: opening summary, cost model, rationale, ordering/notFound behavior, and alternative endpoint. Every sentence adds value with no filler, making it efficient for its length.

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

Completeness5/5

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

Given the tool's complexity (batching, cost, refunds, order) and the presence of an output schema, the description provides all necessary context. It explains why the tool exists, how IDs should be sourced, and what results look like, with no critical gaps.

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?

The schema covers actIds 100% with examples, max/min, duplicate behavior, and id encoding. The description adds cost-per-section context, but this is more behavioral than parameter meaning. Baseline 3 is appropriate given high schema coverage.

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 returns metadata for up to 50 sections in one call. It differentiates from siblings by explicitly noting it returns metadata and source links only, with a pointer to the full-text endpoint, so the purpose is unmistakable.

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 explains when to use this tool (enriching search pages) versus alternatives, explicitly referencing the full-text endpoint and noting that batch IDs should come from prior search results. It also clarifies the cost behavior, making the usage context fully transparent.

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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