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by io.github.Vaquill-AI
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Describe the corpus behind this API: how many acts and provisions it holds, how they break down by jurisdiction, regulator and status, and how DEEP that holding goes.

Cost: free.

Read the depth block before building on this API. It answers four independent questions, because "we hold the act" and "we hold what amended it" are different claims and only one of them is true for most of the corpus:

  • actsWithText is every act we serve;

  • actsWithAmendmentEvents is the much smaller set for which we hold parsed amendment records;

  • actsClaimingAmendmentsWithoutEvents is the known gap between them, published so that an empty amendment list is legible as a gap rather than as a claim that nothing ever changed.

Two different kinds of count appear here and they are labelled. passages in the breakdown lists is faceted live and exact right now. The act-level numbers in totals and depth come from a full distinct-act scan, and measuredOn says when it ran. Do not divide one into the other.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior4/5

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

The annotations already declare readOnlyHint and openWorldHint, so the base behavioral contract is clear. The description adds valuable detail about the meaning of the 'depth' block, the distinction between act counts and amendment-event counts, and cautions against dividing incompatible numbers.

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

Conciseness4/5

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

The description is more verbose than strictly necessary, but the extra length is used to prevent real misunderstandings about the semantics of counts and the 'depth' field. The structure is logical and front-loaded with the core purpose.

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 zero parameters and no output schema details in the description, the tool is adequately specified. The description explains the key data distinctions and cautions, so an agent has enough context to call it correctly and interpret results sensibly.

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

Parameters5/5

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

There are no parameters, so there is nothing to document. The schema coverage is complete and no additional parameter guidance is needed.

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: describing the corpus behind the API, including counts of acts and provisions, breakdowns by jurisdiction/regulator/status, and depth of holdings. It is unmistakably distinct from sibling tools that fetch individual acts or sections.

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

Usage Guidelines4/5

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

The description explains when to use this tool implicitly: to understand corpus coverage and depth before building on the API. It does not explicitly contrast with sibling tools, but the zero-parameter interface and focus on aggregate statistics make the use case clear enough.

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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