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Vaquill-AI/vaquill-mcp

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get_section_neighbors

Retrieve the sections immediately before and after a given statute to get its surrounding statutory context, respecting chapter boundaries and natural ordering.

Instructions

The sections immediately before and after a given section, in statutory order.

Cost: 2 credits. Not-found and failed lookups are not charged.

Reading one section rarely settles a question -- the definitions sit a few sections back and the exceptions a few forward. Search cannot answer "what comes next", because relevance ranking is not statutory order.

Neighbours never cross the containing chapter (or, where a section has no chapter, the containing title or state code), so you get the sections a reader would actually turn to, not the numerically adjacent row from an unrelated part of the corpus.

Ordering is natural, not lexicographic: 9 before 10, 240.9 before 240.10, 1983 before 1983a.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many sections to return on EACH side.
act_idYesSection identifier. Encodes the citation's hierarchy: `USC_T42_C21_S1983` is Title 42, Chapter 21, Section 1983, the section a lawyer writes as 42 U.S.C. § 1983. Take it from a `/us/statutes/search` result rather than assembling it: the title and section are derivable from a citation, but the CHAPTER is not, so hand-built ids usually 404.

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 provided, the description fully carries behavioral disclosure. It clearly states the cost (2 credits), that failed lookups are not charged, that results never cross chapter/title boundaries, and that ordering is natural (not lexicographic). All of these are non-obvious behaviors a user must know, making this excellent transparency.

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 compact and purposefully organized: opening with the core function, then cost, then usage rationale, then boundary/ordering nuances. Every sentence adds value, no filler, and the most critical information (what it does) comes first.

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?

Despite having an output schema (so return values need not be described), the description covers cost, failure handling, scope boundaries, ordering rules, and ID source. For a tool that navigates statute structure, this is exactly the contextual information needed to invoke it correctly, making it complete.

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 description coverage is 100%, so the baseline is 3. The description adds no new parameter-specific meaning beyond the schema; it reinforces the 'EACH side' meaning of limit and the ID hierarchy, but these are already in the schema descriptions. The boundary and ordering details pertain to output behavior, not parameter semantics.

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 first sentence precisely states the tool's function: returning the sections immediately before and after a given section in statutory order. It uses a specific verb ('get') and resource ('sections'), and clearly distinguishes itself from sibling tools like search and batch retrieval by focusing on statutory neighbors.

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 explicitly explains when to use this tool (when context around a section is needed, because 'reading one section rarely settles a question') and contrasts with search, which 'cannot answer what comes next' due to relevance ranking. This provides a clear use-case distinction, effectively serving as a when/when-not guide.

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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