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search_boolean

Execute Westlaw-style Boolean and proximity searches to find North Dakota opinions, statutes, and regulations with connectors like &, /N, and /s.

Instructions

Westlaw-style Boolean / proximity search.

Connectors: & (AND), | or OR (OR), % or NOT (BUT NOT), /N (within N words), /s (same sentence ≈ NEAR/20), /p (same paragraph ≈ NEAR/50), ! truncation (e.g. negligen! → negligen*), and "quoted phrases". Because FTS5 has no sentence/paragraph unit, /s and /p are token-distance approximations — the translated FTS query and any approximation notes are returned for transparency.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum results (default 20, max 50).
queryYesA Westlaw-style query (e.g. `warrant /s nighttime % consent`).
authorNoFilter by authoring justice's last name.
date_toNoFilter to opinions filed on/before this date (YYYY-MM-DD).
date_fromNoFilter to opinions filed on/after this date (YYYY-MM-DD).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries the full transparency burden and does well by disclosing that FTS5 has no sentence/paragraph unit, so /s and /p are token-distance approximations. It also says the translated FTS query and approximation notes are returned for transparency, which is a meaningful behavioral disclosure beyond the name.

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 front-loaded and every line earns its place: a one-line purpose, a compact connector legend, and a brief caveat. The formatting makes the syntax scannable without boilerplate.

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 syntax is fully specified, and the presence of an output schema means return values don't need detailing. The only completeness gap is the lack of explicit when-to-use guidance versus sibling search tools, which is more of a usage-guideline issue than a call-correctness issue.

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 applies. The description adds useful query-syntax semantics for the query parameter but contributes nothing about limit, author, or date filtering, which are already well documented in the schema.

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

Purpose4/5

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

The description opens with 'Westlaw-style Boolean / proximity search', naming a specific search mode and, through the schema's opinion filters, the resource type. It doesn't explicitly distinguish itself from siblings like search_opinions or search_faceted, but the Boolean/proximity emphasis marks it as the specialized structured query tool.

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

Usage Guidelines3/5

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

The connector reference implies this tool is for queries requiring Boolean operators or proximity, but the description never states when to choose it over search_opinions, search_faceted, or corpus-specific sibling searches. There are no exclusions or alternative routing cues.

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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