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Search example sentences

search_examples

Retrieve example sentences from your LingoChunk library using a dictionary word or text substring. Get capped samples for vocabulary practice.

Instructions

Search the user's readable library for sentences. 'lemma' returns the curated example sentences for that word; 'q' does a case-insensitive substring match on sentence text. At least one is required, and 'lemma' takes precedence when both are given. Results are a capped sample, not exhaustive.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoCase-insensitive substring match on sentence text.
lemmaNoFind example sentences for this dictionary form.
limitNo
languageNoRestrict to one language (normalised to lowercase).
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It accurately portrays a read-only search operation and mentions the non-exhaustive nature of results, but could additionally clarify permissions or rate limits.

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?

Three concise sentences with no redundancy, each sentence adds essential information: purpose, parameter behavior, and result limitations.

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?

Covers key aspects given no output schema or annotations, but lacks details on return format or how to handle the capped nature (e.g., pagination). Still largely complete for a search tool.

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?

The description adds significant meaning beyond the schema: clarifies the role of 'lemma' vs 'q', states the requirement of at least one, precedence rule, and sampling behavior, complementing the 75% schema coverage effectively.

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 searches for sentences, specifies two distinct search modes ('lemma' for curated examples, 'q' for substring match), and distinguishes them, making the purpose unambiguous.

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?

Provides explicit guidance on when to use each parameter, states at least one is required, explains precedence when both are given, and notes that results are a capped sample, covering usage context well.

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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