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list_records

Retrieve AI incident law records from a public corpus using filters for domain, jurisdiction, or status to identify relevant litigation and enforcement entries.

Instructions

List compact AI incident law records, optionally filtered by dataset, domain, type, jurisdiction, status, source quality, or review flag.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum records to return. Default 25.
domainNoDomain/category substring filter.
datasetNoDataset bucket, such as included, review, or global.
error_typeNoError or event type substring filter.
jurisdictionNoJurisdiction, country, or authority substring filter.
needs_reviewNoReview flag, usually yes or no.
filing_statusNoOutcome or procedural posture substring filter.
source_qualityNoSource quality substring filter.
Behavior2/5

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

With no annotations, the description carries full burden for behavioral disclosure. It mentions 'compact' records (implying abbreviated output) and optional filters, but fails to disclose side effects, read-only nature, pagination behavior, or any authentication/rate-limit requirements. This is insufficient for a mutationless listing tool.

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 a single sentence that efficiently conveys the core functionality and filter options. It is well-structured and front-loaded, with no extraneous words. Minor room for improvement: could be more precise with parameter names.

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

Completeness2/5

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

Given the complexity (8 parameters, no output schema, no annotations), the description lacks important contextual details such as the response format (e.g., what a 'compact' record includes), pagination behavior, default limit, or ordering. This gap may lead to agent confusion about how to handle large result sets or interpret results.

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 each parameter has a schema description. The tool description lists most filter categories (dataset, domain, type, jurisdiction, status, source quality, review flag), but uses slightly different terminology (e.g., 'type' vs 'error_type', 'status' vs 'filing_status') and omits 'limit'. It adds no new semantic or syntactic details beyond what the schema provides, thus meeting the baseline score of 3.

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 lists 'compact AI incident law records' with optional filtering capabilities, distinguishing it from siblings like 'get_record' (single record) and 'search_records' (likely more extensive). The verb 'list' and resource 'records' are specific and unambiguous.

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 description implies usage for listing filtered records but does not explicitly state when to use this tool over alternatives like 'search_records' or 'get_record'. It lacks guidance on preferred context or exclusion criteria, making it adequate but not exemplary.

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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