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APVentureEngine

warn-mcp

by APVentureEngine
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Python
Remote

employer_layoff_history

Look up an employer's WARN layoff history: notice count, total workers affected, states, and first/latest activity dates since 1988.

Instructions

Every WARN notice one employer has filed, in any covered state, back to 1988: notice count, total workers affected, states, first and latest activity. Returns employers_matched, employers (up to 10 distinct canonical names, most workers affected first) and notices_for_best_match (up to limit notices of the top employer, newest notice_date first). For a date- or state-bounded question use search_layoff_notices.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax individual notices returned, 1-200 (default 25).
companyYesEmployer name, matched on WHOLE WORDS (case-insensitive), e.g. 'united airlines'. Every word you give must appear as a complete word in the filed name, so 'ford' will NOT return 'Stanford Health Care'. On 0 hits the response lists similar_company_names to retry with.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.2.4
    • changedInput schema / properties / company / description
      Previous value: -"Employer name or a substring of it."New value: +"Employer name, matched on WHOLE WORDS (case-insensitive), e.g. 'united airlines'. Every word you give must appear as a complete word in the filed name, so 'ford' will NOT return 'Stanford Health Care'. On 0 hits the response lists similar_company_names to retry with."
  2. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the full behavioral burden and does so thoroughly. It discloses return structure (employers_matched, employers, notices_for_best_match), ordering rules (most workers affected first, newest notice_date first), matching semantics (whole-word, case-insensitive), and the similar_company_names fallback on zero hits. This gives the agent actionable expectations before invoking.

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 sentences pack a high density of useful information: scope, return fields, ordering, fallback behavior, and routing guidance. The most important scoping statement is front-loaded, and no sentence is wasted.

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?

Given no output schema, the description still fully explains the response shape, ordering, and failure handling. It covers the main edge case (0 hits), the matching rule, and the key routing alternative. An agent has enough context to call this tool appropriately and interpret its results correctly.

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 schema already documents company matching and limit behavior. The description adds marginal value by explaining how limit affects notices_for_best_match, but this is mostly redundant with the schema's 'Max individual notices returned' wording. No additional parameter nuance 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 defines the tool as returning the complete WARN notice history for a single employer, including counts, affected workers, states, and activity dates. It distinguishes itself from siblings by explicitly scoping to employer-level history and naming search_layoff_notices as the alternative for date- or state-bounded queries.

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?

State explicitly: 'For a date- or state-bounded question use search_layoff_notices.' This tells the agent when this tool is appropriate (employer-focused, all-time histories) and when to route elsewhere. The employer-centric framing in the first sentence further reinforces the intended use case.

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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