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APVentureEngine

warn-mcp

by APVentureEngine
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

agency_revisions

Track changes between daily WARN notice builds: see amendments, absent or returned notices, filtered by state, company, or change type, with cause classification.

Instructions

Change log of the dataset itself: what differed between consecutive daily builds of the WARN notice table, since 2026-08-31. Each change is one of 'amended' (a tracked field of an existing notice changed — employees_affected, effective_date, location, notice_type, company, notice_date), 'row_absent' (a notice stopped appearing) or 'row_returned' (an absent notice came back). A change can come from the state agency amending or withdrawing a notice OR from an improvement to this project's parser; the log records the observation, never the cause, and cause_class labels only what is mechanically distinguishable (field_populated, field_cleared, format_only, value_changed, unknown). Filters combine with AND. Returns matched_changes, by_change_type, by_cause_class, log_covers_from/to, and up to limit changes newest observed_date first, each with id, state, company, notice_date, change_type, cause_class, field, old_value, new_value. Not a search for notices: use search_layoff_notices for notices and employer_layoff_history for one employer's full record.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax changes returned, 1-200 (default 25). matched_changes always reports the full count.
sinceNoEarliest observed_date (the build date on which the change was seen), YYYY-MM-DD. The log starts 2026-08-31; earlier dates return everything.
stateNoTwo-letter state code, e.g. 'CA'.
companyNoEmployer 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.
change_typeNoExactly one of 'amended', 'row_absent', 'row_returned'. Omit for all three.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed6 schema fields changedv0.2.4
    • changedInput schema / properties / change_type / description
      Previous value: -"e.g. 'row_absent', 'field_changed', 'row_new'."New value: +"Exactly one of 'amended', 'row_absent', 'row_returned'. Omit for all three."
    • addedInput schema / properties / change_type / enum
      Added value: +[
+  "amended",
+  "row_absent",
+  "row_returned"
+]
    • changedInput schema / properties / company / description
      Previous value: -"Substring of the employer name."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."
    • changedInput schema / properties / limit / description
      Previous value: -"Max changes returned, 1-200 (default 25)."New value: +"Max changes returned, 1-200 (default 25). matched_changes always reports the full count."
    • changedInput schema / properties / since / description
      Previous value: -"Earliest observed_date, YYYY-MM-DD."New value: +"Earliest observed_date (the build date on which the change was seen), YYYY-MM-DD. The log starts 2026-08-31; earlier dates return everything."
    • changedInput schema / properties / state / description
      Previous value: -"Two-letter state code."New value: +"Two-letter state code, e.g. 'CA'."
  2. First observedv0.1.0

TDQS

A4.8/5.0
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 it excels. It discloses that the log records observations, never causes, explains what cause_class can and cannot distinguish, states the log's start date, and specifies the exact return shape and ordering. This is far richer than the typical read-only tool description.

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 dense and every sentence earns its place, but it is a single ~200-word paragraph with no visual structure. It front-loads the core purpose and saves sibling routing for the end, but bullet points or short paragraphs would improve scannability without losing content.

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?

For a read-only log query with no output schema, the description covers the full return contract (matched_changes, by_change_type, by_cause_class, log_covers_from/to, per-change fields), the meaning of each change type, filter semantics, and alternative tools. Nothing an agent needs to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds cross-parameter semantics not in any single schema field: 'Filters combine with AND', 'up to `limit` changes newest observed_date first', and the conceptual distinction between matched_changes count and returned items. That extra context justifies a 4.

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 opens by defining the tool as the dataset's own change log and enumerates the exact change types ('amended', 'row_absent', 'row_returned'). It closes with explicit sibling differentiation: 'use search_layoff_notices for notices and employer_layoff_history for one employer's full record.' This makes the purpose concrete and instantly distinguishable from all five sibling tools.

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?

It gives explicit when-not guidance and names the alternatives: 'Not a search for notices: use search_layoff_notices for notices and employer_layoff_history for one employer's full record.' It also clarifies filter combination ('Filters combine with AND'), default limit behavior, and ordering, leaving no ambiguity about when to invoke this tool.

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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