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tengu_v3_workforce_layoff_screen

CROSS-SECTIONAL LAYOFF SCREEN — which listed companies filed WARN-Act layoff notices in the last N days, ranked by employees noticed. One row per ticker (company, filing count, employees noticed, first/latest event date, states touched, largest single filing) across the whole listed universe. Call it for 'who is cutting right now' — notices are filed weeks before the cut, so this surfaces restructurings before the press release. Only employers matched to a listed symbol appear; most WARN filers are private and are excluded by design. For one company's filings use /workforce/layoffs/{ticker}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
limitNo
stateNo
min_employeesNo

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full burden of disclosing behavior. It explains the data source (WARN-Act), the aggregation (one row per ticker), the output fields, and the built-in limitation (only listed symbols). It adds the forward-looking insight about notices filed weeks before the cut, but does not explicitly state read-only semantics or pagination behavior.

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 concise and well-structured, with the main purpose front-loaded in caps. Every sentence earns its place: purpose, output format, usage context, caveat, and alternative reference. No wasted words.

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?

Despite no output schema, the description effectively summarizes the return values (one row per ticker with specific fields). It provides key context about data source, universe coverage, and ranking. It falls short only in not explaining all filter parameters, which are part of the input schema.

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

Parameters2/5

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

Schema description coverage is 0% and the description only indirectly references the 'days' parameter via 'last N days'. It does not explain the 'state', 'limit', or 'min_employees' parameters, leaving the agent to rely on parameter names alone. Since the schema has no per-parameter descriptions, the description should have compensated for this gap but does not.

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's function with a specific verb ('screen') and resource ('listed companies' WARN-Act layoff notices). It distinguishes itself from the per-company sibling by explicitly pointing to '/workforce/layoffs/{ticker}' for single-company filings.

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 provides explicit when-to-use guidance ('call it for who is cutting right now') and explains the lead-time advantage. It also gives an explicit alternative for single-company filings and alerts the user to the intentional exclusion of private companies.

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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TDQS

C2.9/5.0
Disambiguation2/5

With 336 tools, there is substantial overlap. Over a dozen health/status tools share nearly identical 'is the system healthy?' descriptions (e.g., tengu_status, tengu_ready, tengu_ml_health, tengu_v3_system_health, tengu_v3_stream_status), and multiple single-ticker analysis (tengu_ml_predict, tengu_copilot_score_ticker, tengu_v3_intel_ml_prediction) and top-picks (tengu_copilot_top_picks, tengu_ml_top_picks, tengu_v3_trade_setups) tools have poorly defined boundaries. Agents would frequently misselect.

Naming Consistency2/5

The server mixes no-version (tengu_crypto), v2 (tengu_v2_drift), v3 (tengu_v3_intel_*), and copilot (tengu_copilot_*) families, and within families there is inconsistent verb/noun ordering (tengu_v3_research_fetch_url vs tengu_v3_news_summary). While subfamilies like tengu_v3_private_markets_* are internally consistent, the overall naming pattern is chaotic and unpredictable.

Tool Count1/5

336 tools is far beyond any reasonable tool set size, even for an all-in-one financial data platform. This extreme count creates choice paralysis, high latency in tool selection, and makes the server effectively unusable for autonomous agents. The calibration guideline marks 50+ as extreme; this is nearly 7x that threshold.

Completeness4/5

The platform covers a vast domain: equity and crypto prices, fundamentals, insider trading, options, news (including crypto and FX), private markets, streaming data, risk metrics, and execution planning. There are minor gaps (no direct multi-ticker comparison tool, no order placement), but the surface is remarkably comprehensive for an analysis-focused server.