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LiquiLens — the Failure Radar

Corporate Transmission board (US)

corporate_transmission_board
Read-onlyIdempotent

Answers ONE question: whether FUNDING stress is reaching nonfinancial FIRMS — not banks (failure_radar_board), not households (household_credit_board), not money-market plumbing (the Seiche sibling server). Read the Corporate Transmission board for the US basin: is funding stress reaching nonfinancial firms? Channels from free public data (FRED keyless, AOUSC bankruptcy filings): the nonfinancial CP market (spread over bills + the book failing to roll), bank credit lines (C&I revolver drawdown + SLOOS standards tightening), the real-economy confirmation (claims, capex orders, inventories, openings, business bankruptcies), and a balance-sheet context channel capped at WATCH. Serves a TRANSMISSION verdict (counted co-occurrence of the funding and real sides) and a divergence read against the sibling bond board's served regime. Also carries a context-only Seiche Estuary/Oil read of upstream FX, materials, funding passage plus a bounded Ballast/Cushing/Brent-WTI echo; use Seiche's oil_funding_context for the full live-vs-reference oil architecture. used_in_regime and used_in_transmission are always false. Channels that cannot be read are listed in cannot_see, never reported as calm. Display-only: feeds no institution score and no watchlist tier. Returns ~4KB slim by default; pass full:true for thresholds, per-leg basis and method prose.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fullNoalso return method_note, thresholds and per-leg basis prose (larger payload); default false

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the readOnlyHint/idempotentHint annotations, the description discloses key behavioral details: 'Display-only: feeds no institution score and no watchlist tier', 'used_in_regime and used_in_transmission are always false', 'Channels that cannot be read are listed in cannot_see, never reported as calm', and the default vs full output payload. These are non-obvious traits that help the agent interpret results and side effects.

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 longer than a simple two-sentence summary, but each sentence contributes distinct information: the core question, channels covered, exclusions, behavioral caveats, and output format. It is front-loaded with the main purpose and avoids fluff, though slight redundancy exists ('Answers ONE question' vs 'Read the Corporate Transmission board...'). Overall well-structured for a complex dashboard tool.

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?

Despite having no output schema, the description thoroughly explains what the tool returns: a TRANSMISSION verdict, divergence read, cannot_see channel list, and context channels. It also covers default vs full output, flags that are always false, and how missing channels are reported. For a tool of this complexity, the description is self-sufficient.

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?

The only parameter 'full' is already fully documented in the input schema with its effect and default. The description mentions the same ('pass full:true for thresholds, per-leg basis and method prose') but adds no new meaning. Since schema coverage is 100%, the baseline of 3 applies.

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 with a precise statement of what the tool answers: 'whether FUNDING stress is reaching nonfinancial FIRMS' and explicitly differentiates from sibling tools ('not banks (failure_radar_board), not households (household_credit_board), not money-market plumbing'). This satisfies a specific verb+resource+scope and distinguishes from alternatives.

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?

The description explicitly states when to use the tool by naming what it covers and what it does not, and points to a specific alternative for oil context ('use Seiche's oil_funding_context for the full live-vs-reference oil architecture'). It also clarifies that this board is display-only and does not feed institution scores, informing whether it fits the user's need.

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

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct resource, sector, or function: sector-specific boards (corporate, household, crypto, stablecoin, failure radar), evidence details by region, verification, search, and review packet generation. Descriptions explicitly delineate boundaries, leaving no ambiguity about which tool to select.

Naming Consistency3/5

There are recognizable families (e.g., *_board for dashboards, evidence_* for validation records), but the set mixes conventions: noun-phrase boards, verb-first tools like universe_search and verify_published_record, and standalone nouns like forward_odds. This is readable but not uniform.

Tool Count4/5

17 tools is slightly above the ideal 3-15 range, but each tool has a distinct purpose and no redundancy. The count feels justified given the breadth of domains (India, US, Europe, crypto, stablecoins) and functions (monitoring, validation, verification, review).

Completeness5/5

The set covers the full workflow: universe_search for discovery, sector boards for monitoring, failure_radar_institution for deep dives, evidence_* for validation, forward_odds for probability context, verify_published_record for integrity, and institution_review_packet for human review. No obvious gaps or dead ends for the stated failure-radar domain.

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