LiquiLens — the Failure Radar

by io.github.beepboop2025

Failure Radar board (India)

failure_radar_board

Read-onlyIdempotent

Read the live Failure Radar board for India: one row per institution with a fresh vetted dossier — banks, small finance banks, co-operative banks, NBFCs, MFIs and HFCs. Each row carries a corpus-calibrated failure PD term structure (12/24/36 months), a disclosure score, RBI PCA/SAF action-zone status, a funding-fragility index, market-implied distance-to-default for listed names, and a watchlist tier assigned under a published rule. Takes no arguments. Call this first to discover institution slugs, then failure_radar_institution for one name's full dossier. Outputs are research screens, not credit ratings.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already mark readOnlyHint and idempotentHint. The description adds that it takes no arguments and outputs are research screens, not credit ratings, providing useful behavioral context beyond annotations.

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?

Concise yet rich: one sentence on purpose, one listing fields, one on usage, one on output nature. Front-loaded with key information, 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?

Given no output schema, the description details the output fields comprehensively (risk metrics, statuses). Could mention row count or sort order, but overall sufficient.

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

Parameters5/5

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

No parameters, and schema coverage is 100%. The description fully compensates by explaining the purpose and output fields in detail.

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 identifies the tool as reading the live Failure Radar board for India, listing institutions with various metrics. It explicitly contrasts with the sibling 'failure_radar_institution' for individual dossiers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear guidance to call this first to discover institution slugs, then use failure_radar_institution for details. Lacks explicit when-not-to-use context but is sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All LiquiLens — the Failure Radar tools Inspect

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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.

Resources

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