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LiquiLens — the Failure Radar

Failure Radar institution dossier

failure_radar_institution
Read-onlyIdempotent

Read the full radar dossier for one Indian institution: per-quarter score and failure-PD trajectory with named drivers, RBI PCA/SAF headroom history, the funding-fragility read, forensic-screen evidence, and the market reading for listed names. Call failure_radar_board first to discover valid slugs; an institution without a vetted dossier is reported absent, never scored from memory.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYeskebab-case institution slug from a failure_radar_board row, e.g. 'esaf-sfb'

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description's role is lighter. It adds behavioral context about absent dossiers being reported as absent and not scored from memory, which is useful but does not go beyond what annotations indicate.

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 a single, well-structured sentence that front-loads the main action and list of outputs. Every clause adds value, with no wasted words.

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 tool with one parameter and no output schema, the description provides enough context: what the dossier contains, how to call it, and what happens for missing entries. It also references the prerequisite tool. Minimal gaps.

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?

The only parameter 'slug' has 100% schema coverage with a clear description and example. The description adds context by linking it to 'failure_radar_board rows', which enhances understanding beyond the schema.

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 uses the specific verb 'Read' and identifies the resource as 'the full radar dossier for one Indian institution', listing detailed components (per-quarter score, failure-PD trajectory, etc.). It distinguishes from the sibling tool 'failure_radar_board' by instructing to call that first for valid slugs.

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?

Explicitly instructs to call 'failure_radar_board first to discover valid slugs', and states that an institution without a vetted dossier is reported absent and never scored from memory. This provides clear when-to-use and expected outcomes.

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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