latest_articleRead today's exact full-text LiquiLens editorial: current institution-risk analysis when the evidence moved, or a labelled historical replay when it did not. Returns the canonical headline, dek, Markdown, evidence clock and passing publication receipt; quote it without regenerating facts.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnlyHint and idempotentHint already provided, the description adds valuable behavioral context: it describes the conditional behavior ('current institution-risk analysis when the evidence moved, or a labelled historical replay when it did not'), the specific return fields (headline, dek, Markdown, evidence clock, passing publication receipt), and the invariant 'quote it without regenerating facts.' This goes well beyond the annotations and fully discloses the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, tightly packed with only essential information. It front-loads the action and resource, then lists return values and key behavioral notes. No filler or redundancy; every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description fully enumerates what is returned (canonical headline, dek, Markdown, evidence clock, passing publication receipt) and explains the conditional historical replay behavior. With zero parameters, there are no input-side gaps. The description is complete for an agent to select and invoke the tool without ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters, so the baseline is 4. The description does not need to explain parameter semantics, and the input schema is empty (100% coverage). The description adds an implicit 'today' time frame, but since there is no input, no further clarification is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Read') and a specific resource ('today's exact full-text LiquiLens editorial'), clearly distinguishing this tool from siblings like evidence_institution or institution_review_packet by emphasizing it is the latest evidence-led article with a canonical status. It explicitly mentions returning the 'canonical headline, dek, Markdown' etc., which further pins down its purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: when you need today's editorial and when you need either a current institution-risk analysis (if evidence moved) or a labelled historical replay (if it did not). It does not explicitly name alternatives or exclusions, but the specificity of 'today's exact full-text editorial' implies when it should be chosen over siblings.
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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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.
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.
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).
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.