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Heista

by co.heista

Research Search (structured fact-check + numerical)

search_research
Read-only

Structured fact-check + numerical research via Perplexity Sonar Reasoning Pro (Gateway-routed). Returns synthesized answer text plus structured sources[] with direct URLs to primary sources.

Use for: specific numerical claims with methodology context, fact-check against primary sources, effect sizes + confidence intervals, earnings transcripts / SEC filings / research papers.

Per Phase 3.5 empirical A/B: 2-3× cheaper than sonar-pro with comparable or better quality on structured research. Real Meta IR press releases + earnings transcripts on Desk. 17 cites on Quant.

NOT for: Reddit/X/community → use search_community. NOT for: broad topic landscapes → use search.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesResearch query. Phrase as a precise factual question — what number, what claim, what methodology. The research agent has already decomposed the brief; this is one focused query.
recency_filterNoLimit results to content published within this window. Use for recent earnings, recent regulatory filings, recent industry reports.
search_domain_filterNoRestrict to these domains (e.g. ["investor.atmeta.com", "sec.gov"]). Use when triangulating against known T1 sources or specific authoritative publications.

TDQS

A4.6/5.0
Behavior5/5

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

The description adds behavioral details beyond annotations: it uses Perplexity Sonar Reasoning Pro, returns primary source URLs, mentions empirical A/B cost and quality comparisons, and specifies routing via Gateway. Annotations (readOnlyHint, openWorldHint, idempotentHint) are consistent.

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 compact (4 sentences) with front-loaded core purpose, bullet-like use cases, and clear exclusion guidelines. Every sentence adds value without redundancy.

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 the absence of an output schema, the description adequately outlines the return format (synthesized answer text + structured sources with URLs). It covers primary use cases and limitations, but lacks details on pagination, error handling, or response size limits.

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?

Schema coverage is 100% so the schema already documents all parameters well. The description adds only a brief suggestion on query phrasing ('phrase as a precise factual question'), which is helpful but marginal. No additional detail on recency_filter or search_domain_filter beyond 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 clearly states the tool performs 'structured fact-check + numerical research' via a specific model, and specifies output categories. It distinguishes itself from siblings by explicitly listing what it is for and not for, and naming alternative tools.

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 provides explicit 'Use for' and 'NOT for' sections with concrete use cases (numerical claims, fact-check, earnings transcripts) and alternative tools (search_community for Reddit/X, search for broad topics). Also mentions cost advantage.

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

A3.7/5.0
Disambiguation4/5

Despite the high tool count, most tools have distinct purposes with thorough descriptions that specify when to use each. Some overlap exists among creative direction tools (call_creative_worlds vs chat_with_creative_worlds), but the descriptions clarify usage patterns.

Naming Consistency3/5

Naming conventions are inconsistent overall: some follow verb_noun (create_powersource_url, decode_ad), others use noun_verb or compound names (adformula_intelligence, fleet_analytics_overview). However, subgroups like dispatch_* and list_*_presets maintain internal consistency.

Tool Count2/5

112 tools is far beyond the typical 3-15 range for well-scoped servers. While the server covers a broad domain, the sheer number likely overwhelms agents and suggests insufficient consolidation of related operations.

Completeness4/5

The tool set covers core creative intelligence workflows: brand analysis, ad decoding, script generation, creative direction, and research. Minor gaps exist (e.g., no social media publishing tools), but the main use cases are well-supported.

Resources