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by co.heista

Perplexity Sonar Pro Search

perplexity_search
Read-only

Web-grounded search via Perplexity Sonar Pro. Returns synthesized answer text plus a structured sources[] array (url + title) the caller can evaluate per the research.foundation four-tier source ladder. Optional recency_filter (hour/day/week/month/year) for fast-decay topics. Optional search_domain_filter (up to 10 domains) for triangulating against known-authoritative sources.

Use this whenever a specialist needs current, web-grounded information — landscape scans, trend research, evidence queries, counter-evidence checks, named-entity lookups. Pair with the research.foundation skill (always-on craft baseline) and the research.methodologies.desk-synthesis skill (6-phase workflow) for production-grade output.

The agent decomposes the brief into sub-questions BEFORE calling this — one focused query per call, not a multi-question batch. Cost is real (~$0.005-0.015 per query); the agent should budget calls per research.foundation §6 (fact-check 1-3, single comparison 3-8, landscape scan 8-20).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query. Phrase as a natural-language question or precise topic description. The research agent has already done question decomposition — this is one focused query, not a multi-question batch.
recency_filterNoLimit results to content published within this window. Use for fast-decay topics (model capabilities, platform algo changes, ad-format performance) per research.foundation §5. Omit for slow-decay topics (buyer psychology, established frameworks).
search_domain_filterNoRestrict search to these domains (e.g. ["motionapp.com", "about.fb.com"]). Use when triangulating against known-authoritative sources, or when evidence-querying for a specific named brand/publication.

TDQS

A4.6/5.0
Behavior4/5

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

The description adds behavioral details beyond annotations (readOnlyHint=true): it mentions cost ($0.005-0.015 per query), budgeting guidance, and the need for question decomposition before calling. These are not covered by annotations and help the agent use the tool responsibly. No contradictions with 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?

The description is well-structured and concise given the amount of information. It starts with the core purpose, then lists optional parameters, followed by usage guidelines and costing. Every sentence provides unique 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?

The description covers all aspects needed for proper tool use: purpose, parameters, output format (synthesized answer + sources), usage context, cost, and integration with other skills. Despite having no output schema, the description adequately describes the return structure. It is complete for a search tool of this complexity.

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?

All three parameters are fully described in the schema (100% coverage). The description enriches each parameter: for 'query', it reinforces that it should be a single focused query after decomposition; for 'recency_filter', it explains when to use it (fast-decay topics) vs. omit; for 'search_domain_filter', it provides triangulation use cases. This adds significant value 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 specifies the tool's action ('Web-grounded search via Perplexity Sonar Pro') and its output ('synthesized answer text plus a structured sources[] array'). It clearly differentiates from sibling tools like 'search' and 'search_research' by naming the specific service and use cases.

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?

The description explicitly states when to use the tool ('whenever a specialist needs current, web-grounded information') and lists concrete scenarios. It also references complementary skills and research methodologies. While it does not explicitly state when not to use it, the context is clear enough for an agent to decide.

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