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Heista

by co.heista

Web Search (general)

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Read-only

General-purpose web grounding via parallel.ai (Vercel AI Gateway). Returns synthesized text excerpts plus structured sources[] with direct URLs.

Use for: topic landscapes, entity-deep teardowns, recency-sharp queries, named-vendor lookups, general fact retrieval.

NOT for: Reddit/X/community discourse → use search_community. NOT for: numerical effect sizes or methodology-heavy fact-check → use search_research.

The agent decomposes the brief into sub-questions BEFORE calling — one focused query per call. Optional after_date (ISO YYYY-MM-DD) for fast-decay topics. Optional max_results 1-20, default 10.

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.
after_dateNoISO date (YYYY-MM-DD). Restrict results to content published after this date. 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).
max_resultsNoMaximum number of results to return. Default 10. Higher counts return more sources but cost more in tokens — keep at 10 for general use.

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already provide readOnlyHint=true and openWorldHint=true. Description adds context: backend (parallel.ai), return format (excerpts + URLs), and query decomposition expectation. No contradictions. Description adds value 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Approximately 120 words, well-structured with main purpose first, then use cases, exclusions, and parameter notes. Slightly verbose but efficient and front-loaded.

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?

No output schema exists, but description adequately explains return value (excerpts + sources with URLs). Combined with annotations (readOnly/openWorld) and param descriptions, the tool is well-understood. Could mention pagination or result count, 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?

Schema coverage is 100% with descriptions for all 3 parameters. Description reinforces and expands: 'after_date' for fast-decay topics, 'max_results' default 10 and cost implication, 'query' as single focused query. Adds meaningful context not in 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 states 'General-purpose web grounding' and specifies returning 'synthesized text excerpts plus structured sources[] with direct URLs'. It clearly distinguishes itself from sibling tools by listing what it is for and not for (e.g., NOT for community discourse → use search_community).

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 lists when to use: 'topic landscapes, entity-deep teardowns, recency-sharp queries, named-vendor lookups, general fact retrieval.' Also gives clear exclusions with specific alternative tools (search_community, search_research). Provides guidance on pre-decomposition of queries.

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