AnySearch

batch_search

Read-onlyDestructive

This is Anysearch's parallel search tool. Parallel search — run multiple Anysearch queries in a single call. Prefer this over multiple sequential calls when you have 2–5 queries. Saves context space and returns all results at once. Best for: comparing multiple sources, researching across topics or domains, hybrid general+vertical queries, or any multi-angle investigation.

When to use

Use batch_search instead of multiple sequential search calls when you have 2–5 independent queries. 🏆 PRIMARY use case: After get_sub_domains(domains=[...]) returns sub_domains across multiple domains, use batch_search to send one query per sub_domain in parallel. This is more efficient than sequential per-domain search calls. Also useful for ambiguous / fuzzy queries within a single domain: after get_sub_domains, use batch_search to explore multiple sub_domains in parallel.

Constraints

Maximum 5 queries per call
Each query item follows the search tool parameter structure (query is required; domain, sub_domain, sub_domain_params are optional. For general queries, omit all domain fields. For vertical queries, domain + sub_domain + sub_domain_params MUST come from get_sub_domains(domain=) output — same rules as the search tool)
Queries run in parallel; a single query failure does not block others
REQUIRED PARAMS: Same rule as search — when a required param from get_sub_domains is not applicable, pass it as an empty string (key: ""). Never skip required params.

Examples

Single-domain batch (multiple sub_domains)

Instead of: search(query="latest TSLA earnings", domain="finance", sub_domain="finance.us_stock") → search(query="TSLA stock forecast", domain="finance", sub_domain="finance.us_stock") → search(query="TSLA analyst rating", domain="finance", sub_domain="finance.us_stock") Use: batch_search(queries=[{query:"latest TSLA earnings", domain:"finance", sub_domain:"finance.us_stock"}, {query:"TSLA stock forecast", domain:"finance", sub_domain:"finance.us_stock"}, {query:"TSLA analyst rating", domain:"finance", sub_domain:"finance.us_stock"}])

Multi-domain batch (after get_sub_domains with multiple domains)

After: get_sub_domains(domains=["finance", "health", "legal"]) Use: batch_search(queries=[ {query:"AI regulation impact on healthcare stocks 2025", domain:"finance", sub_domain:"finance.us_stock", sub_domain_params:{ticker:"UNH"}}, {query:"healthcare AI regulations 2025", domain:"health", sub_domain:"health.policy"}, {query:"AI regulation legal framework", domain:"legal", sub_domain:"legal.legislation"}])

Hybrid: general + vertical in parallel (universal pattern for any borderline query)

Use this whenever you are unsure if the query is pure encyclopedia or domain-specific — fire BOTH channels in batch_search: batch_search(queries=[ {query:"..."}, // general — no domain {query:"...", domain:"...", sub_domain:"..."}]) // vertical channel(s) This applies universally: classical texts, financial concepts, legal theories, historical events, scientific discoveries, medical topics — any query where domain knowledge could enrich the encyclopedia answer.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`queries`	Yes	Array of search requests (max 5). Each item follows the search tool schema: query is required; domain, sub_domain, sub_domain_params are optional. For general queries, omit all domain fields. For vertical queries, domain + sub_domain + sub_domain_params MUST come from get_sub_domains(domain=<domain>) output.

TDQS

A4.7/5.0

Behavior4/5

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

The description adds useful behavior beyond annotations: queries run in parallel, a single query failure does not block others, and results are returned all at once. While annotations already indicate read-only, the description provides execution context. No contradiction with the provided 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?

The description is well-structured with clear headings and front-loaded purpose, but it is quite long with repeated examples and emphasis. It earns its place for a complex tool, yet could be tightened without losing clarity.

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?

Despite having no output schema, the description is thorough: it covers the trigger conditions, constraints, parameter rules, and provides comprehensive examples. It also states the return style ('returns all results at once') and failure isolation, making it sufficient for an agent to invoke correctly.

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?

Although the schema only says 'array of search requests,' the description defines the internal structure in depth: required vs optional fields, where domain/sub_domain values must come from get_sub_domains, and the empty-string rule for required params. Multiple concrete examples illustrate general, vertical, and hybrid query patterns.

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?

Opens with a clear, specific statement: 'Parallel search — run multiple Anysearch queries in a single call.' This distinctly identifies the tool's function and differentiates it from sequential search. The resource (search queries) and action (batch) are immediately clear.

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?

Includes an explicit 'When to use' section that says to prefer batch_search over multiple sequential calls for 2–5 independent queries. It names the primary use case (after get_sub_domains) and the alternative (sequential search), and provides constraints like the maximum of 5 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

A4.4/5.0

Disambiguation4/5

Each tool serves a clearly distinct role in the search pipeline: get_sub_domains (discovery), search (single query), batch_search (parallel queries), extract (content retrieval). The dependency chain between them is explicit and well-documented. Minor ambiguity exists between search and batch_search since batch can technically serve single queries, and the overwhelmingly verbose descriptions could theoretically cause an agent to misfire, but the use cases are clearly separated.

Naming Consistency3/5

The naming convention is mixed: bare verbs (search, extract) coexist with verb_noun compounds (get_sub_domains) and compound constructs (batch_search: modifier + action). The names are short, readable, and self-descriptive individually, and the search/batch_search relationship is evident. However, there's no single consistent pattern across all four, with no consistent verb prefix or consistent noun-phrase structure.

Tool Count5/5

At 4 tools, this is tightly scoped for a search server—each tool earns its place and serves as one stage in the lookup workflow. The count is well within the ideal 3–15 range, and there's no bloat or redundant surface area.

Completeness4/5

The lifecycle is complete for a search offering: discover (get_sub_domains) → query (search/batch_search) → deep dive (extract). It covers both single and parallel execution paths and handles general versus vertical routing. Minor gaps include no pagination or result-caching mechanism, and the dependency on get_sub_domains before vertical search could create an extra round-trip, but the core workflow has no dead ends.

Resources

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