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universal_search

Scrape aggregated organic results from multiple search engines. Customize by country and language to retrieve relevant web data without managing proxies or parsing.

Instructions

Scrapes results from various search engines without worrying about proxy rotation and data parsing. Supports geographic targeting and language customization. [Credits: 20 API credits per successful request] Notes: Only 20 credits per successful request. Documentation does not name which specific search engine(s) are aggregated beyond 'various search engine results'. Returns: { organic_results: [ { title, displayed_link, snippet, date, missing[], link, extended_sitelinks: [ { title, link } ], rank } ] }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe search query to execute, just like a standard search.
countryNoCountry for the search using a two-letter country code (e.g., US, UK, FR). (default: us)
languageNoLanguage of the results. Possible values: en, es, fr, de, etc. (default: en)
Behavior3/5

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

No annotations provided, so the description carries the full burden. It discloses credit cost and notes that documentation does not name specific engines, which is transparent. However, it does not discuss rate limits, reliability, or handling of duplicates across engines. The return structure is given, adding some transparency.

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 efficiently structured: it states the core capability, then adds credit info and return format. It is front-loaded with the most important information. Could be slightly shorter if credit info were moved, but overall cohesive.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no output schema and moderate complexity (multiple engines, credit cost), the description provides a decent overview. It explains the return structure and credit usage. However, it lacks details on real-time vs cached results, how credits are consumed per query, and whether advanced operators are supported.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, but the description adds value by explaining query behavior ('just like a standard search') and providing examples for country and language parameters. It also emphasizes geographic targeting and language customization, which goes beyond the schema's generic descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it scrapes results from various search engines, handling proxy and parsing. This distinguishes it from sibling tools targeting specific engines (e.g., google_search). However, it could be more explicit that it aggregates multiple engines in one request.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus individual search tools. Lacks context about trade-offs or prerequisites. The description only says it supports geographic targeting, but does not help the agent decide between universal and specific search tools.

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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