Sofya

search

Search the web for current information on any topic. Returns extracted page content, not just snippets. Best for factual lookups, specific questions, or when you need a list of sources. For open-ended questions that need synthesis across many sources, use the research tool instead.

For news queries (current events, breaking news, politics, world events), set topic="news" to search news sources specifically. This returns recent articles with publication dates.

Set include_answer=true to get an AI-synthesized answer alongside results (adds 10 credits). This is the sweet spot for most agent tasks, e.g. basic + include_answer = 12 credits, much cheaper than a full 50-credit research call.

Returns: query, answer (if requested), results (array of {title, url, content, description, fetched, published_date}), search_depth, topic, elapsed_ms, credits_used, credits_remaining, altered_query, relaxed_query (set when the query matched nothing and was retried once with its site: operator, else its quotes, removed - the results answer that looser query).

Args: query: The search query search_depth: "basic" (default) for extracted page content (2 credits), "snippets" for SERP snippets only without page fetching (1 credit) max_results: Number of results (default 10, max 20) include_answer: Generate an AI answer that synthesizes the search results (adds 10 credits) include_domains: Only include results from these domains (max 10) exclude_domains: Exclude results from these domains (max 10) topic: "general" for web search, "news" for news articles. use "news" for current events, breaking news, politics, or any time-sensitive query freshness: Filter by recency - "day", "week", "month", "year", or "YYYY-MM-DD:YYYY-MM-DD"

Input Schema

TableJSON Schema

Name	Required	Default
`query`	Yes
`topic`	No	general
`freshness`	No
`max_results`	No
`search_depth`	No	basic
`include_answer`	No
`exclude_domains`	No
`include_domains`	No

TDQS

A5/5.0

Behavior5/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It covers costs, credit differences between search_depth values, the include_answer behavior and credit cost, the altered_query field, the array of result fields, and what news mode returns. This is comprehensive behavioral transparency.

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 long but every sentence carries practical weight. It front-loads the tool's core purpose, then organizes returns and parameters into clearly labeled sections without repetition or filler.

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?

Given 8 parameters, no output schema, and no annotations, the description is remarkably complete. It specifies the return object, parameter defaults, allowed values, cost behavior, and use-case routing, so an agent has everything needed to invoke the tool 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?

Schema description coverage is 0%, but the description fully documents every parameter: query, search_depth values with credit implications, max_results defaults and maximums, include_answer behavior, domain filtering limits, topic values, and freshness formats. It adds substantial meaning beyond the bare 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 opens with a specific verb and resource: 'Search the web for current information on any topic.' It also makes the deliverable explicit ('Returns extracted page content, not just snippets') and distinguishes itself from the sibling research tool by naming it directly.

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?

Usage guidance is explicit: it is 'best for factual lookups, specific questions, or when you need a list of sources,' and it tells the agent to use the research tool for open-ended questions needing synthesis. It also provides concrete guidance for news queries and when to set topic='news'.

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

A4.9/5.0

Disambiguation5/5

Each tool serves a clearly distinct purpose: search for web queries, fetch for raw page content, extract for structured data extraction, and research for comprehensive synthesis. Descriptions are detailed enough to prevent confusion.

Naming Consistency5/5

All tool names are single lowercase verbs (extract, fetch, research, search), following a consistent and predictable pattern. No mixing of styles or non-standard conventions.

Tool Count5/5

With 4 tools, the set covers the core needs of web information retrieval and research without being bloated. Each tool earns its place, and the count is ideal for the domain.

Completeness5/5

The tool surface provides a complete workflow: search to find sources, fetch to retrieve full content, extract to pull specific data, and research to synthesize multiple sources. No obvious gaps for typical agent tasks.

Resources

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