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Sofya

by co.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
NameRequiredDescriptionDefault
queryYes
topicNogeneral
freshnessNo
max_resultsNo
search_depthNobasic
include_answerNo
exclude_domainsNo
include_domainsNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

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 full responsibility; it delivers by describing extracted content, optional AI synthesis, credit costs, result shape, and the relaxed_query retry behavior when the original query matches nothing. This is well beyond minimal labeling.

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 section has a purpose: purpose/usage intro, news guidance, cost guidance, return structure, and parameter details. Key guidance is front-loaded and formatting is scannable.

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?

With 8 parameters, no output schema, and no annotations, the description covers the return object fields, parameter semantics, costs, and fallback behavior. It is complete enough for an agent to select and call the tool correctly without further lookup.

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 Args section fully compensates by explaining every parameter, including defaults, choices for search_depth and topic, freshness formats, domain count limits, and cost implications. This transforms bare schema fields into actionable parameters.

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?

Clearly states the tool performs a web search for current information and returns extracted page content rather than snippets. The 'Best for factual lookups, specific questions...' framing and reference to the research tool identify its niche among siblings.

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 says use search for factual lookups, specific questions, or lists of sources, and use research for open-ended synthesis questions. It also gives concrete conditions for choosing topic='news', so an agent knows exactly when to invoke this tool and when not to.

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