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срезAI — Search API for AI agents

Answer Search — RAG с семантическим ранжированием

answer_search
Read-only

Полный RAG-цикл: поиск источников, семантическое ранжирование через BGE-reranker-v2-m3, чтение релевантных страниц и синтез финального ответа. Возвращает готовый ответ с указанием релевантности каждого источника (0–1).

Когда: нужен итоговый ответ на вопрос, а не список ссылок. Лучше web_search + read_url, когда требуется интерпретация и синтез по нескольким источникам. Конвейер: переформулировка запроса → параллельный поиск → BGE-reranker отбирает топ → чтение страниц → синтез ответа. Возвращает: текст ответа + sources (каждый с relevance, chars_read, read_success). Источники всегда отсортированы по убыванию relevance. Цена зависит от depth: fast — 50 кредитов, balanced — 100 кредитов, deep — 200 кредитов.

Full RAG cycle: search, semantic reranking via BGE-reranker-v2-m3, page reading, answer synthesis. Returns a ready answer with relevance scores (0–1) for each source.

Use when: you need a final answer, not a list of links. Better than web_search + read_url when interpretation and synthesis across sources is required. Pipeline: query rewriting → parallel search → BGE-reranker picks top results → page reading → answer synthesis. Returns: answer text + sources (each with relevance, chars_read, read_success). Sources are always sorted by descending relevance. Cost depends on depth: fast — 50 credits, balanced — 100 credits, deep — 200 credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoГлубина: fast (2 страницы), balanced (5 страниц, по умолчанию), deep (10 страниц). Больше страниц — выше качество, дольше время. / Depth: fast (2 pages), balanced (5 pages, default), deep (10 pages). More pages = higher quality, longer time.balanced
queryYesВопрос или поисковый запрос / Question or search query
languageNoЯзык поиска и ответа / Search and answer language: auto, ru, enauto

Schema Changelog

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

  1. Changed6 schema fields changed
    • addedInput schema / properties / depth / default
      Added value: +"balanced"
    • changedInput schema / properties / depth / description
      Previous value: -"Глубина поиска / Search depth: fast=2 pages, balanced=5, deep=10"New value: +"Глубина: fast (2 страницы), balanced (5 страниц, по умолчанию), deep (10 страниц). Больше страниц — выше качество, дольше время. / Depth: fast (2 pages), balanced (5 pages, default), deep (10 pages). More pages = higher quality, longer time."
    • addedInput schema / properties / language / default
      Added value: +"auto"
    • changedInput schema / properties / language / description
      Previous value: -"Язык поиска / Search language (default: auto)"New value: +"Язык поиска и ответа / Search and answer language: auto, ru, en"
    • changedInput schema / properties / query / description
      Previous value: -"Вопрос или задача / Question or search task"New value: +"Вопрос или поисковый запрос / Question or search query"
    • changedInput schema / properties / query / maxLength
      Previous value: -500New value: +2000
  2. Added
  3. Removed
  4. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnly and non-destructive behavior, and the description adds valuable context beyond those hints: the pipeline steps, source sorting by descending relevance, per-source fields like chars_read and read_success, and credit cost dependence on depth. No contradiction with annotations is present.

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 labeled sections: when to use, pipeline, returns, and cost. It is front-loaded with the purpose, but the full bilingual repetition in both Russian and English adds length; still, each section earns its place and remains 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 no output schema present, the description fully explains return values: answer text plus sources with relevance, chars_read, and read_success, and the guarantee of descending relevance order. It also covers depth semantics and cost, making it complete 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.

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3; the description adds extra meaning by explaining the cost implications of depth (50/100/200 credits) and framing query as a question to be synthesized. This supplements rather than merely repeats the 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 a specific verb and resource: a full RAG cycle performing search, semantic reranking via BGE-reranker-v2-m3, page reading, and answer synthesis. It clearly distinguishes itself from siblings by emphasizing that it returns a ready answer rather than a list of links.

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?

Explicit use-when guidance is present: 'нужен итоговый ответ на вопрос, а не список ссылок' and explicitly names web_search + read_url as the alternative when synthesis across multiple sources is not needed. This gives the agent actionable routing criteria.

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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