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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 - 3 кредита, balanced - 5 кредитов, deep - 10 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 - 3 credits, balanced - 5 credits, deep - 10 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.

  1. Changed1 schema field changed
    • changedInput schema / properties / depth / description
      Previous 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."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."
  2. 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
  3. Added
  4. Removed
  5. Added

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnly/openWorld/idempotent=false/destructive=false, so the safety profile is covered. The description adds genuinely new behavioral context beyond that: the five-stage pipeline, the fact that sources are always sorted by descending relevance, and a per-depth credit cost table. It does not mention latency, caching, or failure modes, keeping it short of a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Content is well front-loaded into labeled sections (When, Pipeline, Returns, Cost), but the entire description is duplicated verbatim in Russian and English, roughly doubling the token footprint with zero added information for a given reader.

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, the description fully carries the return-value burden, describing the answer text plus sources with relevance, chars_read, and read_success, and stating the sort order. Nothing an agent needs to call or interpret the result is missing.

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 schema already documents all three parameters and enum values, giving a baseline of 3. The description adds value by mapping each depth value to a credit cost (fast 3, balanced 5, deep 10), which is not in the schema and directly informs parameter choice.

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?

States a specific verb and resource ('Полный RAG-цикл: поиск... синтез финального ответа') and explicitly names the sibling alternatives ('Лучше web_search + read_url'), so an agent can distinguish it from the link-returning tools without opening any schema.

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?

Provides an explicit when clause ('нужен итоговый ответ на вопрос, а не список ссылок') plus the condition that selects the alternative ('когда требуется интерпретация и синтез по нескольким источникам'). Both the trigger and the exclusion are stated.

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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