срезAI — Search API for AI agents

Глубокое исследование / Deep research

deep_research

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Проводит многошаговое исследование: сам формулирует запросы, ищет, читает источники и возвращает готовый связный ответ со ссылками на использованные страницы.

Когда: вопрос требует сопоставления нескольких источников и вывода — «сравни», «разберись», «что известно о». Когда не: нужен один факт или список ссылок — это web_search, он в 20 раз дешевле и отвечает за секунды. Как это работает: исследование идёт 1–15 минут — дольше, чем живёт одно соединение. Поэтому инструмент запускает задачу и ждёт до ~90 секунд. Если за это время ответ готов — возвращает его сразу. Если нет — возвращает строку с пометкой [research_pending] и талоном (ticket). Тогда вызовите этот же инструмент ещё раз, передав ticket (query можно не повторять), чтобы дождаться готового результата; повторяйте, пока не придёт ответ. Такой дозабор по талону НЕ списывается заново — деньги берутся один раз, за саму задачу. Возвращает: текст ответа плюс список источников. Если источники не вернулись, в ответе будет предупреждение — такой вывод не считается проверенным. Цена: 20 кредитов плюс 3 за каждую 1000 токенов ответа — самый дорогой вызов. Списывается один раз за задачу; повтор ТЕМЫ (новый query) — новая задача и новое списание, а дозабор по ticket бесплатен.

Runs multi-step research: forms its own queries, searches, reads sources and returns a finished answer with links to the pages it used.

Use when: the question needs several sources reconciled into a conclusion — "compare", "analyse", "what is known about". Do not use when: you need a single fact or a list of links — that is web_search, 20× cheaper and seconds fast. How it works: research takes 1–15 minutes — longer than a single connection lives. So the tool starts the job and waits up to ~90 seconds. If the answer is ready by then, it returns it. If not, it returns a string marked [research_pending] with a ticket. Call this same tool again passing that ticket (you may omit query) to wait for the finished result; repeat until it arrives. Polling by ticket is NOT charged again — you pay once, for the job itself. Returns: the answer text plus a source list. If no sources came back the response says so — treat that output as unverified. Cost: 20 credits plus 3 per 1000 output tokens — the most expensive call. Charged once per job; repeating the TOPIC (a new query) is a new job and a new charge, while polling by ticket is free.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`query`	No	Вопрос или тема исследования. Обязателен для НОВОГО исследования; при дозаборе по ticket не нужен. / The question or research topic. Required to START research; omit it when polling by ticket.
`ticket`	No	Талон незавершённой задачи из прошлого ответа `[research_pending]`. Передайте его, чтобы дождаться готового результата — бесплатно, без нового списания. / Ticket of an unfinished job from a previous `[research_pending]` response. Pass it to wait for the finished result — free, no new charge.

Schema Changelog

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

Changed1 schema field changedAug 25, 2026
- changedInput schema / properties / ticket / type
```
Previous value: -"string"New value: +[
+  "string",
+  "number"
+]
```

Changed4 schema fields changedAug 25, 2026

changedInput schema / properties / query / description

Previous value: -"Вопрос или тема исследования / The question or research topic"New value: +"Вопрос или тема исследования. Обязателен для НОВОГО исследования; при дозаборе по ticket не нужен. / The question or research topic. Required to START research; omit it when polling by ticket."

removedInput schema / properties / query / minLength
```
Removed value: -1
```

addedInput schema / properties / ticket

Added value: +{
+  "description": "Талон незавершённой задачи из прошлого ответа `[research_pending]`. Передайте его, чтобы дождаться готового результата — бесплатно, без нового списания. / Ticket of an unfinished job from a previous `[research_pending]` response. Pass it to wait for the finished result — free, no new charge.",
+  "type": "string"
+}

removedInput schema / required
```
Removed value: -[
-  "query"
-]
```

First observedJul 29, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations are minimal (readOnlyHint, openWorldHint, etc.), but the description goes far beyond them. It reveals the 1–15 minute runtime, the 90-second synchronous wait, the [research_pending] ticket mechanism, free polling, one-time billing, and the unverified-source warning. It also explains that repeating a topic costs again. This is rich behavioral disclosure without any contradiction to annotations.

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?

Despite its length, the description is exceptionally well-structured: it opens with the purpose, then has clear 'when/not' and 'how it works' sections, followed by return format and cost. Every sentence carries information — no filler. The front-loaded purpose immediately answers 'what does this do?' and the cost/billing details are essential for an agent deciding whether to invoke.

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?

For a complex async tool with no output schema, the description covers everything an agent needs: the workflow, the polling protocol, the return structure (answer + sources), the source-missing warning, and the cost model. It anticipates the pain points of async execution and billing. No critical details are missing.

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

Parameters3/5

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

Schema description coverage is 100%: both query and ticket are already documented in the schema with clear explanations of when each is required. The description adds context about the ticket being free and the query being repeatable, but these are behavioral rather than parameter-semantic additions. The schema already does the heavy lifting, so a baseline 3 is appropriate.

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 clearly states a specific verb ('runs multi-step research'), the resource (web sources), and the output (finished answer with links). It explicitly names the sibling web_search as the alternative for simpler queries, making differentiation unambiguous. The purpose is concrete and actionable.

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 explicit when-to-use (multi-source comparison, analysis) and when-not-to-use (single fact or link list, pointing to web_search as 20x cheaper and faster). Also explains the async polling workflow (start with query, poll with ticket) and that polling is free — no hidden costs. This is a complete usage guide for an agent.

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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