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garant_search

Search the Russian GARANT legal database using keywords or document requisites. Get a paginated list of documents with type distribution to locate relevant legislation and court practice.

Instructions

Поиск документов в базе ГАРАНТ по реквизитам или контексту.

query — 3–5 КЛЮЧЕВЫХ СЛОВ, а не фраза-тезис. Гарант трактует запрос как конъюнкцию всех слов: длинная формулировка даёт одно-два случайных попадания или ноль. «продление домашнего ареста мотивированность» — рабочий запрос; «суд обязан привести конкретные фактические обстоятельства» — нет. Возвращает {total, страница, всего_страниц, items[...], kinds[]}, где kinds — распределение находок по видам (акты власти, высшие суды, комментарии). doc_id использовать дальше в garant_document / garant_article / garant_revisions.

Перечень находок кэшируется (тексты норм — никогда). Ответ из кэша несёт в provenance из_кэша, дата_записи, возраст_часов и срок_годности_часов: документ, появившийся в базе после записи, в таком ответе не появится. use_cache=False — потребовать свежего похода в «Гарант»; применять, когда важна именно сегодняшняя полнота выдачи.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
limitNo
queryYes
use_cacheNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations to rely on, the description fully carries the behavioral burden. It discloses that GARANT treats queries as a conjunction of words, that result lists are cached while text is not, that cached responses include provenance fields, and that documents added after cache entry will not appear. This is substantial, non-obvious behavioral context.

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 information-dense but well-structured: purpose, query constraints with examples, return shape, downstream use, then cache mechanics. Every sentence earns its place and the most critical usage pitfall is front-loaded.

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?

The description covers the essential operational context: what the tool returns, how to phrase queries, how caching affects freshness, and how to route the resulting doc_id into sibling tools. Given the tool's moderate complexity and absence of annotations, this is complete enough for an agent to invoke it 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 description coverage is 0%, so the description must compensate. It provides rich semantics for query (3–5 keywords, conjunction behavior, good/bad examples) and for use_cache (freshness behavior and provenance). Page and limit are not explained but their names, defaults, and the returned 'страница'/'всего_страниц' fields make their meaning reasonably inferable.

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: 'Поиск документов в базе ГАРАНТ по реквизитам или контексту.' It makes the search behavior immediately clear and distinguishes it from sibling document-retrieval tools by noting that doc_id is used downstream in garant_document / garant_article / garant_revisions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives strong usage guidance for the query parameter and the cache: use_cache=False is recommended when today's completeness matters. It also implicitly explains the tool's role in the pipeline via 'doc_id использовать дальше в garant_document / garant_article / garant_revisions,' though it does not explicitly state when not to use this tool versus each sibling.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.