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Пересобрать материалы

knowledge_reindex

Re-queues knowledge base materials for re-parsing after changes to chunk size or AI service connection. Without a document ID, rebuilds the entire base.

Instructions

Ставит материалы в очередь на повторный разбор. Нужно после смены размера куска или подключения к ИИ-сервису: старые фрагменты нарезаны по-прежнему. Без параметра document пересобирается вся база.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
baseYesБаза знаний: название или идентификатор.
caseNoДело: название или идентификатор. Если не указать, берётся дело по умолчанию, иначе последнее открытое в панели.
documentNoИдентификатор одного материала.
Behavior4/5

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

Annotations already indicate a non-read-only, non-idempotent, non-destructive operation. The description adds crucial context: the operation is asynchronous ('в очередь' – in queue) and that old fragments are reprocessed in the same way. This goes beyond the annotations by explaining the queuing behavior and the default base-wide scope.

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?

Two sentences, no filler. The first sentence states the action, the second provides usage context and a key parameter detail. Front-loaded and efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity and full schema descriptions, the description covers the purpose, use cases, and a critical behavioral default. It does not explain the result of reindexing (e.g., whether old data is deleted), but for a queueing tool this is acceptable, and no output schema exists to mandate further return-value detail.

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 significant meaning for the 'document' parameter by explaining that its absence triggers a full-base rebuild ('Без параметра document пересобирается вся база'). This clarifies the parameter's optionality and effect better than the schema description alone.

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 the tool's function: 'Ставит материалы в очередь на повторный разбор' (puts materials in queue for re-processing). It identifies the resource (materials in a knowledge base) and the action (reindexing), and distinguishes itself from sibling tools like knowledge_search or knowledge_delete.

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 provides specific scenarios when to use: 'после смены размера куска или подключения к ИИ-сервису' (after changing chunk size or connecting to an AI service). It also clarifies the default behavior without the document parameter. However, it does not explicitly mention alternatives, though the context is clear.

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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