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reindex_vector_database

Reindex vector database with optimized configurations to improve search performance and resolve slow query issues. Automatically detects or manually selects profiles for small, medium, or large datasets.

Instructions

使用优化配置重新索引向量数据库。 使用场景:

  • 更改配置文件

  • 搜索速度非常慢

  • 希望针对特定数据库大小进行优化

  • 存在持续的性能问题

参数: profile: 配置文件('small', 'medium', 'large', 'auto')。 'auto' 会自动检测最佳配置文件

返回: 有关重新索引过程的信息。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
profileNoauto

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description carries full burden. It indicates this is a reindexing operation (implies mutation/write), mentions performance optimization, and describes the 'auto' profile option. However, it doesn't disclose important behavioral aspects like whether this operation is destructive, requires downtime, has rate limits, or specific permission requirements for a database mutation tool.

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 well-structured and appropriately sized. It begins with the core purpose, then provides usage scenarios in bullet points, followed by parameter details, and finally return information. Every section adds value with no redundant information. The Chinese text is concise and clear.

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 this is a database mutation tool with no annotations but with an output schema, the description does well. It explains the purpose, when to use it, parameter details, and mentions what the return contains. The output schema existence means the description doesn't need to detail return values. However, for a potentially destructive database operation, more behavioral context would be helpful.

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?

The description adds significant value beyond the schema. The input schema has 0% description coverage and only shows 'profile' as a string parameter. The description explains the parameter meaning ('配置文件' - configuration file), lists the four possible values ('small', 'medium', 'large', 'auto'), and explains what 'auto' does ('会自动检测最佳配置文件' - automatically detects the best configuration file). This fully compensates for the poor schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: '使用优化配置重新索引向量数据库' (Reindex vector database using optimized configuration). It specifies the verb ('重新索引' - reindex) and resource ('向量数据库' - vector database). However, it doesn't explicitly differentiate from sibling tools like 'optimize_vector_database' - both seem related to vector database optimization.

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?

The description provides excellent usage guidelines with a dedicated '使用场景' (Usage scenarios) section listing four specific situations when to use this tool: after configuration changes, when search is very slow, for database size optimization, and for persistent performance issues. This gives clear context for when this tool is appropriate.

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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