Проверить поиск по базе

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

Beyond the read-only, open-world, idempotent, and non-destructive annotations, the description adds a crucial behavioral nuance: if the base is disabled or vectors are not computed, the response will be empty without an error. This prevents misinterpretation of empty results and significantly enhances transparency.

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

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

The description is two sentences, with the first sentence front-loading the core purpose and the second adding both the use case and an important edge-case behavior. Every sentence earns its place with no redundancy.

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

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

The description is complete enough for a search/debug tool: it explains what is returned, how it is used, and a key failure mode. It does not detail the output structure (e.g., whether fragments include scores or metadata), but given the simple purpose and rich schema, this is a minor gap.

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

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

The input schema already provides 100% coverage with descriptions for all four parameters, including defaults and constraints. The tool description adds no parameter-level details beyond what is in the schema, so the baseline score of 3 applies.

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

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

The description uses a specific verb ('Показывает' = shows) and identifies the resource (which fragments the knowledge base will provide to the model for a given question). This clearly distinguishes it from sibling tools like knowledge_list, knowledge_save, 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.

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

The description explicitly states when to use this tool: to verify that materials are parsed and the similarity threshold is correctly chosen. It does not mention alternatives or exclusions, but the intended use case is clear and actionable.

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