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michal7kw
by michal7kw

qdrant_find

Search Qdrant collections semantically by submitting a natural language query to retrieve relevant matches based on meaning.

Instructions

Search for relevant information in the Qdrant database using semantic similarity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language query to search for. The query is embedded using the same GPU model used for storage, ensuring accurate results.
top_kNoMaximum number of results to return (default: 5).
collection_nameNoName of the collection to search in. Required if no default collection is configured.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It implies a read-only operation but does not explicitly state that no data is modified, does not mention return behavior, error conditions, or limitations. The single sentence provides minimal behavioral disclosure beyond the literal action.

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

Conciseness4/5

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

The description is a single, efficient sentence with no redundancy or filler. It is front-loaded with the verb and resource. While extremely brief, it is not a tautology and conveys the essential purpose. It avoids unnecessary words while being clear.

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

Completeness2/5

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

Given the tool has a sibling (qdrant_store) and an output schema (which covers return format), the description is still incomplete. It lacks any usage context, such as when to choose this over storage or how the search integrates with the workflow. The presence of an output schema reduces the need to explain returns, but the description does not cover the selection decision or behavioral expectations.

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?

The input schema covers all three parameters (query, top_k, collection_name) with descriptions, so the baseline is 3. The description adds nothing beyond the schema; it mentions 'semantic similarity' which is already implied by the query parameter's embedding mention. No additional value is provided.

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 states a clear verb ('Search'), the target resource ('Qdrant database'), and the method ('semantic similarity'). This distinguishes it from the sibling qdrant_store, which likely stores information. However, it does not explicitly name the sibling or contrast with it, so it lacks the full differentiation seen in higher-scoring examples.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus the alternative qdrant_store, nor any mention of prerequisites or context. The description only states the action without any direction on selection or exclusion criteria.

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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