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azhang

qdrant-llamaindex-mcp-server

by azhang

qdrant-find

Search for semantically similar documents stored by LlamaIndex in Qdrant to retrieve relevant context and knowledge base content.

Instructions

Search for documents stored by LlamaIndex in Qdrant. Use this tool when you need to:

  • Find relevant documents or text chunks by semantic similarity

  • Access stored knowledge base content

  • Retrieve context from previously indexed documents

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesWhat to search for
collection_nameYesThe collection to search in
limitNoMaximum number of results to return
offsetNoNumber of results to skip for pagination
score_thresholdNoMinimum similarity score threshold (0.0 to 1.0). Results below this score will be filtered out.
Behavior3/5

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 indicates a read-like operation (search), which is non-destructive. However, it does not disclose other behavioral traits such as idempotency, required permissions, or side effects. The description is adequate but lacks depth beyond the obvious.

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 concise at three sentences plus bullet points. However, the bullet points largely restate the first sentence, adding some redundancy. It is front-loaded with the core purpose but could be slightly more streamlined.

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

Completeness3/5

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

Given the lack of output schema, the description does not explain what the tool returns (e.g., document chunks, scores). It provides useful context about LlamaIndex but omits return value information. The input schema is fully covered, but completeness for a search tool weighing output is moderate.

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?

Schema description coverage is 100%, with all parameters (query, collection_name, limit, offset, score_threshold) well-described in the schema itself. The description adds no extra semantic meaning beyond what the schema already provides, meeting the baseline of 3 for high 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 searches for documents by semantic similarity, accesses knowledge base content, and retrieves context from indexed documents. It uses specific verbs and resource contexts. However, it does not differentiate from the sibling tool 'qdrant-search-by-vector', which likely performs a similar function.

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

Usage Guidelines3/5

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

The description lists three use cases (find relevant documents, access knowledge base, retrieve context), providing some guidance. But it lacks explicit 'when not to use' or alternative tools (e.g., 'qdrant-search-by-vector' for raw vector search), leaving usage boundaries implied rather than 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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