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qdrant-kuzu-mcp

qdrant-kuzu-mcp

by NovaAI-innovation
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

qdrant-find

Retrieve memories from Qdrant by searching their content. Use this to access stored information and personal details for further analysis.

Instructions

Look up memories in Qdrant. Use this tool when you need to:

  • Find memories by their content

  • Access memories for further analysis

  • Get some personal information about the user

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>",
      "collection_name": "<collection_name>"
    },
    "name": "qdrant-find"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesWhat to search for
collection_nameYesThe collection to search in

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.8.1

TDQS

A3.6/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only says 'look up memories,' which implies a read-only operation but does not explicitly state that, nor does it mention return format, permissions, or potential side effects. This is a significant gap for a lookup tool.

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 and front-loaded with the main action. The bullet list adds some redundancy (e.g., 'Find memories by their content' is nearly synonymous with 'Look up memories'), but the overall length is appropriate and no words are wasted.

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?

The tool is simple (2 params, no output schema), and the description covers its main purpose and key use cases. However, it does not describe what the tool returns (since there is no output schema), and it does not clarify that it is strictly a retrieval tool (as opposed to qdrant-store). This leaves some gaps, but the description is adequate for a basic lookup tool.

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% (both 'query' and 'collection_name' have descriptions), so the baseline is 3. The tool description itself adds no extra parameter context, but the schema already adequately explains what each parameter does.

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 opens with a clear verb+resource construction ('Look up memories in Qdrant'), immediately distinguishing this tool from its sibling 'qdrant-store'. The bulleted use cases further reinforce the purpose by listing specific retrieval scenarios.

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 explicitly states when to use the tool with three concrete use cases. However, it does not mention when not to use it or reference the sibling 'qdrant-store' as an alternative, so it falls short of the explicit when/when-not guidance required for a 5.

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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