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mcp-server-qdrant

mcp-server-qdrant

Official
by qdrant
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

qdrant-store

Store text information and metadata in a Qdrant collection for later retrieval, enabling persistent memory across sessions.

Instructions

Keep the memory for later use, when you are asked to remember something.

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": {
      "information": "<information>",
      "collection_name": "<collection_name>"
    },
    "name": "qdrant-store"
  }
}

Input Schema

NameRequiredDescriptionDefault
informationYesText to store
collection_nameYesThe collection to store the information in
metadataNoExtra metadata stored along with memorised information. Any json is accepted.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.0.0

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It only states that memory is kept for later use, but does not disclose potential side effects, idempotency, permission requirements, or return behavior. This is a significant gap for a write-type tool, similar to the update_drive example.

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 sentence with no unnecessary words, making it concise. However, it under-specifies in terms of purpose, but that is a matter of content, not conciseness. The structure is efficient and front-loaded with the core action.

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 with fully documented parameters and no output schema. However, the description lacks details about return values or behavior on failure/success, and the relationship to qdrant-find is not addressed. Given the simplicity, this is adequate but has clear gaps.

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 schema covers 100% of the parameters with descriptions, so the baseline is 3. The tool description does not add any additional meaning about the parameters beyond what the schema already provides for 'information', 'collection_name', and 'metadata'.

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

Purpose3/5

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

The description 'Keep the memory for later use' conveys a storing/remembering action but is metaphorical and lacks specificity about the resource (e.g., 'store information in a qdrant collection'). It does not explicitly differentiate from the sibling tool qdrant-find, though the contrast is implied. The verb 'keep' is less precise than 'store' or 'save'.

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 phrase 'when you are asked to remember something' provides a clear context for when to use the tool. However, it does not mention alternatives or exclusion cases, so it falls short of a perfect score.

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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