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qdrant-llamaindex-mcp-server

qdrant-llamaindex-mcp-server

by azhang
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

qdrant-search-by-vector

Search a Qdrant collection by providing a raw vector to find similar items based on vector similarity, without needing a text query.

Instructions

Search using a raw vector instead of text query.

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": {
      "vector": "<vector>",
      "collection_name": "<collection_name>"
    },
    "name": "qdrant-search-by-vector"
  }
}

Input Schema

NameRequiredDescriptionDefault
vectorYesThe query vector to search with
collection_nameYesThe collection to search in
limitNoMaximum number of results to return

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

B3/5.0
Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral traits such as return format (e.g., points with distances), pagination, or performance characteristics. This is a significant gap for a search tool.

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

Conciseness3/5

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

The description is a single concise sentence, but it lacks structure and does not front-load critical information. While it is not verbose, it could be more informative without losing conciseness.

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?

Despite the tool having a simple signature and no output schema, the description does not explain what results are returned (e.g., points, distances). This omission makes it less complete for an agent to understand its behavior.

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%, so the baseline is 3. The description adds no additional meaning beyond the schema parameter descriptions, which are already self-explanatory.

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 it performs a vector-based search, contrasting with text-based queries. This sets a specific purpose and distinguishes it from tools like qdrant-find.

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 implies use cases by saying 'instead of text query', but does not provide explicit guidance on when to use or not use this tool, nor does it mention prerequisites like collection existence.

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

Deploy Server

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