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azhang

qdrant-llamaindex-mcp-server

by azhang

qdrant-store

Save new documents or text chunks to a Qdrant vector database for later retrieval, adding information to a knowledge base with metadata support.

Instructions

Store information in Qdrant vector database. Use this tool when you need to:

  • Save new documents or text chunks

  • Add information to the knowledge base

  • Store content for later retrieval

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesPoint ID. If omitted, a new point is created.
informationYesText to store
collection_nameYesThe collection to store the information in
metadataNoExtra metadata stored along with memorised information. Any json is accepted.
Behavior2/5

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

No annotations are provided, so the description must disclose behavior. It does not mention idempotency, side effects, authentication, or what happens if an ID already exists. The only behavioral hint comes from the schema description for 'id', not the main description.

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

Conciseness5/5

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

The description is concise, front-loading the main action, and uses bullet points for clarity. Every sentence adds value without redundancy.

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 description adequately explains what the tool does and when to use it for a simple store operation. However, it lacks details on return values (no output schema) and edge cases like duplicate IDs, leaving some gaps for agent understanding.

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 parameters are documented. The tool description adds no further semantic meaning beyond the use cases; it does not explain parameter details or relationships.

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 'Store information in Qdrant vector database' and lists specific use cases like saving documents and adding to knowledge base. However, it does not differentiate from the sibling tool 'qdrant-add-documents', which may cause confusion.

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 provides bullet points on when to use the tool (e.g., save new documents), but lacks guidance on when not to use it or alternatives among sibling tools.

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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