Skip to main content
Glama
avaazquezz

io.github.avaazquezz/mcp-qdrant

by avaazquezz

qdrant_query

Read-onlyIdempotent

Search Qdrant collections by vector similarity, passing a query vector or combining multiple prefetch queries with RRF/DBSF fusion for hybrid retrieval.

Instructions

Vector similarity search, with optional hybrid search over multiple prefetch stages.

    Pass `query_vector` (a literal vector, or a point id to reuse an
    existing point's vector) for a plain nearest-vector query, or
    `fusion` + 2+ `prefetch` stages to combine multiple retrieval
    strategies via Reciprocal Rank Fusion (`fusion="rrf"`) or
    Distribution-Based Score Fusion (`fusion="dbsf"`) — exactly one of
    `query_vector`/`fusion` is required. `using` selects a named vector;
    `lookup_from` resolves `query_vector` from a point id in another
    collection instead of the current one. Fails with a clear error if
    the collection doesn't exist.

    Example (plain): {"collection_name": "docs", "query_vector": [0.1, 0.2, 0.3, 0.4],
        "limit": 5}
    Example (hybrid): {"collection_name": "docs", "fusion": "rrf", "prefetch": [
        {"query_vector": [0.1, 0.2, 0.3, 0.4], "using": "dense", "limit": 20},
        {"query_vector": [0.5, 0.5], "using": "sparse", "limit": 20}
    ], "limit": 5}
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
usingNo
fusionNo
prefetchNo
lookup_fromNo
query_filterNo
query_vectorNo
with_payloadNo
with_vectorsNo
collection_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pointsYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.1.1

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare read-only/idempotent/non-destructive behavior. The description adds substantial behavioral detail beyond that: the exactly-one requirement, the 'one level only' prefetch restriction, lookup_from cross-collection resolution, and error-on-missing-collection behavior. No contradiction with annotations.

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 long but well-structured: a summary sentence, parameter semantics, an error note, then two concrete examples. Every sentence adds value, and the examples make complex hybrid usage concrete. Appropriate length for a tool with this many parameters.

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

Completeness5/5

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

Given the tool's complexity, the description is remarkably complete: both invocation modes, fusion options, prefetch semantics, the exactly-one constraint, and failure behavior are all covered. The output schema and annotations fill the remaining gaps. Only the lack of sibling-tool comparison is a minor omission, and that is a usage-guideline nuance rather than a completeness failure.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With top-level schema description coverage at 0%, the description carries a heavy burden and does much of the work: it explains query_vector (literal or point id), fusion algorithms, prefetch stages, using, and lookup_from. It doesn't explicitly define limit, with_payload, with_vectors, query_filter, or collection_name, though several are self-evident or documented in the schema's $defs. A solid but not total compensation for the schema gap.

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 precise verb and resource: 'Vector similarity search, with optional hybrid search over multiple prefetch stages.' This leaves no doubt about the tool's function and immediately distinguishes it from sibling collection-management and point-scroll tools.

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?

It gives clear context for when to use plain query_vector mode vs hybrid fusion+prefetch mode, including the 'exactly one of query_vector/fusion is required' rule and two full examples. However, it never explicitly names alternative sibling tools (e.g., qdrant_points_scroll) or states when not to use this tool, so it falls just short of a 5.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/avaazquezz/Qdrant-MCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server