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avaazquezz

io.github.avaazquezz/mcp-qdrant

by avaazquezz

qdrant_collection_update

Idempotent

Adjust optimizer, HNSW, vector, quantization, and strict mode settings on an existing Qdrant collection, changing only specified fields.

Instructions

Update optimizer/HNSW/collection/vector params on an existing collection.

    Only the fields you pass are changed; omitted ones keep their current
    value. `quantization_config="disabled"` turns quantization off.
    `vectors_config`/`sparse_vectors_config` only **adjust** named
    vectors that already exist (HNSW/quantization/index tuning) — they
    cannot add a new one; use `qdrant_collection_vector_create` for
    that, or this fails with Qdrant's own "Not existing vector name"
    error. Fails with a clear error if the collection doesn't exist.

    Example: {"collection_name": "docs", "optimizers_config": {"indexing_threshold": 10000}}
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hnsw_configNo
vectors_configNo
collection_nameYes
collection_paramsNo
optimizers_configNo
strict_mode_configNo
quantization_configNo
sparse_vectors_configNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
configYesCurrent statistics and configuration of the collection
statusYesCurrent statistics and configuration of the collection
warningsNoWarnings related to the collection
points_countNoApproximate number of points (vectors + payloads) in collection. Each point could be accessed by unique id.
update_queueNoUpdate queue info
payload_schemaYesTypes of stored payload
segments_countYesNumber of segments in collection. Each segment has independent vector as payload indexes
optimizer_statusYesCurrent statistics and configuration of the collection
indexed_vectors_countNoApproximate number of indexed vectors in the collection. Indexed vectors in large segments are faster to query, as it is stored in a specialized vector index.

Schema Changelog

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

  1. First observedv1.1.1

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the annotations, the description discloses important behavioral traits: partial-update semantics ('Only the fields you pass are changed; omitted ones keep their current value'), the special 'disabled' value for quantization_config, and the constraint that vectors_config/sparse_vectors_config can only adjust existing vectors. It also discloses failure modes for both missing collections and non-existent vector names. This is rich, actionable behavioral context.

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 front-loaded with the core purpose, then adds only high-value semantic context, failure modes, and an example. There is no filler, repetition of schema, or unnecessary prose. Every sentence earns its place, and the example is compact and illustrative.

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 high complexity, the description covers the essential operational semantics, the key constraint about vector names, the special quantization value, the error condition for missing collections, and a realistic example. The output schema exists, so not describing return values is acceptable. An agent has enough information to select and invoke this tool correctly.

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?

Schema description coverage is 0%, so the description carries real weight here. It adds crucial meaning: partial updates, quantization_config='disabled', and the difference between adjusting and adding vectors. It also provides a concrete example using collection_name and optimizers_config. However, it does not explicitly call out strict_mode_config or enumerate every top-level parameter, leaning on the nested schema definitions for those details.

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 specific verb and resource: 'Update optimizer/HNSW/collection/vector params on an existing collection.' It clearly differentiates this update tool from creation/read/delete tools by emphasizing 'existing collection' and explicitly noting it cannot add new vectors. This is far beyond a tautology and gives an agent a precise mental model.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when-to-use context: update parameters on an existing collection. It also gives a clear when-not-to-use and alternative: vectors_config/sparse_vectors_config cannot add new vectors; use qdrant_collection_vector_create instead. It additionally warns that the tool fails if the collection does not exist, which helps the agent decide whether a create tool is needed first.

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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