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YARlabs

HyperspaceDB MCP Server

Official
by YARlabs

hyperspace_get_stats

Retrieve detailed statistics for a named vector collection, including count, dimension, metric, logical clock, storage, cache, metadata, and write-buffer size.

Instructions

Get detailed statistics for the named collection. Returns a JSON object with count, dimension, metric, logical_clock, and storage stats. Merges metadata, cache stats and WriteBuffer size.

Input Schema

NameRequiredDescriptionDefault
collectionYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv4.2.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does disclose the return shape (count, dimension, metric, logical_clock, storage stats) and that multiple internal sources are merged, which is genuine added context for a read tool. However, it says nothing about cost, whether it is a point-in-time snapshot, or what happens when the collection does not exist.

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?

Three short sentences, purpose front-loaded, with no filler. Each sentence contributes: what it does, what it returns, and what it aggregates.

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

Completeness4/5

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

For a single-parameter read tool with no output schema, the description compensates well by enumerating the return fields and the merged sources. Only the omitted error/format semantics for the collection argument keep it from being fully complete.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must explain the single `collection` parameter, and it only restates it as "the named `collection`". It never clarifies whether this is a collection name versus an ID, whether wildcards work, or the error behavior for an unknown name — the exact gap the missing schema descriptions leave open.

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?

States a specific verb and resource ("Get detailed statistics for the named collection") and enumerates the returned fields. It partially distinguishes itself from hyperspace_cache_stats by noting it merges metadata, cache stats and WriteBuffer size, but never explicitly names that sibling as an alternative, so differentiation is implicit rather than stated.

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?

Usage is only implied: an agent can infer this is the per-collection stats reader, but the description never says when to call it versus hyperspace_list_collections (discovery), hyperspace_cache_stats (cache-only) or hyperspace_get_points. No exclusions, prerequisites, or failure conditions are given.

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