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Numonic

by ai.numonic
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GetCollectionAssets

Get assets in a collection with membership metadata (position, role, added date).

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum number of assets to return. Default: 50
offsetNoOffset for pagination. Default: 0
order_byNoSort order: "position" or "added_at". Default: "position"position
tenant_idNoOptional: UUID of the tenant. Omit it: the server uses the tenant your credential (OAuth connection or API key) is bound to. A tenant_id that differs from the credential's tenant is rejected.
include_nestedNoInclude assets from all descendant collections. Default: false
collection_pathYesCollection ltree path to get assets from

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / tenant_id / description
      Previous value: -"UUID of the tenant"New value: +"Optional: UUID of the tenant. Omit it: the server uses the tenant your credential (OAuth connection or API key) is bound to. A tenant_id that differs from the credential's tenant is rejected."
    • changedInput schema / required
      Previous value: -[
-  "tenant_id",
-  "collection_path"
-]New value: +[
+  "collection_path"
+]
  2. First observed

TDQS

B3.3/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It usefully discloses the return payload (membership metadata), which is beyond schema, but is silent on auth/permission requirements and pagination behavior (the latter is covered by the schema). For a read tool with zero annotation coverage, this is adequate but thin.

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?

A single front-loaded sentence with no filler; the resource and the notable return content come first. Every clause earns its place.

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?

For a 6-parameter read tool with no output schema and no annotations, the description is minimal. The schema fully documents inputs, but the description does not fill the gap left by the absent output schema, so an agent gets only a hint of the return shape.

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%, including detailed notes on tenant_id fallback and the include_nested behavior, so the schema does the heavy lifting. The description adds no parameter-level detail beyond what the schema already states, matching the baseline of 3.

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?

Clear verb ('Get') plus resource ('assets in a collection') and even names the shape of the return ('membership metadata: position, role, added date'). An agent can distinguish it from GetAssetDetails or ListCollections, though it never explicitly contrasts with those siblings.

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

Usage Guidelines2/5

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

No when-to-use guidance, no prerequisites, and no mention of the obvious alternative (ListCollections) or how this differs from GetAssetDetails. The agent must infer the context of use entirely.

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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