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Get assets

get_assets
Read-only

Get full details for up to 100 assets in ONE call — the batch form of get_asset. Use it after list_assets or a search to read the fields those rows do not carry: description, colors, source_url, custom fields, dimensions and comment counts. Prefer it over calling get_asset in a loop; a 60-asset board is one call, not sixty. Tokens that do not exist, or that you cannot see, come back in not_found rather than failing the whole call — check it, because a missing asset is otherwise indistinguishable from one you forgot to ask for. Grouped child assets ARE returned here, unlike in list_assets, which lists only top-level rows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asset_tokensYesAsset tokens to read (1-100 per call). The parameter name is `asset_tokens`, not `tokens`. Split a larger set and call again.
organizationNoWorkspace slug, as returned by list_organizations. Omit it when the account has a single workspace — it is then resolved from your token. Pass it only when the account has several, or when you mean a workspace other than the obvious one.
get_ai_payloadNoInclude each asset's `ai_agent_payload`. Default false here, UNLIKE get_asset, which always returns it: each payload can be up to 4 KB, so 100 of them is a very large response. The key is absent rather than null when not asked for.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / additionalProperties
      Added value: +false
    • removedInput schema / properties / context
      Removed value: -{
-  "description": "Explain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): \"Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization.\"",
-  "type": "string"
-}
    • changedInput schema / required
      Previous value: -[
-  "asset_tokens",
-  "context"
-]New value: +[
+  "asset_tokens"
+]
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

The annotations already establish readOnly/non-destructive/openWorld=false, but the description adds substantial behavioral detail beyond them: partial-failure semantics for unknown or invisible tokens in not_found, the contrast with list_assets regarding grouped children, and the return-field scope. It also warns that get_ai_payload defaults differently here than in get_asset and that payload responses can be very large.

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 batch purpose and the get_asset alternative are front-loaded, and each subsequent sentence carries a distinct constraint (return fields, loop avoidance, not_found handling, grouped children). No sentence is redundant with the title, annotations, or schema prose.

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?

With no output schema, the description compensates by naming the fields returned, explaining the not_found bucket, and calling out the get_ai_payload size/default difference. For a 3-parameter read tool with annotations covering safety, this is complete enough for correct invocation.

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 each parameter already has its own detailed description; the baseline is 3. The description reinforces the 1-100 bound and the not_found behavior but adds little parameter-level meaning beyond the schema for organization or get_ai_payload.

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 states a specific verb and resource ('Get full details for up to 100 assets'), identifies the tool as 'the batch form of get_asset,' and explicitly contrasts it with list_assets and get_asset. An agent can distinguish it from every relevant sibling without opening a schema.

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?

It gives a concrete usage sequence ('after list_assets or a search'), an explicit preference rule ('Prefer it over calling get_asset in a loop'), and a scoping contrast with list_assets ('Grouped child assets ARE returned here, unlike in list_assets'). It also explains when not to expect success for individual tokens via the not_found bucket.

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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