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Spark - AI Assets Marketplace

Get Asset

get_asset

Get full details of a Spark asset by its slug.

Returns description, metadata, outcome reports, files, and the URL on spark.entire.vc.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesAsset slug (e.g. 'vb-seo-expert', 'vb-python-expert')

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
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 behavioral disclosure burden. It makes the read-only nature and return contents clear ('Get full details... Returns description, metadata, outcome reports, files, and the URL'), but it does not mention error behavior, access requirements, or rate limits. For a simple read tool this is adequate but not exceptional.

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 two concise sentences with no filler. The first sentence states the core action and scope, and the second lists the returned data fields, making it easy for an agent to quickly assess purpose and output.

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?

With only one required parameter and an output schema present, the description is largely complete for a simple lookup operation. It could be slightly stronger by clarifying how it relates to get_asset_content or when to prefer this tool over search_assets, but nothing essential about invoking it is missing.

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%, and the slug parameter already includes explanatory text and examples. The description adds no new parameter-level meaning beyond restating that the lookup is by slug, so the baseline of 3 is appropriate.

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 uses a specific verb ('Get full details of a Spark asset by its slug') and clearly identifies the resource and key. It also enumerates the returned fields, which helps distinguish it from sibling tools like get_asset_content that likely return only a subset.

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?

The description gives no explicit guidance on when to use this tool versus alternatives such as get_asset_content or search_assets. It implies usage by slug, but it does not state any exclusions or provide routing cues, leaving the agent to infer when this is the right choice.

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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