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Spark - AI Assets Marketplace

Search Assets

search_assets

Search the Spark AI assets marketplace.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobNoJob/task the agent is trying to accomplish. Enables job-based relevance ranking (e.g. 'review Python code', 'generate marketing copy', 'analyze data'). Recommended for agent use.
sortNoSort order: combo, popular, newest, rating. 'combo' = combined quality+ratings+agent outcomes score (default, recommended).combo
typeNoFilter by asset type (agent, skill, prompt, prompt_chain, mcp_connector, bundle)
limitNoNumber of results (1-50, default 10)
queryYesSearch text (matches title and description)
domainNoFilter by domain slug (e.g. 'development', 'marketing')

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden and only says it searches, offering no detail about result behavior, default ranking, or side effects. It implies a read-only operation but does not disclose additional behavioral traits.

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?

One front-loaded sentence that conveys the core purpose with no filler. It is appropriately sized for a tool whose invocation details live in the schema.

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 search tool with six fully documented parameters and an output schema, the one-line description plus schema provides enough invocation context. The main omission is sibling-routing guidance, but that is covered under usage guidelines.

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 query, job, sort, type, domain, and limit are already documented. The description adds no additional parameter meaning beyond what the input schema provides, matching the baseline baseline for high schema coverage.

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?

States a specific verb and resource: search across the Spark AI assets marketplace, which distinguishes it from sibling get/list tools. The purpose is unambiguous even without inspecting the schema.

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 guidance about when to use this tool versus list_popular, list_categories, or get_asset. It neither states when this tool should be preferred nor mentions any exclusions.

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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