Spark - AI Assets Marketplace

Search assets

search_assets

Read-onlyIdempotent

Search the Spark AI assets marketplace for agents, skills, prompts, prompt chains, MCP connectors and bundles.

For when a text query is available but the slug is not known; get_asset describes a known slug. Returns numbered summaries with type, downloads, outcomes, price, tags and URL, or a no-results message. No login required; no acquisition is made. Unmatched nonempty queries may be recorded as unmet catalog needs. A nonempty job ranks text matches by task fit, even with a non-combo sort.

For example, search_assets(query='rate limiting', job='add a rate limiter', limit=5).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`job`	No	Optional task, e.g. 'review Python code'; a nonempty value enables job-based ranking regardless of sort.
`sort`	No	combo (default): quality, ratings and outcomes; popular: downloads; newest: creation date; rating: average rating. Invalid values fall back to combo; job enables ranking for any sort.	combo
`type`	No	Optional asset type: agent, skill, prompt, prompt_chain, mcp_connector or bundle; omitted means all types.
`limit`	No	Maximum results; clamped to 1-50, default 10.
`query`	Yes	Text matched against titles and descriptions, e.g. 'rate limiting'; empty text browses the catalog.
`domain`	No	Optional domain slug from list_categories, e.g. 'development'; omitted means all domains.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed6 schema fields changedOct 2, 2026

changedInput schema / properties / domain / description

Previous value: -"Filter by domain slug (e.g. 'development', 'marketing')"New value: +"Optional domain slug from list_categories, e.g. 'development'; omitted means all domains."

changedInput schema / properties / job / description

Previous value: -"Job/task the agent is trying to accomplish. Enables job-based relevance ranking (e.g. 'review Python code', 'generate marketing copy', 'analyze data')."New value: +"Optional task, e.g. 'review Python code'; a nonempty value enables job-based ranking regardless of sort."

changedInput schema / properties / limit / description

Previous value: -"Number of results (1-50, default 10)"New value: +"Maximum results; clamped to 1-50, default 10."

changedInput schema / properties / query / description

Previous value: -"Search text (matches title and description)"New value: +"Text matched against titles and descriptions, e.g. 'rate limiting'; empty text browses the catalog."

changedInput schema / properties / sort / description

Previous value: -"Sort order: combo, popular, newest, rating. 'combo' = combined quality+ratings+agent outcomes score (default)."New value: +"combo (default): quality, ratings and outcomes; popular: downloads; newest: creation date; rating: average rating. Invalid values fall back to combo; job enables ranking for any sort."

changedInput schema / properties / type / description

Previous value: -"Filter by asset type (agent, skill, prompt, prompt_chain, mcp_connector, bundle)"New value: +"Optional asset type: agent, skill, prompt, prompt_chain, mcp_connector or bundle; omitted means all types."

Changed2 schema fields changedSep 24, 2026

changedInput schema / properties / job / description

Previous value: -"Job/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."New value: +"Job/task the agent is trying to accomplish. Enables job-based relevance ranking (e.g. 'review Python code', 'generate marketing copy', 'analyze data')."

changedInput schema / properties / sort / description

Previous value: -"Sort order: combo, popular, newest, rating. 'combo' = combined quality+ratings+agent outcomes score (default, recommended)."New value: +"Sort order: combo, popular, newest, rating. 'combo' = combined quality+ratings+agent outcomes score (default)."

First observedSep 23, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already cover readOnly/idempotent/closed-world, and the description adds genuinely new behavior: what the response contains (numbered summaries with type, downloads, outcomes, price, tags, URL), the no-results message, and the side effect that unmatched nonempty queries may be recorded as unmet catalog needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the purpose, then when-to-use, then return shape, then an example — every sentence carries information. Slightly long at four paragraphs for a search tool, but no filler.

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 an output schema present, the definition need not explain return values, yet it still summarizes them alongside the usage rule, side effects, and an example invocation. Nothing needed to call this six-parameter tool correctly 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%, so baseline is 3. The description's notes on job-based ranking ('a nonempty job ranks text matches by task fit, even with a non-combo sort') largely restate the job and sort schema descriptions, and the example adds no syntax beyond what the schema already documents.

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 ('Search') and resource (the Spark AI assets marketplace), enumerates the asset types covered, and explicitly contrasts with get_asset ('get_asset describes a known slug'), so the agent can separate it from its nearest 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?

Gives an explicit selection rule: use this when a text query is available but the slug is not known, and use get_asset for a known slug. It also states prerequisites (no login required) and that no acquisition occurs, which removes ambiguity about commitment.

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

All Spark - AI Assets Marketplace tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.