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search_scripts

Find scripts in the marketplace by describing your need in natural language, then get the top 5 matches with vending codes and descriptions.

Instructions

Search the marketplace for scripts matching a natural language query. Returns top 5 results with vending codes and descriptions.

Args: query: Natural language description of what you need (e.g. "analyze CSV data").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It does disclose the result limit ('top 5 results') and result contents ('vending codes and descriptions'), but does not mention whether the operation is read-only, authentication requirements, or any rate limits. This is minimally adequate but not rich.

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 concise and front-loaded with the primary purpose, followed by a useful return summary and a structured Args section. Every sentence earns its place without redundancy or filler.

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 simple search tool with one parameter, the description is mostly complete: it states what to search, how to phrase the query, and what results will contain. An output schema exists, so detailed return types are covered elsewhere. It could add a note about error conditions or sorting, but overall it provides sufficient context for an agent to use the tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema only provides the parameter name and type, so the description adds essential meaning by explaining 'query' as a natural language description and providing a concrete example ('analyze CSV data'). This compensates for the 0% schema description coverage and gives the agent clear guidance for constructing the parameter.

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 ('Search') with a clear resource ('marketplace for scripts') and a defining scope ('matching a natural language query'). It also states the return value (top 5 results with vending codes and descriptions), making it easy to distinguish from sibling tools like list_agents or vector_store_search.

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

Usage Guidelines3/5

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

The description implies the tool should be used when you need to find scripts based on a natural language description, which is clear context. However, it does not explicitly mention alternatives or when not to use this tool, leaving the comparison with sibling tools implicit.

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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