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invok-it

invokit-mcp

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by invok-it

search_tools

Find AI agent tools on the invok.it marketplace using natural language queries. Filter by category, quality score, and price per call to pinpoint the right tool.

Instructions

Search the invok.it marketplace for AI agent tools using natural language.

Args: query: Natural language search query (e.g. "send email", "image recognition"). category: Filter by category slug (use list_categories to see available ones). min_quality_score: Minimum quality score (0-100). max_price_per_call: Maximum price per API call in USD. page: Page number for pagination. per_page: Results per page (max 100).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
queryYes
categoryNo
per_pageNo
min_quality_scoreNo
max_price_per_callNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries the behavioral transparency burden. It explains the natural-language search behavior, filter semantics, price units, quality range, and pagination. It does not explicitly state that the operation is read-only or describe relevance/sort behavior, but those are largely inferable from 'Search' and the output schema.

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 opens with a one-sentence purpose and then uses a clean, scannable Args list. Every line adds information needed for correct invocation, with no filler or repetition of schema defaults.

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 six-parameter tool with no annotations and an output schema, the description is largely complete: it explains the search task, all parameters, and how to discover valid categories. It does not mention result ordering or threshold semantics, but the output schema covers return values and the missing details are not critical for selecting or calling the tool.

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

Parameters5/5

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

Schema description coverage is 0%, so the description is the only source of parameter meaning. It compensates fully by documenting all six parameters, adding a query example, category slug guidance, a 0-100 quality range, USD price units, and a per_page maximum of 100.

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: 'Search the invok.it marketplace for AI agent tools using natural language.' This clearly differentiates from siblings like search_skills and search_apps by targeting 'AI agent tools,' so an agent can tell what this tool is for without reading 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 Guidelines4/5

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

The description provides clear usage context: natural-language queries, optional filters, pagination, and a pointer to list_categories for valid category slugs. It does not explicitly state when to prefer this over get_tool, search_skills, or search_apps, so it stops short of full alternative routing.

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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