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Search the Agent402 catalog of 500+ pay-per-call tools and skill packs by natural language query. Find tools for data conversion, web search, LLM inference, and more with pricing and input details.

Instructions

Search the full Agent402 catalog (525 pay-per-call tools: live market data like stock-quote at $0.003, encoding, crypto, data conversion, text, time, validation, math, unit conversions, network, browser, memory). Many pure-CPU tools are free via proof-of-work — no wallet needed. There is also an OpenAI-compatible LLM gateway at https://agent402.tools/v1, flat per-call (chat nano $0.003, auto $0.01, embeddings $0.002) with no API key — a funded wallet is the account; its tiers are callable here via call_tool (slugs v1-chat-nano, v1-chat-auto, v1-embeddings) when a wallet key is set. Returns matching tools with price, payment options, and input schema — call them with call_tool. Also returns matching multi-tool workflow templates (skill packs) when the query is task-shaped; fetch the whole template via prompts/get { name: "", arguments: { … } }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default 10)
queryYesWhat you need, e.g. "convert miles to km", "decode JWT", "cron next run"
Behavior4/5

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

Discloses the scope of the catalog, pricing models (pay-per-call, free via proof-of-work), and the LLM gateway. Without annotations, it adequately covers 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.

Conciseness4/5

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

The description is dense but all information is relevant. Could be slightly more structured, but no wasted sentences.

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?

Given no output schema, the description explains what is returned and how to proceed. Sufficient for a search tool.

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?

Schema covers both parameters fully, and the description adds concrete query examples that enhance understanding beyond the schema.

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 clearly states it searches the Agent402 catalog for tools and workflow templates, returning matches with pricing and schema. It distinguishes itself from sibling tools by being the search entry point.

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?

Provides query examples and explains that found tools are callable via call_tool. Does not explicitly state when not to use it, but the context of search is clear.

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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