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OV3RK177

Kairos Signal MCP Server

by OV3RK177

purchase_data

Buy DePIN telemetry, DAG APIs, signal feeds, GPU compute, and MCP unlimited with your credits. Use the api_key from register_agent to complete transactions.

Instructions

Buy a product with your credits. Products: DePIN supply-telemetry snapshots, DAG manifold API (dag_pro/dag_quant/dag_enterprise), signal feed tiers, GPU compute, MCP unlimited. Use api_key from register_agent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
api_keyYesYour API key from register_agent
product_keyYesProduct key to purchase (e.g. dag_pro, mcp_unlimited)
Behavior3/5

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

The description discloses the key behavior—credits are spent and an api_key is required—so the agent knows this is a purchase/billing action. However, with no annotations provided, it does not disclose failure modes, irreversibility, or what happens after a successful purchase beyond the immediate action.

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 only two sentences long, starts with the core action, and packs product context and api_key guidance without filler. It is easy to scan and no sentence is redundant with 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 two-parameter tool with 100% schema coverage and no output schema, the description is largely sufficient: it names the products, indicates the purchase action, and points the agent to the proper api_key source. A small gap is that it does not explicitly direct the agent to list_products for a definitive product catalog, but the sibling context makes this recoverable.

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 already describes both parameters and gives example values. The description adds useful semantic context beyond the schema by listing the categories of purchasable products (for example GPU compute, MCP unlimited, and DAG manifold tiers), which helps the agent reason about valid product_key values.

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 clear user action ('Buy a product with your credits') and specifies the resource type, then enumerates supporting product categories keyed to product_key values. This clearly distinguishes the tool from siblings like register_agent, list_products, and topup_credits.

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 context is implied rather than explicit: you purchase a product using credits, and you need an api_key from register_agent. There is no explicit instruction about when not to use it, such as pointing to list_products for browsing products or topup_credits for adding credits.

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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