ScorpTech x402 Tools

Power Mod

power_mod

Read-onlyIdempotent

a to the power e, reduced modulo m. 2^10 mod 1000 is 24, 3^5 mod 7 is 5, and (-2)^3 mod 7 is 6. A zero exponent gives 1, including 0^0, except modulo 1 where every power is 0. e runs from 0 to 1000000000000, and m runs from 1 to 1000000000000. Price: $0.001 USDC per successful call (x402, Solana). Same tool over HTTPS: POST https://scorptech.it.com/api/x402/v1/power-mod

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "a": "<a>",
      "e": "<e>",
      "m": "<m>"
    },
    "name": "power_mod"
  }
}

Input Schema

Name	Required	Description	Default
`a`	Yes
`e`	Yes
`m`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 23, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already establish readOnlyHint, idempotentHint, and destructiveHint=false, while the description adds useful context beyond them: edge cases like 0^0 and modulo 1, plus pricing ($0.001 per successful call), x402/Solana payment, and an HTTPS endpoint. It does not discuss rate limits beyond the per-call price, but it meaningfully supplements the annotations.

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 front-loads the formula, then gives examples, edge cases, ranges, and finally pricing/endpoint details. It is appropriately compact for a simple three-parameter math tool, though the payment/endpoint sentence is auxiliary rather than core semantic guidance.

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 read-only modular exponentiation tool with no output schema, the description covers the operation, examples, edge cases, and most parameter ranges. The main remaining gaps are the explicit range for a and an explicit statement of the return type, though both are inferable from the schema and formula.

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 description coverage is 0%, so the description must carry parameter meaning. It defines a via the formula and examples, explicitly bounds e to 0..1000000000000 and m to 1..1000000000000, and covers edge cases involving the exponent and modulus. It omits a's explicit bounds, which are only in the schema, so it does not fully compensate for the coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the operation: a to the power e, reduced modulo m, with concrete examples. It is specific enough to distinguish the math operation itself, but it does not mention sibling tools such as mod_pow, so it falls short of full sibling differentiation.

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?

Usage is implied by the formula and examples, but the description gives no explicit when-to-use guidance, exclusions, or alternative tools. For a simple math operation this is adequate, but it leaves the agent to infer why to choose this over a similar sibling.

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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