DPYC Oracle

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economic_model

Explain the DPYC Social Contract economic model: value flow, certification fees, and cascade to the First Curator, without quoting specific rates or revenue figures.

Instructions

Explain the DPYC Social Contract economic model — qualitatively.

Describes how value flows through the network: ad valorem certification fees, the cascade up the Certification Chain to the First Curator, and where the live numbers actually live. The Oracle quotes no rates, counts, or revenue figures — those belong to the Authorities' pricing models and the live registry, not to a docent. Free, unauthenticated.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "economic_model"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Sep 15, 2026

TDQS

A4.9/5.0

Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states that the tool provides qualitative explanation only, avoids all quantitative figures, and is 'Free, unauthenticated.' It also indicates the tool is a 'docent' rather than a live data source, which is valuable behavioral context.

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 exceptionally concise: three sentences that front-load the purpose, then add critical boundary information (no quantitative data, free/unauthenticated). Every sentence earns its place with no filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a no-parameter, qualitative explanation tool with an output schema, the description covers the essential aspects: what the tool explains (value flows, certification fees, cascade, first curator), where live data lives, and what it deliberately does not cover. This is complete for the tool's intended use.

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 tool takes zero parameters, so the schema provides no parameter information. The baseline for no-parameter tools is 4. The description appropriately focuses on content and behavior rather than parameters, which is sufficient given the absence of inputs.

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 opens with a specific verb and resource: 'Explain the DPYC Social Contract economic model — qualitatively.' It clearly identifies the tool's function and distinguishes it from quantitative tools like get_tax_rate by emphasizing the qualitative nature and stating that it provides no rates or figures.

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

Usage Guidelines5/5

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

The description explicitly states when NOT to use this tool: 'The Oracle quotes no rates, counts, or revenue figures — those belong to the Authorities' pricing models and the live registry, not to a docent.' This clearly redirects users to alternative sources for quantitative data, providing strong usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.