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calculate_staking_apr_decay

Calculate reward distribution curves and halving decay for proof-of-stake or liquidity farming smart contracts to model APR over time.

Instructions

Calculates reward distribution curves and halving decay for proof-of-stake or liquidity farming smart contracts. (0.015 USDC on Base L2)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
payloadYesInput parameters or JSON string payload for the tool execution
paymentSignatureNoBase L2 USDC micropayment signature or transaction hash for x402 settlement

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It discloses a cost (0.015 USDC on Base L2) and implies a calculation, but does not state whether the payment is required upfront, what side effects occur, what the output format is, or whether this is a read-only operation. For a tool that involves a payment, this is a significant transparency gap.

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?

A single sentence with no waste. It front-loads the core purpose and includes the essential cost information. The sentence is efficient and easy to parse, though it could be more structured with additional context.

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

Completeness2/5

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

Given the complexity of staking APR calculations and the lack of an output schema or annotations, this description is incomplete. It does not explain the expected payload format, the exact calculation method, or the return value. The cost is mentioned but not the settlement mechanics. An agent would need to guess many details to call this correctly.

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

Parameters3/5

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

Schema coverage is 100% and both parameters (payload, paymentSignature) are described in the schema. The description does not add any semantic detail beyond the schema, such as expected JSON structure for payload or how paymentSignature is used. Since the schema already documents the parameters, baseline 3 is appropriate.

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?

States a clear verb ('Calculates') and a specific resource ('reward distribution curves and halving decay for proof-of-stake or liquidity farming smart contracts'). This differentiates it from sibling tools like calculate_compound_yield or calculate_impermanent_loss, though it doesn't explicitly name alternatives. The scope is well-defined.

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?

Mentions the applicable contexts (proof-of-stake or liquidity farming) but provides no explicit when-to-use vs alternatives or exclusions. An agent would have to infer usage from the domain context alone; no comparison to sibling tools is offered.

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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