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DropEngine x402 Agent Services

backtest_strategy

Paid Strategy Backtest ($0.10 USDC). Tests built-in long-only spot strategies against bounded public Binance OHLCV history. Signals form at candle close and execute at the next candle open. Applies configured fees/slippage; accepts declarative parameters only and never executes user code. Historical results are not predictions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endYes
startYes
marketYes
fee_bpsNo
strategyYes
timeframeYes
slippage_bpsNo
initial_capitalYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
metaYes
toolYes
successYes
timestampYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior5/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It covers the cost, data scope, execution timing (candle close to next open), fee/slippage application, declarative-only inputs (no code execution), and a warning that results are not predictions. This is comprehensive for a paid backtesting tool.

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 concise, front-loads the cost and purpose, and uses a structured sequence: purpose, data, execution, fees/safety, and disclaimer. Each sentence earns its place with no fluff. The semicolon usage is efficient. Very well-structured.

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

Completeness3/5

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

For a complex tool with 8 parameters, a nested strategy object, and an output schema, the description covers many behavioral aspects (cost, timing, safety) but fails to explain what each parameter does. Since schema descriptions are absent and coverage is 0%, the description should at least summarize key parameters. The output schema exists, which partially mitigates, but parameter semantics remain unclear.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate by explaining parameter meanings. It does not mention any parameter by name (market, timeframe, start, end, initial_capital, strategy, fee_bps, slippage_bps). Even the strategy enum is not elaborated. The description only broadly mentions 'declarative parameters' without adding value to the schema. This is a significant gap for an 8-parameter tool.

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 a specific verb ('tests') and a specific resource ('built-in long-only spot strategies against bounded public Binance OHLCV history'). It also includes the cost ($0.10 USDC), making it unique and distinct from the unrelated sibling tools. The purpose is unmistakable.

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?

The description clearly implies when to use this tool (for backtesting strategies) and provides context about the data source and execution model. However, it does not explicitly mention alternatives or when not to use it. Since no sibling is related to backtesting, differentiation from alternatives is less critical, but explicit exclusions would have improved this score.

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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