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Aether-Grid

@sentimentracker/signals-mcp

by Aether-Grid

Compose Hyperliquid order payload

propose_trade_from_signal
Read-onlyIdempotent

Convert trading signals and sizing strategy into a Hyperliquid order payload. Choose notional, fixed base, or risk-based sizing, with optional take-profit and stop-loss for follow-up placement.

Instructions

PURE COMPUTE: compose a Hyperliquid propose_order payload from a signal envelope + sizing strategy. No IO. The result drops directly into @sentimentracker/hyperliquid-mcp's propose_order tool. Three sizing modes — notionalUsd / fixedBase / riskUsd (with stopLossPx). Optional takeProfitPx + stopLossPx surface a follow-up tip to invoke place_tpsl after the entry fills.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tifNo
coinYes
isPerpNo
markPxYes
signalYes
sizingYes
orderTypeNomarket
reduceOnlyNo
stopLossPxNo
takeProfitPxNo
limitOffsetBpsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description doesn't need to cover safety. It adds value by clarifying that the tool performs no IO, does not execute orders, and produces a payload for another tool, plus the follow-up tip about 'place_tpsl'. This is beyond what annotations provide.

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 is compact and front-loaded with the core purpose and no-IO claim. Every sentence adds value: the destination, sizing modes, and follow-up tip. It could be slightly more structured but is not verbose.

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?

Given the tool is a pure compute with a rich schema and multiple enums, the description covers the key aspects: purpose, no IO, sizing modes, and the output flow. It doesn't explain return values, but there is no output schema and the tool hints it drops into another tool, which is sufficient. Minor gap: no mention of how signal is used beyond type, but schema covers that.

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 description coverage is 0%, so the description must compensate. It does name the three sizing modes (notionalUsd, fixedBase, riskUsd) and mentions optional takeProfitPx/stopLossPx, which adds meaning. However, it doesn't explain many other parameters like tif, orderType, limitOffsetBps, or the structure of signal envelope, though the schema is fairly descriptive for those.

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 uses a specific verb 'compose' and identifies the exact resource (a Hyperliquid 'propose_order' payload) and its destination. It clearly distinguishes this tool from signal-generation and indicator sibling tools by stating it is 'pure compute' with 'no IO', which is a unique purpose.

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 states this tool is for composing a payload for a specific downstream tool, which implies when to use it (when preparing an order from a signal). It does not explicitly mention when not to use it or alternatives, but the strong 'no IO' and destination reference provide clear context and distinguish it from siblings.

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