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LuxAlgo

LuxAlgo Library MCP

Official
by LuxAlgo

Get one journal trade

journal_get_trade
Read-only

Retrieve full details for a specific LuxAlgo journal trade by key, including fills, gross P&L, hidden entries, and annotations. Use after listing trades or before annotating; requires LuxAlgo OAuth.

Instructions

One trade in full: the summary fields plus its fills (each with the effective values, what the source reported, the user's corrections and whether it is hidden), per-exit gross P&L, hidden fills inside the trade's span, and every annotation — notes, tags, mistakes, playbook id, stop loss, profit target, review time. Use after journal_list_trades or journal_get_day when the user asks about a specific trade or before annotating it. Requires signing in with a LuxAlgo account (OAuth).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesThe trade's `key` exactly as returned by journal_list_trades, journal_get_day, journal_search_notes or journal_add_trade. Never construct one.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.5.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so safety is covered. The description adds real behavioral context beyond them: the OAuth/LuxAlgo account requirement and the fact that hidden fills and user corrections are exposed in the response. It stops short of noting pagination or payload size, but for a single-record read that is minor.

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?

Front-loaded with the returned payload, then usage guidance, then auth. The long field enumeration is dense but earns its place because there is no output schema; still, it could be tightened slightly for readability.

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?

With no output schema, the description compensates by itemizing the response contents, and it covers the trigger, prerequisites, and auth requirement. An agent has everything needed to select and invoke this tool 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 the schema's own description already instructs that the key must come from sibling tools and never be constructed. The body text adds no syntax or format detail about the key, so the baseline 3 applies — the schema does the heavy lifting here.

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?

States a specific verb and resource — fetch one trade in full — and enumerates exactly what the payload contains (summary fields, fills, per-exit gross P&L, hidden fills, annotations). This distinguishes it cleanly from list/day/search siblings that return multiple trades or partial data.

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?

Explicitly says when to call it: 'Use after journal_list_trades or journal_get_day when the user asks about a specific trade or before annotating it.' It names the prerequisite siblings and the downstream workflow (annotating), leaving nothing to inference.

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