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get_track_record_detail

Retrieve day-by-day track-record receipts for each scored symbol, showing the published level and whether the session held it. Filter by symbol to audit accuracy per ticker.

Instructions

Authenticated track-record detail — the day-by-day report card behind the public accuracy headline. For each scored day it lists the level published that morning (call_wall / put_wall / max_pain / expected_move) and whether the session respected it: the receipts behind the percentage.

Plan: Pro and up. (The public, aggregate version is the keyless track_record tool.)

Args: symbol: Optional ticker to filter the report to one name; omit for all scored symbols.

Returns the detail JSON: days, outcomes[] (newest first — each with date, symbol, spotOpen, dayHigh, dayLow, dayClose, level, value, held, detail) and updatedAt.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.7/5.0
Behavior4/5

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 covers authentication/plan requirements, the per-day structure, newest-first ordering, and the exact field list in the response. It stops short of 5 only because it does not mention auth mechanism, error behavior, or rate limits.

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 front-loaded with purpose and uses a compact Args/Returns structure that is easy to scan. It is slightly more elaborate than necessary with metaphors like 'report card' and 'receipts,' but every sentence contributes useful context, so the extra prose is forgivable.

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?

Although there is no output schema, the description enumerates the return shape completely: `days`, `outcomes[]` with each field, and `updatedAt`. Combined with the plan requirement and sibling comparison, an agent has everything needed to invoke this tool correctly and interpret its result.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must fully compensate for the schema. It does: `symbol` is explained as an optional ticker filter, with explicit guidance to omit it for all scored symbols. This adds real semantic value beyond the raw anyOf string/null schema.

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 defines this as the authenticated, day-by-day track-record detail tool and contrasts it with the public aggregate version. It specifies the exact scoped content: per-day levels published and whether the session respected them. The mention of `track_record` as the keyless aggregate sibling disambiguates it from related tools.

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?

It explicitly states the Plan requirement ('Pro and up') and names the alternative: the public, aggregate version is the keyless `track_record` tool. It also explains when to pass `symbol` versus omit it, giving clear selection guidance without ambiguity.

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