quantifyme

Show Backtest Chart (equity + trades)

get_model_chart

Read-onlyIdempotent

Visualize a trained model's backtest — a cumulative-return chart + trade log + stats.

Use after `one_shot` / `list_models` with the model's `stem` to SHOW the user how it
traded (the "is it actually any good" view). In ChatGPT this renders an interactive
widget. In Claude, render an interactive **artifact** from this tool's structured
output: a line chart of the cumulative return plus a table of the trades.

Args:
  stem: The model stem (e.g. "14_EURUSD_15min_Model_24") from `list_models` / `one_shot`.

Returns:
  dict with: ok, stem, symbol, timeframe, stats {ret, wr, pf, n, mdd, sharpe},
  and trades [{type, entry_time, exit_time, entry_price, exit_price, pnl,
  pnl_pct, exit_reason, period}] (most recent ~200). exit_reason is one of
  TP / SL / close_only / signal / end. ret/mdd/wr are fractions; pnl_pct is percent.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`stem`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. The description adds valuable behavioral details: rendering difference between ChatGPT (interactive widget) and Claude (artifact), and the structured output includes trade log and stats. No contradictions.

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?

Description is front-loaded with purpose, usage, parameter explanation, and return value. Each sentence adds distinct value without redundancy. Despite length, it is well-organized and efficient.

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?

Given the simple single-parameter tool and presence of an output schema, the description thoroughly covers behavior, output structure (even detailing keys like exit_reason values), and platform-specific behavior. No gaps.

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

Parameters4/5

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

Schema coverage is 0%, but the description explains the 'stem' parameter with an example and its source (list_models/one_shot). This adds meaning beyond the bare schema, but could further specify format or constraints.

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?

Description uses specific verb 'Visualize' and resource 'trained model's backtest', clearly distinguishing it from sibling tools like list_models and one_shot by focusing on the chart view.

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 states to use after one_shot or list_models with the model's stem, and explains it shows the user how it traded, including context like 'is it actually any good' view. No mention of when not to use, but the guidance is very clear.

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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TDQS

A3.9/5.0

Disambiguation4/5

Most tools have clearly distinct purposes, though browse_community, find_strategy, and one_shot (community_id) are related and could cause confusion if descriptions are not read carefully. Overall well-differentiated.

Naming Consistency4/5

Overwhelmingly follows a verb_noun pattern (browse_community, find_strategy, generate_strategy, get_deploy_result, etc.). A few exceptions like 'one_shot' and 'top_up' break the pattern but are still clear and memorable.

Tool Count4/5

13 tools cover the major functions of a trading strategy platform (discover, generate, deploy, monitor, account management). The count feels appropriate, though 'stream_test' is diagnotic and may not be needed in all contexts.

Completeness3/5

Covers core workflows well—browse, find, generate, deploy, and get results. However, lacks tools to update or stop a deployed model, and there's no way to edit an existing strategy, leaving some lifecycle gaps.

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