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longbridge

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Quant — Run Indicator Script

quant_run
Read-onlyIdempotent

Execute a quant indicator script against historical K-line data server-side. Returns computed indicator values as JSON.

Instructions

Run a quant indicator script against historical K-line data on the server. Executes the script server-side and returns the computed indicator/plot values as JSON. Periods: 1m, 5m, 15m, 30m, 1h, day, week, month, year (default: day). The optional input parameter accepts a JSON array matching the order of input.*() calls in the script, e.g. "[14,2.0]".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesSymbol in <CODE>.<MARKET> format, e.g. TSLA.US, 700.HK
periodNoK-line period: 1m, 5m, 15m, 30m, 1h, day, week, month, year (default: day)day
startYesStart date (YYYY-MM-DD) for the K-line range
endYesEnd date (YYYY-MM-DD) for the K-line range
scriptNoIndicator script source.
inputNoScript input values as a JSON array, e.g. "[14,2.0]". Must match the order of input.*() calls in the script.
Behavior3/5

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

Annotations already label the tool as read-only, idempotent, and non-destructive. The description adds that execution is server-side and returns computed values, which aligns. However, it does not elaborate on potential side effects, performance implications, or error behavior, so it adds limited value beyond annotations.

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?

Two focused sentences with no extraneous information. Each sentence provides essential operational details (purpose, return type, period options, input format).

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?

The description covers the core functionality and key parameters. However, for a script execution tool with no output schema, more details on response structure, error handling, or script constraints would enhance completeness.

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%, so the description adds only minor clarification (e.g., period default 'day' and input format example). These are largely redundant with the schema descriptions.

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 the action ('Run'), the resource ('quant indicator script'), and the context ('against historical K-line data'). It specifies server-side execution and JSON return, distinguishing it from other tools that deal with data retrieval or actions on orders, watchlists, etc.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no explicit guidance on when to use this tool versus alternatives, nor does it mention when not to use it. The mention of periods and input format is instructional but not comparative or contextual.

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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