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longbridge

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Quant — Run Indicator Script

quant_run
Read-onlyIdempotent

Execute quant indicator scripts on historical K-line data. Compute and return plot values as JSON for analysis.

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
endYesEnd date (YYYY-MM-DD) for the K-line range
inputNoScript input values as a JSON array, e.g. "[14,2.0]". Must match the order of input.*() calls in the script.
startYesStart date (YYYY-MM-DD) for the K-line range
periodNoK-line period: 1m, 5m, 15m, 30m, 1h, day, week, month, year (default: day)day
scriptNoIndicator script source.
symbolYesSymbol in <CODE>.<MARKET> format, e.g. TSLA.US, 700.HK
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive, and open-world hints. The description adds that execution is server-side and returns indicator/plot values as JSON, providing useful behavioral context beyond the 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 sentences plus a period list and input example. All information is front-loaded and no filler. Every sentence adds value.

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 no output schema, the description explains the output is JSON of computed values. It covers main parameters and execution. However, it lacks details on error handling or script syntax, but is adequate for a script-running tool.

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 100%, so baseline is 3. The description adds value by explaining the period options (listing them with default) and the input parameter's format with an example, clarifying its relationship to script inputs.

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 tool runs a quant indicator script against historical K-line data, specifying the verb 'run' and resource 'quant indicator script'. It is distinct from sibling tools as none appear to execute user-provided scripts.

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

Usage Guidelines3/5

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

The description does not explicitly guide when to use this tool versus alternatives. It states the tool runs server-side and returns JSON, but does not mention prerequisites or compare to other indicator-related tools among siblings.

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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