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longbridge

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Quant — Run Indicator Script

quant_run
Read-onlyIdempotent

Run quant indicator scripts against historical K-line data to compute and return 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.
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds value by specifying that execution is server-side, returns JSON, and details the period parameter and input format. This goes beyond the annotations to provide functional context.

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?

The description is concise with no unnecessary words. It is front-loaded with the main action and efficiently covers key details in two sentences plus one more for input.

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?

No output schema exists, but the description explains that the return is computed indicator values as JSON. Parameters are well-covered. It could mention error handling or performance implications, but overall it is contextually sufficient for the tool's purpose.

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 description coverage is 100%, so baseline is 3. The description adds value by listing allowed periods with defaults and explaining the input parameter format with an example. This enhances understanding beyond the 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 states the action (run a quant indicator script), the resource (historical K-line data), and the outcome (returns computed values as JSON). It distinguishes from sibling tools like candlesticks or quote by focusing on custom script execution.

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 does not provide explicit guidance on when to use this tool versus alternatives. It lacks any mention of prerequisites, suitable scenarios, or when not to use it, leaving the agent with insufficient context for tool selection.

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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