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compute_indicator

Validate an indicator's behavior on a small price array before integrating it into a strategy, testing parameters and window effects.

Instructions

Run a single FLOX indicator over a list of floats and return the output. Use this to sanity-check an indicator's behaviour on a small price array BEFORE wiring it into a strategy — especially when the indicator has window / period / smoothing parameters whose effect isn't obvious from the name. Input is capped at 1 MiB. Requires the optional flox-py dependency.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesIndicator name in flox_py — case either matches the class (`EMA`, `RSI`, `Bollinger`) or the function (`ema`, `rsi`, `vwap`).
dataYesInput series (e.g. close prices). Up to 125k samples.
Behavior4/5

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

Without annotations, description discloses input cap (1 MiB), data limit (125k samples), and optional dependency. Does not explicitly state side-effect-free nature or return format, but the context implies a pure computation. Minor gap.

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?

Three focused sentences: purpose, usage guidance, constraints. No wasted words, front-loaded with key action.

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?

Covers purpose, usage, constraints, and dependency. Without output schema, it could specify return type more precisely, but still adequate for an agent to understand core function.

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%, but description adds naming convention for the name parameter (case sensitivity, class vs function). This adds value beyond the schema description.

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?

Clearly states verb (run) and resource (FLOX indicator) with specific use case: sanity-check before wiring into strategy. distinguishes from siblings like list_indicators or suggest_indicator by focusing on execution.

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 advises to use before wiring into a strategy, especially for indicators with non-obvious parameters. Mentions input cap and dependency constraints, providing clear when-to-use context.

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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