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indicator_psar

Compute Parabolic SAR (PSAR) for any trading pair to identify stop-and-reverse points for trend following. Customize symbol, timeframe, and acceleration settings.

Instructions

Calculate the Parabolic SAR (PSAR) - provides stop-and-reverse points for trend following

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of OHLCV data points to fetch
symbolYesTrading pair, e.g., 'BTC/USDT'
timeframeNoTimeframe, e.g., '1m', '1h', '1d'1h
accelerationFactorMaxNoMaximum acceleration factor
accelerationFactorStepNoAcceleration factor step
Behavior2/5

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

With no annotations provided, the description must carry the full burden of behavioral disclosure. It only states the calculation purpose and does not mention data fetching requirements, output format, edge cases, or any side effects. This is minimal for a tool that likely relies on OHLCV data and has multiple tuning parameters.

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 a single, concise sentence that front-loads the primary purpose and provides a brief functional elaboration. There is no fluff or redundant information, every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 5 parameters, no output schema, and no annotations, the description is too minimal to be considered complete. It lacks critical context about return values, how the acceleration factors affect results, and how the tool fits with related indicators. This makes it inadequate for a user to fully understand the tool's capabilities.

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 description coverage is 100% for all 5 parameters, so the schema already documents the meaning of each parameter. The description adds no additional parameter context beyond what is in the schema, which is the baseline expectation for high coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool calculates the Parabolic SAR and provides stop-and-reverse points, which is a specific and meaningful purpose. It goes beyond a simple restatement of the name, though it does not explicitly differentiate from sibling indicator tools beyond the indicator name itself.

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 phrase 'provides stop-and-reverse points for trend following' implies the use case of trend following, but there is no explicit guidance on when to choose this over other indicators or the related strategy_psar tool. It gives a functional hint without clear alternatives or exclusion criteria.

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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