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wshobson

MaverickMCP

analyze_market_regimes

Identify market regimes for any stock using machine learning methods like HMM, K-means, or threshold analysis. Choose number of regimes and lookback period for custom detection.

Instructions

Analyze market regimes for a stock using ML methods.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesStock symbol to analyze
start_dateNoStart date for analysis
end_dateNoEnd date for analysis
methodNoDetection method (hmm, kmeans, threshold)hmm
n_regimesNoNumber of regimes to detect
lookback_periodNoLookback period for regime detection

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

With no annotations, the description carries the full burden. It only says 'using ML methods' without detailing what those methods are, what the tool does internally, or any side effects. This is insufficient for a complex analytical tool with multiple 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 sentence with no unnecessary words. It is as concise as possible while still conveying the core purpose.

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 6 parameters and an output schema, the description is too brief. It does not explain the return format, the significance of the 'method' parameter, or how to interpret results. More context is needed for effective use.

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%, so the schema already documents all parameters. The tool description does not add any additional meaning beyond what the schema provides. Baseline 3 is appropriate.

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 analyzes market regimes for a stock using ML methods. It specifies the resource (stock market regimes) and the action (analyze). However, it does not differentiate from siblings like 'get_market_regime' which may have similar purposes.

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 guidance on when to use this tool versus alternatives such as 'get_market_regime' or 'backtest_signal'. It lacks any 'when to use' or 'when not 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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