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scientific_method

Formalize and test hypotheses through structured stages of observation, question, experiment, analysis, and conclusion.

Instructions

Formal hypothesis testing and experimentation following the scientific method.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stageYesThe current stage in the scientific inquiry process.
analysisNoThe interpretation of the experimental results and statistical analysis.
questionNoA specific question that arises from the observation (e.g., 'Why is user engagement low?').
inquiryIdYesA unique identifier for the entire scientific inquiry from observation to conclusion.
iterationYesThe iteration number of the inquiry process, for tracking cycles of refinement.
conclusionNoThe final conclusion drawn from the analysis, stating whether the hypothesis was supported or refuted.
experimentNoThe design and details of the experiment to test the hypothesis.
hypothesisNoThe formal hypothesis being investigated.
observationNoAn initial observation that sparks inquiry (e.g., 'The new feature has lower engagement than expected').
nextStageNeededYesA flag indicating whether the inquiry requires a subsequent stage.
Behavior2/5

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

With no annotations, the description fully bears the burden of behavioral disclosure. It only states the high-level purpose and does not describe what happens during the process (e.g., mutates state, requires certain stages to be set, or has side effects).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence that efficiently conveys the core purpose. However, it may be too brief given the complexity of the tool.

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's complexity (10 parameters, nested objects, no output schema), the description is far too minimal. It does not explain the workflow of stages, how to use the tool effectively, or what constitutes valid input.

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 baseline is 3. The description adds no additional meaning about the parameters beyond what the schema already provides.

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 it is for 'formal hypothesis testing and experimentation following the scientific method,' which is specific and aligns with the tool name. However, it does not distinguish this tool from sibling tools that also involve reasoning or structured thinking.

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. There is no mention of prerequisites, when to avoid, or how it fits with sibling tools like 'problem_decomposition' or 'structured_argumentation'.

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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