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scientificmethod

Apply structured scientific reasoning to analyze questions by guiding through hypothesis testing, variable identification, prediction making, and evidence evaluation.

Instructions

A detailed tool for applying formal scientific reasoning to questions and problems. This tool guides models through the scientific method with structured hypothesis testing. It enforces explicit variable identification, prediction making, and evidence evaluation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stageYes
observationNo
questionNo
hypothesisNo
experimentNo
analysisNo
conclusionNo
inquiryIdYesUnique identifier for this scientific inquiry
iterationYesCurrent iteration of the scientific process
nextStageNeededYesWhether another stage is needed in the process

Implementation Reference

  • The `processScientificMethod` method is the core handler for the "scientificmethod" tool. It validates the input, processes the hypothesis and experiment data, formats the output for the console, and returns a JSON-formatted MCP-compliant response.
    public processScientificMethod(input: unknown): { content: Array<{ type: string; text: string }>; isError?: boolean } {
      try {
        const validatedData = this.validateInputData(input);
        const processedData: ScientificInquiryData = {
          ...validatedData,
          hypothesis: this.processHypothesis(validatedData.hypothesis),
          experiment: this.processExperiment(validatedData.experiment)
        };
        
        const formattedOutput = this.formatOutput(processedData);
        console.error(formattedOutput);
    
        return {
          content: [{
            type: "text",
            text: JSON.stringify({
              stage: processedData.stage,
              inquiryId: processedData.inquiryId,
              iteration: processedData.iteration,
              nextStageNeeded: processedData.nextStageNeeded,
              status: 'success'
            }, null, 2)
          }]
        };
      } catch (error) {
        return {
          content: [{
            type: "text",
            text: JSON.stringify({
              error: error instanceof Error ? error.message : String(error),
              status: 'failed'
            }, null, 2)
          }],
          isError: true
        };
      }
    }
Behavior3/5

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

Without annotations, the description carries full behavioral disclosure burden. It mentions the tool 'enforces' explicit variables and 'guides' through the method, hinting at validation behavior. However, it omits crucial details for a complex stage-based workflow: whether stages must be completed sequentially, what happens if required nested fields are missing, or whether inquiries persist between calls.

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

Conciseness3/5

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

Three sentences with moderate redundancy ('detailed tool' is filler; 'scientific reasoning' and 'scientific method' overlap). Content is front-loaded but could be tighter. Not bloated, but phrases like 'guides models through' and 'enforces explicit' compete for similar meaning without advancing distinct information.

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 high complexity (10 parameters, deeply nested objects with 6 required sub-fields, state-machine stages), lack of annotations, and no output schema, the description is insufficient. It fails to explain the iterative workflow indicated by 'iteration' and 'nextStageNeeded' parameters, the relationship between stage enum values and their corresponding payload fields, or what successful execution produces.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 30% (low), with 7 of 10 parameters undocumented including complex nested objects like 'hypothesis' and 'experiment'. While the description mentions concepts that map to these parameters (hypothesis testing, variables, predictions), it fails to explain the required object structures, enum meanings, or relationships between the stage parameter and its corresponding content fields.

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 identifies the tool applies 'formal scientific reasoning' and 'structured hypothesis testing' with specific activities like variable identification and evidence evaluation. However, it fails to differentiate from sibling tools like 'sequentialthinking' or 'structuredargumentation' which also involve step-by-step reasoning workflows.

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?

No guidance provided on when to select this tool versus the numerous sibling reasoning tools (collaborativereasoning, decisionframework, etc.). No mention of prerequisites, scientific domain requirements, or when alternative reasoning approaches would be more appropriate.

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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