Clear Thought MCP Server

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

Name	Required	Description
`stage`	Yes
`observation`	No
`question`	No
`hypothesis`	No
`experiment`	No
`analysis`	No
`conclusion`	No
`inquiryId`	Yes	Unique identifier for this scientific inquiry
`iteration`	Yes	Current iteration of the scientific process
`nextStageNeeded`	Yes	Whether another stage is needed in the process

Implementation Reference

src/tools/scientificMethodServer.ts:171-207 (handler)

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
    };
  }
}

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.1.2Mar 25, 2026

TDQS

B3.1/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool 'guides models through the scientific method' and 'enforces explicit variable identification, prediction making, and evidence evaluation,' which suggests it's a structured reasoning process rather than a data operation. However, it doesn't disclose whether this is a read-only tool, whether it stores or modifies data, what permissions might be needed, or what the output format looks like. For a complex tool with 10 parameters, this leaves significant behavioral gaps.

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 appropriately concise with three sentences that efficiently convey the tool's purpose and approach. It's front-loaded with the main function ('applying formal scientific reasoning') and follows with supporting details. There's no wasted text, though it could be slightly more structured with bullet points given the complexity.

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, nested objects, no output schema, and 30% schema coverage), the description is insufficiently complete. It doesn't explain how to use the tool across multiple stages, what the expected inputs/outputs are, or how the scientific method iteration works in practice. For such a sophisticated tool, the description should provide more context about workflow and expected outcomes.

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 only 30%, so the description should compensate but doesn't. The description mentions 'structured hypothesis testing,' 'variable identification,' 'prediction making,' and 'evidence evaluation,' which loosely map to some parameters like 'hypothesis' and 'experiment,' but it provides no specific guidance on parameter usage, relationships between parameters, or how to structure the complex nested objects. The description adds minimal value beyond the schema.

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?

The description clearly states the tool's purpose: 'applying formal scientific reasoning to questions and problems' and 'guides models through the scientific method with structured hypothesis testing.' It specifies the verb ('guides through') and resource ('scientific method'), and distinguishes from siblings by focusing on formal scientific reasoning rather than other reasoning approaches like collaborative reasoning or debugging.

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. It mentions 'structured hypothesis testing' but doesn't specify contexts where scientific reasoning is preferred over other reasoning tools like 'collaborativereasoning' or 'decisionframework.' There are no explicit when/when-not instructions or named alternatives.

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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