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interpret_result

Interpret p-values correctly and produce APA-style conclusions with effect size and confidence interval, avoiding common misinterpretations.

Instructions

Interpret a p-value correctly and produce a defensible, APA-style conclusion.

Guards against the classic mistakes: a non-significant result does NOT prove the null, and statistical significance is not practical importance (report effect size + CI).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "p_value": "<p_value>"
    },
    "name": "interpret_result"
  }
}

Input Schema

Name	Required	Default
`alpha`	No
`p_value`	Yes
`test_name`	No	the test
`effect_size`	No
`effect_size_type`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 9, 2026

TDQS

B3.3/5.0

Behavior3/5

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

No annotations exist, so the description carries the full burden. It discloses the tool guards against common mistakes and produces a conclusion, but does not describe side effects, error handling, or output format sufficiently.

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 two sentences long, front-loads the purpose, and wastes no words. Every sentence adds value.

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 5 parameters and no output schema, the description is incomplete. It fails to explain parameter roles or the output format, leaving the agent with insufficient guidance.

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

Parameters1/5

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

Schema coverage is 0% and the description does not explain any parameter beyond what field names suggest. Parameters like alpha, test_name, effect_size_type are not described, leaving significant ambiguity.

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 verb ('Interpret') and resource ('p-value'), and specifies an APA-style conclusion as output. It distinguishes from sibling tools (e.g., check_assumptions, recommend_test) by focusing on result interpretation.

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 description implies the tool is used after obtaining a p-value to get a correct interpretation, but does not explicitly state when not to use it or provide alternatives among siblings.

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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