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111-test-111

Math MCP Server

by 111-test-111

statistics_analyzer

Perform comprehensive statistical analysis on datasets, including descriptive statistics, hypothesis testing, and distribution fitting.

Instructions

Brief description: Comprehensive statistical analysis tool, supporting descriptive statistics, hypothesis testing, and distribution analysis.
Examples:
    statistics_analyzer(data1=[1,2,3,4,5], analysis_type='descriptive')
    statistics_analyzer(data1=[1,2,3], data2=[4,5,6], analysis_type='comparison')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
data1YesPrimary dataset for analysis
analysis_typeYesType of statistical analysis. Supports: 'descriptive', 'distribution', 'hypothesis_test', 'correlation', 'regression', 'comparison'
data2NoSecondary dataset for comparison operations
test_typeNoStatistical test type. Supports: 't_test', 'chi_square', 'anova', 'kolmogorov_smirnov'
hypothesis_test_typeNoHypothesis test variant. Supports: 'one_sample', 'two_sample', 'paired'
confidenceNoConfidence level
distribution_typeNoDistribution type for analysis. Supports: 'normal', 'binomial', 'poisson'
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It does not mention side effects, memory usage, required permissions, or what happens with invalid inputs. The description is too minimal.

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 brief and includes examples immediately. It is front-loaded with the purpose and avoids unnecessary words.

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 complexity (7 parameters, no output schema, no annotations), the description is insufficient. It lacks details on return values, error handling, and limitations.

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 baseline is 3. The description adds example usage but does not provide additional semantic detail beyond the schema's parameter descriptions.

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 'comprehensive statistical analysis' and lists examples of use. However, it does not differentiate from sibling tools like 'regression_modeler' or 'complex_analysis_suite', which might offer similar functionality.

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 explicit guidance on when to use this tool versus alternatives. The description provides examples but no context about prerequisites, data size limits, or when not to use it.

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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