Excel MCP Server

by ishayoyo

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correlation_analysis

Calculate correlation between two numeric columns in Excel or CSV files to identify statistical relationships in data.

Instructions

Calculate correlation between two numeric columns

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": {
      "filePath": "<filePath>",
      "column1": "<column1>",
      "column2": "<column2>"
    },
    "name": "correlation_analysis"
  }
}

Input Schema

Name	Required	Description
`filePath`	Yes	Path to the CSV or Excel file
`column1`	Yes	First column name or index (0-based)
`column2`	Yes	Second column name or index (0-based)
`sheet`	No	Sheet name for Excel files (optional)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jan 2, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It states what the tool does but doesn't mention critical behaviors like what correlation method is used (e.g., Pearson), error handling for non-numeric data, performance characteristics, or output format. For a statistical tool with zero annotation coverage, this leaves significant gaps.

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 a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose and appropriately sized for a straightforward tool, making it easy for an agent to parse quickly.

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 of statistical analysis, lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., correlation coefficient, p-value), error conditions, or behavioral nuances. For a tool with 4 parameters and no structured output definition, more context is needed.

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 schema fully documents all parameters. The description doesn't add any semantic context beyond what's in the schema (e.g., explaining how column names/indices work together, file format limitations, or the optional 'sheet' parameter's role). Baseline 3 is appropriate when the schema does all the heavy lifting.

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 the action ('Calculate correlation') and the target ('between two numeric columns'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'statistical_analysis' or 'smart_data_analysis' that might offer similar functionality, preventing a perfect score.

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. With multiple sibling tools that might perform statistical operations (e.g., 'statistical_analysis', 'trend_analysis'), there's no indication of when this specific correlation calculation is preferred or what prerequisites exist (e.g., numeric data requirements).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.