Excel MCP Server

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parse_natural_language

Convert natural language queries into Excel formulas or commands to analyze spreadsheet data without manual formula writing.

Instructions

Convert natural language to Excel formula or command

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": {
      "query": "<query>"
    },
    "name": "parse_natural_language"
  }
}

Input Schema

Name	Required	Description
`query`	Yes	Natural language query (e.g., "sum all sales", "find duplicates", "average by category")
`filePath`	No	Path to file for context (optional)
`provider`	No	Preferred AI provider: anthropic, openai, deepseek, gemini, or local (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. While 'convert' implies a transformation operation, it doesn't specify whether this requires external API calls (given the provider parameter), what happens with the optional filePath context, rate limits, error conditions, or what the output format looks like (formula string, command object, etc.).

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 that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded with the core functionality.

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 natural language to formula conversion, no annotations, and no output schema, the description is inadequate. It doesn't explain what kind of output to expect (Excel formula syntax, command objects, error handling), nor does it address the implications of the provider parameter for AI service usage.

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%, providing good documentation for all three parameters. The description adds no additional parameter semantics beyond what's already in the schema, so it meets the baseline of 3 where the schema does 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 tool's function as converting natural language to Excel formulas or commands, which is a specific verb+resource combination. However, it doesn't distinguish itself from sibling tools like 'evaluate_formula' or 'explain_formula' that also work with formulas, leaving some ambiguity about its unique role.

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 many sibling tools for Excel operations (evaluate_formula, explain_formula, data_cleaner, etc.), there's no indication of whether this is for initial formula generation, debugging, or other specific contexts.

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