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rcarmo

office-document-mcp-server

by rcarmo

office_read

Read-only

Read content from Word, Excel, or PowerPoint documents, auto-detecting format from file extension, and output as JSON or markdown with optional scope for Excel ranges.

Instructions

Read content from Word, Excel, or PowerPoint documents.

Auto-detects document format from file extension and extracts content in the requested output format.

Replaces: word_extract, word_to_markdown, excel_extract, excel_to_markdown, excel_get_range, pptx_extract, pptx_to_markdown

Examples: # Read entire Excel file as JSON office_read(file_path="data.xlsx")

# Read Excel file as markdown
office_read(file_path="data.xlsx", output_format="markdown")

# Read specific range from Excel
office_read(file_path="data.xlsx", scope="Sheet1!A1:D10")

# Read a single worksheet
office_read(file_path="data.xlsx", scope="Sheet1")

# Read Word document as markdown
office_read(file_path="report.docx", output_format="markdown")

# Read PowerPoint as JSON
office_read(file_path="deck.pptx")

Args: file_path: Path to the document (.docx, .xlsx, .xlsm, .pptx) output_format: Output format - "json" for structured data, "markdown" for text representation scope: Optional scope limiter: - Excel: sheet name like "Sheet1" or range like "A1:D10" or "Sheet1!B2:C5" - Word: section title (not yet implemented) - PowerPoint: slide number like "slide:3" (not yet implemented) include_formulas: For Excel, return formulas instead of values

Returns: dict for JSON format, str for markdown format

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYesPath to the document (.docx, .xlsx, .xlsm, .pptx)
output_formatNoOutput format - "json" for structured data, "markdown" for text representation
scopeNoOptional scope limiter
include_formulasNoFor Excel, return formulas instead of values
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds auto-detection behavior, parameter details (e.g., scope specifics per document type), return type info, and notes on unimplemented features, providing useful context beyond annotations.

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?

Well-structured intro, replacements list, examples, and parameter docs. Each section serves a purpose. Slightly verbose due to examples, but they enhance clarity. Good front-loading of key info.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers all parameters, return types, supported document types, and includes examples. Notes limitations (scope not implemented for Word/PPT). With no output schema, description adequately explains what to expect. Complete for a read-only tool.

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

Parameters5/5

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

Input schema has 100% description coverage, but description's Args section adds deeper detail for each parameter (e.g., valid file extensions, scope format for Excel/Word/PPT, explanation of include_formulas). Significantly enriches meaning beyond 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 it reads content from Word, Excel, or PowerPoint documents, auto-detects format, and extracts in requested output format. It lists replacements (word_extract, etc.) distinguishing from sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Description explicitly names alternative tools that this replaces, guiding when to use this. Examples illustrate usage. However, no explicit when-not-to-use or exclusion criteria beyond replacements.

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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