This Excel to PDF MCP server converts spreadsheet files to PDF format and integrates seamlessly with AI assistants like Claude via the Model Context Protocol (MCP).
Convert Excel files: Supports .xls and .xlsx formats using the convert_excel_to_pdf tool
Convert Apple Numbers files: Supports .numbers format using the convert_numbers_to_pdf tool
AI assistant integration: Works with Claude Desktop and other AI tools through MCP
Secure file handling: Processes files securely within project boundaries
Easy installation: Available via npm or Smithery CLI
Requirements: Node.js 16+ and LibreOffice for conversion processing
Supports conversion of Apple Numbers files (.numbers) to PDF format
Uses LibreOffice as the conversion engine to transform spreadsheet files into PDFs
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Excel to PDF Converter convert my budget.xlsx to PDF for sharing"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP (Model Context Protocol) server that can convert Excel (.xls/.xlsx) and Apple Numbers (.numbers) files to PDF format. This tool integrates with AI assistants like Claude to enable file conversion directly through the conversation.
Convert Excel files (.xls, .xlsx) to PDF
Convert Apple Numbers files (.numbers) to PDF
Integrates with AI assistants via the Model Context Protocol
Secure file handling that respects project boundaries
Easy installation via npm
Related MCP server: PDF2MD MCP Server
Node.js 16 or higher
LibreOffice (for the conversion process)
To install Excel to PDF Converter for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @kmexnx/excel-to-pdf-mcp --client claudeLibreOffice is required for the conversion process. Install it according to your operating system:
brew install libreofficeapt-get install libreofficeDownload and install from LibreOffice official website.
npm install -g excel-to-pdf-mcpTo use this MCP server with Claude desktop:
Configure your MCP settings in Claude desktop by adding this server to your mcp_settings.json:
{
"mcpServers": {
"excel-to-pdf-mcp": {
"command": "npx",
"args": ["excel-to-pdf-mcp"],
"name": "Excel to PDF Converter"
}
}
}Make sure your Excel or Numbers files are within your project directory.
Once configured, Claude will be able to convert your spreadsheet files to PDF using this tool.
Here's an example of how a conversation with Claude might look when using this MCP server:
User: "I need to convert my quarterly_report.xlsx to PDF so I can share it with stakeholders."
Claude: "I can help you convert your Excel file to PDF. Let me use the Excel to PDF converter tool."
Claude would then use the tool behind the scenes:
Tool: convert_excel_to_pdf
Arguments: {
"input_path": "quarterly_report.xlsx",
"output_format": "pdf"
}Claude: "I've converted your Excel file to PDF. You can find it at: quarterly_report-1628347658-a7b2c9.pdf in your project directory."
This MCP server provides the following tools:
Converts Excel files (.xls/.xlsx) to PDF format.
Arguments:
input_path: Relative path to the Excel file (required)
output_format: Output format, currently only PDF is supported (default: "pdf")
Converts Apple Numbers files (.numbers) to PDF format.
Arguments:
input_path: Relative path to the Numbers file (required)
output_format: Output format, currently only PDF is supported (default: "pdf")
If you want to run from source or contribute:
Clone the repository
Install dependencies: npm install
Build the project: npm run build
Run the server: npm start
MIT
Converts Excel files (.xls, .xlsx) to PDF format
| Name | Required | Description | Default |
|---|---|---|---|
| input_path | Yes | Relative path to the Excel file (.xls, .xlsx) to convert | |
| output_format | No | Output format (currently only PDF is supported) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the conversion action but lacks details on permissions, side effects, rate limits, or output handling. For a file conversion tool with zero annotation coverage, this is a significant gap in transparency, as it doesn't describe what happens during or after conversion.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration, 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.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of file conversion (which may involve permissions, errors, or format constraints), no annotations, and no output schema, the description is incomplete. It doesn't address potential behavioral aspects or output details, leaving gaps that could hinder correct tool invocation by an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema fully documents both parameters (input_path and output_format). The description adds no additional parameter semantics beyond what's in the schema, such as format details or usage examples. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: converting Excel files to PDF format, specifying the supported input file types (.xls, .xlsx). It uses a specific verb ('Converts') and identifies the resource (Excel files). However, it doesn't explicitly differentiate from the sibling tool 'convert_numbers_to_pdf' beyond the file format mention, which might imply differentiation but isn't explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. It doesn't mention the sibling tool 'convert_numbers_to_pdf' or any other conversion tools, nor does it specify prerequisites, contexts, or exclusions for usage. This leaves the agent without clear direction on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Converts Apple Numbers files (.numbers) to PDF format
| Name | Required | Description | Default |
|---|---|---|---|
| input_path | Yes | Relative path to the Numbers file (.numbers) to convert | |
| output_format | No | Output format (currently only PDF is supported) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Converts' implies a transformation operation, it doesn't specify whether this is a read-only conversion or modifies files, what permissions are needed, how errors are handled, or what the output looks like (e.g., file location, format details). The description lacks critical behavioral context for a file conversion tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with zero wasted words. It's front-loaded with the core purpose and efficiently specifies the input format (.numbers) and output format (PDF). Every part of the sentence earns its place by conveying essential information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (file conversion with 2 parameters) and lack of annotations or output schema, the description is minimally adequate. It states what the tool does but omits behavioral details, usage guidelines, and output information. It's complete enough to understand the basic function but leaves significant gaps for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 both parameters (input_path and output_format). The description adds no additional parameter semantics beyond what's in the schema—it doesn't explain path requirements, format constraints, or usage examples. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Converts') and resource ('Apple Numbers files (.numbers) to PDF format'), making it immediately understandable. However, it doesn't explicitly differentiate from its sibling tool 'convert_excel_to_pdf', which handles a different file format but performs a similar conversion function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. It doesn't mention the sibling tool 'convert_excel_to_pdf' or clarify that this tool is specifically for Numbers files, not other spreadsheet formats. There's no context about prerequisites, error conditions, or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
convert_excel_to_pdfconvert_numbers_to_pdfThe two tools have clearly distinct purposes: one handles Excel files (.xls, .xlsx) and the other handles Apple Numbers files (.numbers). There is no overlap or ambiguity between them, as they target different file formats within the same conversion domain.
Both tools follow a consistent verb_noun pattern with 'convert_*_to_pdf', making them predictable and easy to understand. The naming is uniform across the set, with no deviations in style or convention.
With only 2 tools, the server feels thin for a file conversion domain that might include other formats or operations. While it covers two specific formats well, the scope could be expanded to feel more complete, placing it in the borderline range.
The server is severely incomplete for a file conversion tool, as it only supports two specific formats (Excel and Numbers) without covering other common ones like Word, PowerPoint, or images. This creates significant gaps that will limit agent functionality in broader conversion tasks.
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