Office PowerPoint

The Office-PowerPoint-MCP-Server enables your AI assistant (like Claude and Cursor) to programmatically build and edit PowerPoint presentations.

It’s built on the popular python-pptx library but adds a much simpler, tool-based interface suitable for the Model Context Protocol.

Features

  • 🎨 Professional Design: Apply one of four built-in color schemes and professional typography (Segoe UI) for polished, consistent presentations.
  • 🔄 Full Round-Trip Support: Open any .pptx file, modify its content, and save it without losing existing elements or formatting.
  • 🧩 Template Management: Create presentations from your own .pptx or .potx templates, automatically inheriting themes and custom layouts.
  • 🛠️ Unified v2.0 Tools: A consolidated set of 20 tools manages text, images, shapes, charts, and design elements through a clean, operation-based interface.
  • 🖼️ Advanced Image Control: Add images from files or base64 and apply enhancements like brightness, contrast, shadows, and reflections.
  • 📊 Data Visualization: Generate column, bar, line, and pie charts with full control over data series, legends, and labels.
  • ⚙️ Modular Architecture: A well-organized codebase with separate modules for presentation, content, and design makes the server maintainable and extensible.

Use Cases

  • Automated Business Reporting: You can connect the server to a data source, like a sales database. An AI agent can then pull the latest quarterly figures, generate charts, and populate a pre-branded company template with key takeaways. This turns a multi-hour manual task into a fully automated process.
  • AI-Powered Content Creation: A user could ask an AI assistant: “Create a 5-slide presentation about the impact of AI on software development.” The AI uses this server to build the presentation on the fly, selecting appropriate layouts, writing text, and even finding and inserting relevant images.
  • Batch Presentation Generation: A marketing team needs to create personalized sales decks for 50 different clients. A script can loop through a list of client data, using the MCP server to inject each client’s name, logo, and specific data points into a master template, producing 50 custom .pptx files in minutes.
  • Technical Documentation Slides: A developer can write documentation in Markdown and have a script that uses the server to convert it into a well-formatted slide deck for a team presentation, correctly placing code blocks, bullet points, and diagrams.

Installation

Option 1: Automatic Setup (Recommended)

python setup_mcp.py

The setup script handles prerequisites, offers installation methods, and generates MCP configuration files.

Option 2: Via Smithery

npx -y @smithery/cli install @GongRzhe/Office-PowerPoint-MCP-Server --client claude

Option 3: Manual Installation

git clone https://github.com/GongRzhe/Office-PowerPoint-MCP-Server.git
cd Office-PowerPoint-MCP-Server
pip install -r requirements.txt

MCP Configuration

Local Installation:

{
  "mcpServers": {
    "ppt": {
      "command": "python",
      "args": ["/path/to/ppt_mcp_server.py"],
      "env": {}
    }
  }
}

Using UVX (No Local Installation):

{
  "mcpServers": {
    "ppt": {
      "command": "uvx",
      "args": ["--from", "office-powerpoint-mcp-server", "ppt_mcp_server"],
      "env": {}
    }
  }
}

Available Tools

Presentation Management (7 tools)

  • create_presentation – Create new presentations
  • create_presentation_from_template – Create from templates with theme preservation
  • open_presentation – Open existing presentations
  • save_presentation – Save presentations to files
  • get_presentation_info – Get comprehensive presentation information
  • get_template_info – Analyze template files and layouts
  • set_core_properties – Set document properties

Content Management (6 tools)

  • add_slide – Add slides with optional background styling
  • get_slide_info – Get detailed slide information
  • populate_placeholder – Populate placeholders with text
  • add_bullet_points – Add formatted bullet points
  • manage_text – Unified text tool (add/format/validate)
  • manage_image – Unified image tool (add/enhance)

Structural Elements (4 tools)

  • add_table – Create tables with enhanced formatting
  • format_table_cell – Format individual table cells
  • add_shape – Add shapes with text and formatting options
  • add_chart – Create charts with comprehensive customization

Professional Design (3 tools)

  • apply_professional_design – Unified design tool (themes/slides/enhancement)
  • apply_picture_effects – Unified effects tool (9 effects combined)
  • manage_fonts – Unified font tool (analyze/optimize/recommend)

Parameters

Professional Color Schemes:

  • modern_blue – Microsoft-inspired blue theme
  • corporate_gray – Professional grayscale with blue accents
  • elegant_green – Forest green with cream accents
  • warm_red – Deep red with orange accents

Chart Types:

  • column, bar, line, pie with full customization options

Image Enhancement Styles:

  • presentation, photo, logo with automatic optimization

Picture Effects:

  • Shadow, reflection, glow, bevel, rotation, and more

Basic Usage Example

# Create professional presentation
result = use_mcp_tool("ppt", "apply_professional_design", {
    "operation": "slide",
    "slide_type": "title_content", 
    "color_scheme": "modern_blue",
    "title": "Quarterly Review",
    "content": ["Revenue up 15%", "Customer satisfaction 94%"]
})
# Add chart
result = use_mcp_tool("ppt", "add_chart", {
    "slide_index": 0,
    "chart_type": "column",
    "categories": ["Q1", "Q2", "Q3", "Q4"],
    "series_values": [[100, 120, 140, 160]]
})
# Save presentation
result = use_mcp_tool("ppt", "save_presentation", {
    "file_path": "quarterly_review.pptx"
})

FAQs

Q: Can I use existing PowerPoint templates with this server?
A: Yes. Use get_template_info to analyze templates, then create_presentation_from_template to create presentations that preserve all theme elements, layouts, and branding.

Q: What image formats and enhancements are supported?
A: The server supports standard image formats (PNG, JPG, GIF) plus base64 input. Enhancement includes brightness, contrast, saturation adjustments, plus 9 professional effects like shadows, glows, and reflections through Pillow integration.

Q: Can I validate text fitting and auto-fix layout issues?
A: Yes, the manage_text tool includes validation operations that check text fit and can automatically adjust font sizes, apply auto-fit, or provide optimization suggestions to prevent text overflow.

Q: How do I work with charts and data visualization?
A: Use add_chart with support for column, bar, line, and pie charts. You can specify categories, multiple data series, legends, data labels, and comprehensive formatting options for professional data visualization.

Q: Is there support for corporate branding and consistent styling?
A: Absolutely. The server includes 4 professional color schemes, font management tools, and template support. Use apply_professional_design to maintain consistent branding across presentations or apply themes to entire presentations.

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FAQs

Q: What exactly is the Model Context Protocol (MCP)?

A: MCP is an open standard, like a common language, that lets AI applications (clients) and external data sources or tools (servers) talk to each other. It helps AI models get the context (data, instructions, tools) they need from outside systems to give more accurate and relevant responses. Think of it as a universal adapter for AI connections.

Q: How is MCP different from OpenAI's function calling or plugins?

A: While OpenAI's tools allow models to use specific external functions, MCP is a broader, open standard. It covers not just tool use, but also providing structured data (Resources) and instruction templates (Prompts) as context. Being an open standard means it's not tied to one company's models or platform. OpenAI has even started adopting MCP in its Agents SDK.

Q: Can I use MCP with frameworks like LangChain?

A: Yes, MCP is designed to complement frameworks like LangChain or LlamaIndex. Instead of relying solely on custom connectors within these frameworks, you can use MCP as a standardized bridge to connect to various tools and data sources. There's potential for interoperability, like converting MCP tools into LangChain tools.

Q: Why was MCP created? What problem does it solve?

A: It was created because large language models often lack real-time information and connecting them to external data/tools required custom, complex integrations for each pair. MCP solves this by providing a standard way to connect, reducing development time, complexity, and cost, and enabling better interoperability between different AI models and tools.

Q: Is MCP secure? What are the main risks?

A: Security is a major consideration. While MCP includes principles like user consent and control, risks exist. These include potential server compromises leading to token theft, indirect prompt injection attacks, excessive permissions, context data leakage, session hijacking, and vulnerabilities in server implementations. Implementing robust security measures like OAuth 2.1, TLS, strict permissions, and monitoring is crucial.

Q: Who is behind MCP?

A: MCP was initially developed and open-sourced by Anthropic. However, it's an open standard with active contributions from the community, including companies like Microsoft and VMware Tanzu who maintain official SDKs.

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