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P6xer MCP Server
The P6XER MCP Server is a Model Context Protocol service built on PyP6XER, providing a machine-readable manifest for XER file parsing, analysis, and conversion.
rating : 2 points
downloads : 8.7K
What is the P6XER MCP Server?
The P6XER MCP Server is a Model Context Protocol server built on the PyP6Xer library, specifically designed to handle the XER file format of Primavera P6. It provides a standardized interface for AI models to access the parsing, analysis, and conversion functions of XER files.How to use the P6XER MCP Server?
Simply install the dependencies and start the server, then you can access various XER processing functions through the RESTful API. The server will automatically generate a machine-readable MCP manifest for AI models to understand and use.Use cases
Suitable for scenarios that require automated processing of Primavera P6 project data, such as project data analysis, report generation, data conversion, etc. It is particularly suitable for integration with AI models to implement an intelligent project management system.Main features
XER file parsing
Parse Primavera P6 XER files into structured data
XER data analysis
Analyze and statistics the parsed project data
XER format conversion
Convert XER data to other formats or from other formats
Automatic generation of MCP manifest
Automatically generate a machine-readable Model Context Protocol manifest
Advantages
Built on the mature PyP6Xer library, with stable and reliable functions
Provide a standardized MCP interface for easy integration with AI models
Lightweight RESTful API design, easy to use and expand
Automatically generate machine-readable API documentation
Limitations
Currently only supports the XER file format
Performance may be limited when processing large files
Requires a Python environment to run
How to use
Install dependencies
Ensure that Python 3.7+ is installed, then install the required dependency packages
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Start the server
Start the MCP server using Uvicorn
Access the API
Access the API endpoints in a browser or through an HTTP client
Usage examples
Integrate AI models
AI models automatically discover and call XER processing functions through the MCP manifest
Project data analysis
Batch analyze project data in multiple XER files through the API
Frequently Asked Questions
What version of Python is required?
How to expand new models?
What authentication methods are supported?
Related resources
PyP6Xer project homepage
The core library on which the P6XER MCP Server is based
MCP protocol documentation
Standard documentation for the Model Context Protocol
Uvicorn documentation
Documentation for the ASGI server
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