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Spécifai-MCP-Server
MCP Server pour fournir des exigences, des tâches, etc. du projet Specifai à tous les outils d'IA prenant en charge MCP tels que HAI Build, Cursor.
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Specifai MCP Server
A Model Context Protocol (MCP) server for Specifai project integration and automation with any MCP-compatible AI tool. This server is designed to work with tool agnostic, meaning it can be used with any tool that supports the MCP protocol. This server currently expose tools to read all documents generated by Specifai project.
[!WARNING] This server is currently in experimental. The functionality and available tools are subject to change and expansion as we continue to develop and improve the server.
Table of Contents
- Installation
- Configuration
- Preparing Your Project
- IDE Integration
- Available Tools
- Requirements
- Contributing
- Security
- License
Installation
# Latest version
npx --yes @presidio-dev/specifai-mcp-server@latest
# Specific version
npx --yes @presidio-dev/specifai-mcp-server@1.2.3
We recommend npx
to install the server, but you can use any node package manager of your preference such as yarn
, pnpm
, bun
, etc.
Configuration
with npx
with latest version:
{
"specifai": {
"command": "npx",
"args": ["--yes", "@presidio-dev/specifai-mcp-server@latest"],
"disabled": false,
"autoApprove": []
}
}
with npx
with specific version:
{
"specifai": {
"command": "npx",
"args": ["--yes", "@presidio-dev/specifai-mcp-server@1.2.3"],
"disabled": false,
"autoApprove": []
}
}
Preparing your project
This is completely optional, but it's recommended to use it to avoid having to specify the project directory path every time you access the server. For AI IDE / Extension (Hai Build), it's recommended to use a
.specifai-path
file to specify the project directory path.
Make sure your project root directory contains a .specifai-path
file. It's how the Specifai MCP server know where to find the specification documents generated by Specifai.
The file is a plain text file containing the absolute path to the project directory where the specification documents for an project are stored.
For example, if your project directory is located at /path/to/project
, the .specifai-path
file should contain the following line:
/path/to/project
Specifai MCP integration with popular IDE and extension
See the setup instructions for each
Available Tools
The server provides several tools for interacting with your specification documents:
Tool Name | Description |
---|---|
get-brds |
Get Business Requirement Documents |
get-prds |
Get Product Requirement Documents |
get-nfrs |
Get Non-Functional Requirements |
get-uirs |
Get User Interface Requirements |
get-bpds |
Get Business Process Documents |
get-user-stories |
Get User Stories for a specific PRD |
get-tasks |
Get Tasks for a specific User Story |
get-task |
Get details of a specific Task |
set-project-path |
Set or change the project directory path |
Requirements
Package manager installation:
- Node.js >= 16.0.0
- Bun >= 1.0.0 (if using Bun runtime)
Contributing
We welcome contributions to the Specifai MCP Server! Please see our Contributing Guide for more information on how to get started.
Development Setup
For detailed instructions on setting up your development environment, please refer to our Development Setup Guide.
Architecture
To understand the project architecture, please see our Architecture Guide.
Security
For information about our security policy and how to report security vulnerabilities, please see our Security Policy.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgements
- Model Context Protocol - The protocol specification this server implements
- Specifai - The project this server integrates with
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Reviews

user_6qnmIiaD
As a dedicated user of specifai-mcp-server by presidio-oss, I must say this tool is excellent for managing and deploying predictive models efficiently. The integration process is smooth, and the server handles tasks seamlessly, making it essential for any data-driven project. Highly recommended for professionals looking to streamline their machine learning workflows!