BolideAI MCP

BolideAI MCP

A comprehensive ModelContextProtocol server that provides AI-powered tools for marketing automation, content generation, research, and project management, integrating with various AI services to streamline workflows for developers and marketers.

Category
访问服务器

README

BolideAI MCP

npm version License: MIT

BolideAI MCP is a comprehensive ModelContextProtocol (MCP) server that provides tools for marketing automation, content generation, research, and project management. It integrates with various AI services to streamline workflows for developers and marketers.

Features

  • 🚀 Project Scaffolding - Create marketing project directories and structures
  • 📱 Marketing Automation - Capture screenshots and videos using companion app
  • 🎯 Artifact Management - Organize marketing materials with structured directories
  • 🤖 AI-Powered Content Generation - Generate social media posts using Gemini AI
  • 🔍 Research Tools - Comprehensive research using Perplexity AI and OpenAI
  • 📊 Asset Management - Organize and manage marketing assets
  • 🛠️ Diagnostic Tools - System validation and troubleshooting

Getting Started

Quick Start (NPM Package)

For quick testing without local builds:

Installation

npm install -g @bolide-ai/mcp

Or use with npx (recommended):

npx @bolide-ai/mcp@latest

Configuration

Configure your MCP client with the following:

{
  "mcpServers": {
    "BolideAI": {
      "command": "npx",
      "args": ["-y", "@bolide-ai/mcp@latest"],
      "env": {
        "BOLIDEAIMCP_API_KEY": "your-api-key",
      }
    }
  }
}

Development Setup (Local Build)

For development or local builds, follow these detailed steps:

1. Install Prerequisites

Install Node.js 22+

# Download and install from https://nodejs.org/en/download

Clone the repositories

git clone https://github.com/Bolide-AI/mcp

2. Build the MCP Server

# In the directory of the cloned repository
npm install && npm run build

4. Configure MCP in Cursor

  1. In Cursor menu select Cursor → Settings → Cursor Settings
  2. In the opened window select Tools & Integrations
  3. Click New MCP Server
  4. Insert the MCP server configuration, replacing:
    • <PATH TO MCP DIRECTORY> with the path to the MCP directory
    • <BOLIDEAI_API_TOKEN> with your BolideAI key (can be generated at here)
{
  "mcpServers": {
    "BolideAI-dev": {
      "type": "stdio",
      "command": "node",
      "args": [
        "--inspect=9999",
        "<PATH TO MCP DIRECTORY>/build/index.js"
      ],
      "env": {
        "BOLIDEAI_MCP_DEBUG": "true",
        "BOLIDEAI_API_TOKEN": "<your-api-key-here>"
      }
    }
  }
}
  1. Make sure the workspace is open in Cursor
  2. Launch the application in the simulator

Start Using Tools

// Create a project
scaffold_marketing_project()

// Perform research
use_openai_deep_research({ 
  query: "AI trends in marketing automation 2024" 
})

// Analyze video content
analyze_videos({ 
  artifactName: "my-project-20240101",
  videoNames: ["demo.mov"],
  force: false
})

Environment Variables

  • BOLIDEAI_API_TOKEN - Required
  • BOLIDEAI_API_URL - Optional, defaults to https://bolide.ai/api

Tool Configuration

  • BOLIDEAIMCP_DEBUG=true - Enable diagnostic tools and detailed logging
  • Tool Groups - Enable specific tool categories (see Tool Options)
  • Individual Tools - Enable specific tools only (see Tool Options)

Available Tools

BolideAI MCP provides 15 tools across 7 categories:

🚀 Project Scaffolding

  • scaffold_marketing_project - Create marketing project directory structure

📱 Utility Tools

  • check_companion_app_status - Check companion app running status
  • launch_companion_app - Launch Companion App for marketing capture
  • stop_companion_app - Stop running companion app instances

🎯 Artifact Management

  • create_artifact_directory - Create organized artifact directories
  • create_post_artifact - Store post artifacts with metadata

📊 Asset Management

  • create_post_asset - Create marketing asset files
  • create_research_asset - Create research asset files

🤖 Content Generation

  • analyze_videos - Analyze video content using AI
  • generate_gif - Convert video segments to GIFs
  • enhance_audio - Extract and enhance audio from videos using ElevenLabs
  • fetch_reddit_posts - Fetch Reddit posts from specified subreddits

🔍 Research Tools

  • use_perplexity - Research using Perplexity AI
  • use_openai_deep_research - Deep research using OpenAI o4-mini-deep-research

🛠️ Diagnostic Tools

  • diagnostic - System environment validation (debug mode only)

Research Tools

Perplexity AI Research

Perform quick research and information gathering:

use_perplexity({ 
  query: "Latest trends in AI marketing automation",
  search_mode: "web" // or "academic"
})

OpenAI Deep Research

Conduct comprehensive research with query enrichment:

use_openai_deep_research({ 
  query: "Economic impact of renewable energy adoption" 
})

The deep research tool:

  1. Enriches your query using GPT-4.1 with detailed research instructions
  2. Researches using o4-mini-deep-research with web search and code interpreter
  3. Returns both enriched instructions and comprehensive findings

Common Workflows

Complete Marketing Content Creation

  1. Set up project structure:

    scaffold_marketing_project()
    
  2. Create artifact directory:

    create_artifact_directory({ artifactName: "feature-demo" })
    
  3. Check app status and capture content:

    check_companion_app_status()
    launch_companion_app({ 
      artifactsDirectory: "/path/to/artifacts/feature-demo-20240101" 
    })
    
  4. Enhance audio quality:

    enhance_audio({ 
      artifactName: "feature-demo-20240101",
      videoNames: ["demo.mov"]
    })
    
  5. Store results:

    create_post_artifact({ 
      artifactName: "feature-demo-20240101",
      fileName: "final-post.md",
      fileContent: "Generated content..."
    })
    
  6. Store research:

    create_research_asset({ 
      name: "market-research.md",
      content: "Detailed research findings..."
    })
    

Research Workflow

  1. Quick research:

    use_perplexity({ 
      query: "Your research question",
      search_mode: "web"
    })
    
  2. Deep research:

    use_openai_deep_research({ 
      query: "Complex research topic requiring comprehensive analysis"
    })
    
  3. Store research findings:

    create_research_asset({ 
      name: "market-analysis.md",
      content: "Research findings and analysis..."
    })
    

Reddit Data Collection

  1. Fetch Reddit posts:

    fetch_reddit_posts({ 
      subreddit: "programming",
      limit: 25,
      sort: "top",
      timePeriod: "day"
    })
    
  2. Store Reddit data:

    create_research_asset({ 
      name: "reddit-programming-trends.md",
      content: "Analysis of top programming posts..."
    })
    

Configuration Options

Selective Tool Registration

Enable only the tools you need to optimize performance:

{
  "env": {
    "BOLIDEAIMCP_GROUP_RESEARCH": "true",
    "BOLIDEAIMCP_GROUP_CONTENT_GENERATORS": "true",
    "GOOGLE_API_KEY": "your-api-key",
    "OPENAI_API_KEY": "your-api-key"
  }
}

Available Tool Groups

  • BOLIDEAIMCP_GROUP_LAUNCH - Launch and utility tools
  • BOLIDEAIMCP_GROUP_SCAFFOLDING - Project scaffolding tools
  • BOLIDEAIMCP_GROUP_ARTIFACTS - Artifact management tools
  • BOLIDEAIMCP_GROUP_ASSET_GENERATORS - Asset management tools
  • BOLIDEAIMCP_GROUP_CONTENT_GENERATORS - Content generation tools
  • BOLIDEAIMCP_GROUP_RESEARCH - Research and information gathering tools
  • BOLIDEAIMCP_GROUP_DIAGNOSTICS - Diagnostic tools

Documentation

System Requirements

Dependencies

  • Node.js 18+ (automatically handled by npx)
  • ffmpeg (required for GIF generation tools)
  • Companion App

Troubleshooting

Enable Debug Mode

{
  "env": {
    "BOLIDEAIMCP_DEBUG": "true"
  }
}

Run Diagnostics

diagnostic() // Available in debug mode

Common Issues

  1. Missing API Keys: Ensure all required environment variables are set
  2. ffmpeg Not Found: Install ffmpeg using brew install ffmpeg
  3. Permission Issues: Check file system permissions for artifact directories

Contributing

We welcome contributions! Please see our Contributing Guide for details.

License

MIT © Data Route LLC

Support


BolideAI MCP - Streamline your marketing automation and research workflows with AI-powered tools.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选