MCP Integrations
Entelligence's Model Context Protocol (MCP) is our specialized implementation of the Model Context Protocol, designed specifically for development teams and engineering workflows. This integration leverages the MCP standard to connect your AI assistant directly with your development ecosystem, enabling seamless interaction with productivity apps, intelligent code analysis, and automated documentation generation while maintaining full context about your codebase and design decisions.
Model Context Protocol (MCP) is an open standard developed by Anthropic that enables AI models to securely connect with external data sources, tools, and applications through a standardized interface. Rather than requiring custom integrations for each service, MCP provides a universal protocol that allows AI assistants to access databases, APIs, file systems, and other applications while maintaining proper security controls and permissions. This creates a seamless bridge between AI capabilities and existing software ecosystems, enabling more powerful and contextually aware assistance across different platforms and tools.
What is Entelligence MCP?
Unlike generic MCP implementations, Entelligence MCP is purpose-built for software development workflows. It allows you to manage your entire development process through intelligent conversations with you AI Assistant that understand your code context, project requirements, and team dynamics, eliminating the need to constantly switch between multiple applications and tools.
Key Capabilities:
- Intelligent Code Reviews: Automated analysis of git diffs with context-aware feedback and actionable suggestions
- Sequential Design Thinking: Structured approach to system design with conversation tracking and iterative refinement
- Multi-Platform Publishing: Create and distribute documentation across Google Docs, Notion, Confluence, and Slack
- Project Management Integration: Seamlessly create and manage Linear issues with full context from your design discussions
- Smart Documentation Generation: Transform design conversations into comprehensive technical documentation
Core Features
AI-Powered Code Review
Transform your code review process with intelligent analysis that goes beyond syntax checking. Entelligence examines your git diffs in the context of your entire codebase, providing detailed feedback on potential bugs, architectural concerns, and optimization opportunities.
How it works:
- Automatically analyzes git diffs and changed files
- Provides line-by-line reviews with specific suggestions
- Identifies potential bugs and security vulnerabilities
- Offers refactoring recommendations based on best practices
- Maintains awareness of your project's coding standards and patterns
Sequential Design Thinking
Navigate complex system design challenges with a structured thinking framework that captures every decision, consideration, and trade-off. This feature helps engineering teams document their thought process while making architectural decisions.
Benefits:
- Structured Decision Making: Follow a systematic approach to design challenges
- Context Preservation: Maintain complete history of design rationale and alternatives considered
- Collaborative Design: Enable team members to contribute to and understand design evolution
- Documentation Generation: Automatically convert design discussions into comprehensive technical documents
- Revision Tracking: Keep track of design changes and the reasoning behind them
Seamless App Integrations
Connect your design and development workflow across all your essential tools without leaving the conversation context.
Supported Integrations:
- Google Docs: Create formatted technical documentation with automatic styling
- Notion: Build structured knowledge bases with hierarchical page organization
- Confluence: Publish to team wikis with proper formatting and linking
- Slack: Share updates and summaries with your team channels
- Linear: Create detailed issues with full context from design discussions
How Teams Use MCP Tools
Team Role | Primary Use Cases | Key Benefits |
---|---|---|
Software Engineers | Code review automation, design documentation, technical analysis | Faster code reviews, better documentation habits, architectural guidance |
Tech Leads | System design facilitation, team communication, knowledge sharing | Structured design processes, improved team alignment, decision tracking |
Engineering Managers | Progress tracking, team coordination, documentation oversight | Better visibility into design decisions, streamlined workflows, knowledge retention |
Product Managers | Technical understanding, requirement clarification, feature documentation | Clear technical explanations, better product-engineering alignment, accessible documentation |
Getting Started with MCP Tools
Prerequisites
Before using MCP tools, ensure you have:
- Active Entelligence.ai account
- Connected integrations for the apps you want to use (Google Docs, Notion, etc.)
- Access to your git repository for code review features
- IDE with MCP support enabled
Configure MCP in Cursor/VSCode
Follow these steps to add Entelligence MCP to your IDE configuration:
- Get MCP Configuration: Navigate to your Entelligence Dashboard → Profile → MCP to access your personalized configuration URL
Note: Do not share your personalized MCP configuration URL with others. This URL grants access to your Entelligence integration and should be kept private.
- Open IDE Settings: Open Cursor settings by pressing
Cmd + Shift + J (⌘⇧J)
on Mac orCtrl + Shift + J
on Windows - Navigate to MCP: Go to
MCP
in the settings menu - Add Server: Click
Add New Global Server
- Configure Integration: Add the provided URL to the
mcpServers
object (separate with comma,
if needed)
Setting Up Integrations
For a complete list of supported features and setup instructions, check out the Integrations page.
Advanced Features
Smart Documentation Generation
Convert technical discussions into professional documentation automatically. The system understands the flow of your design conversation and structures it into comprehensive technical documents with:
- Executive Summaries: High-level overviews for stakeholders
- Technical Architecture: Detailed implementation approaches with code examples
- Design Rationale: Explanation of decisions and trade-offs considered
- Implementation Roadmap: Structured breakdown of development tasks
Context-Aware Code Analysis
Go beyond traditional static analysis with reviews that understand:
- Project Architecture: How changes fit into your overall system design
- Historical Context: Previous decisions and patterns in your codebase
- Team Practices: Adherence to your established coding standards
- Performance Implications: Impact on system performance and scalability
Collaborative Design Sessions
Facilitate better team design discussions with:
- Structured Conversations: Guided frameworks for exploring design options
- Decision Documentation: Automatic capture of rationale and alternatives
- Visual Summaries: Clear presentation of design progression and key decisions
- Action Item Generation: Automatic creation of follow-up tasks and assignments
Best Practices
- Include Complete Context: Provide full file contents along with git diffs for comprehensive analysis
- Start with Clear Objectives: Define what you're trying to accomplish before beginning the design process
- Ask Specific Questions: Guide reviews toward particular concerns or areas of focus
- Follow Up on Suggestions: Engage with recommendations to understand reasoning and alternatives
- Maintain Conversation Flow: Let natural discussion evolve rather than forcing rigid structures
- Review Before Publishing: Use preview features to ensure documentation meets your standards
Summary
Entelligence's MCP integration revolutionizes how development teams work by bringing AI-powered intelligence directly into your existing workflow through the Model Context Protocol standard, creating a seamless bridge between your AI assistant and everyday tools like your IDE, git repositories, Notion, Slack, and Linear. This creates a fundamentally different development experience where intelligent code reviews happen automatically, design decisions are captured and documented in real-time, and your team's collective intelligence is augmented to make better architectural decisions faster - all without the context switching and manual overhead that typically fragments development workflows and causes valuable knowledge to get lost.