Model Context Protocol (MCP)

Build advanced applications using the Model Context Protocol for enhanced AI workflows

Last updated: May 2025

Universal AI Integration Standard

The Model Context Protocol (MCP) is an open standard that enables seamless integration between AI applications and external data sources, providing a unified way to connect AI models with the tools and information they need.

🔌 Resources

Data sources that AI can read from - databases, APIs, file systems

🛠️ Tools

Actions AI can perform - create files, run queries, send messages

💬 Prompts

Pre-built templates for common workflows and interactions

🚀 Why MCP Matters

  • • Standardization: One protocol for all AI integrations
  • • Security: User-controlled permissions and data access
  • • Composability: Mix and match servers for powerful workflows
  • • Open Source: Community-driven, vendor-neutral standard

Popular MCP Servers

Filesystem

Read, write, and navigate local file systems

Capabilities:

File operationsDirectory traversalSearch

Use Case: Local development, file management, documentation

Supabase

Query and manage PostgreSQL databases

Capabilities:

SQL queriesSchema inspectionData operations

Use Case: Database development, data analysis, backend integration

GitHub

Interact with GitHub repositories and issues

Capabilities:

Repository accessIssue managementPR operations

Use Case: Code review, project management, CI/CD workflows

Brave Search

Web search with privacy-focused results

Capabilities:

Web searchReal-time infoFact checking

Use Case: Research, fact verification, current events

Puppeteer

Browser automation and web scraping

Capabilities:

Page navigationScreenshotsForm interaction

Use Case: Testing, automation, data extraction

Context7

Access up-to-date library documentation

Capabilities:

API docsCode examplesFramework guides

Use Case: Development, learning, troubleshooting

MCP Composition Patterns

The real power of MCP emerges when you combine multiple servers together. Here are proven patterns that unlock exponential capabilities through composition.

🏗️ Full-Stack Development Stack

Filesystem + Supabase + GitHub + Context7

  • • Filesystem: Read/write project files, create components
  • • Supabase: Query database, update schemas, test queries
  • • GitHub: Create PRs, manage issues, review code
  • • Context7: Access latest Next.js/React documentation

Example workflow: Claude reads your database schema (Supabase), references latest Next.js docs (Context7), creates components (Filesystem), and opens a PR (GitHub) - all autonomously.

🔍 Research & Fact Verification Stack

Brave Search + Puppeteer + Filesystem

  • • Brave Search: Find sources and recent information
  • • Puppeteer: Navigate sites, extract data, take screenshots
  • • Filesystem: Save findings, create reports

Example workflow: Claude searches for sources (Brave), visits and extracts specific data (Puppeteer), then compiles a verified report with citations (Filesystem).

📊 Data Pipeline Stack

Puppeteer + Supabase + Filesystem

  • • Puppeteer: Extract data from websites
  • • Supabase: Store structured data in database
  • • Filesystem: Export reports and analytics

Example workflow: Claude scrapes competitor pricing (Puppeteer), stores in normalized tables (Supabase), and generates comparison reports (Filesystem).

🧪 Testing & QA Stack

Puppeteer + Filesystem + GitHub + Supabase

  • • Puppeteer: Run E2E tests, capture screenshots
  • • Supabase: Verify database state after operations
  • • Filesystem: Generate test reports, update snapshots
  • • GitHub: Create issues for failures, comment on PRs

Example workflow: Claude runs UI tests (Puppeteer), verifies data integrity (Supabase), generates coverage reports (Filesystem), and files issues for regressions (GitHub).

💡 Composition Pro Tip

Start with one MCP server, master its capabilities, then gradually add complementary servers. The most powerful workflows emerge from 3-4 well-chosen servers working together, not from trying to connect everything at once.

Getting Started with MCP

Installation

1. Install Claude Desktop or Claude Code CLI

npm install -g claude-cli

2. Add an MCP server (example: Supabase)

claude mcp add supabase -s project \ -e SUPABASE_ACCESS_TOKEN=your_token \ -- npx -y @supabase/mcp-server-supabase \ --project-ref=your_project_ref --read-only

3. Verify installation

claude mcp list

⚠️ Security Best Practices

  • • Start with --read-only flags when possible
  • • Use project-level scoping (-s project) for database servers
  • • Never commit access tokens to version control
  • • Regularly audit MCP server permissions with claude mcp list

Resources & Community

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