mcp-integration▌
anthropics/claude-plugins-official · updated Apr 8, 2026
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Integrate external services into Claude Code plugins via Model Context Protocol servers with four connection types.
- ›Supports four server types: stdio for local processes, SSE for OAuth-enabled hosted services, HTTP for REST APIs with token auth, and WebSocket for real-time bidirectional communication
- ›Configure MCP servers in dedicated .mcp.json file or inline in plugin.json with environment variable expansion and automatic tool discovery
- ›Tools are automatically namespaced and prefixe
MCP Integration for Claude Code Plugins
Overview
Model Context Protocol (MCP) enables Claude Code plugins to integrate with external services and APIs by providing structured tool access. Use MCP integration to expose external service capabilities as tools within Claude Code.
Key capabilities:
- Connect to external services (databases, APIs, file systems)
- Provide 10+ related tools from a single service
- Handle OAuth and complex authentication flows
- Bundle MCP servers with plugins for automatic setup
MCP Server Configuration Methods
Plugins can bundle MCP servers in two ways:
Method 1: Dedicated .mcp.json (Recommended)
Create .mcp.json at plugin root:
{
"database-tools": {
"command": "${CLAUDE_PLUGIN_ROOT}/servers/db-server",
"args": ["--config", "${CLAUDE_PLUGIN_ROOT}/config.json"],
"env": {
"DB_URL": "${DB_URL}"
}
}
}
Benefits:
- Clear separation of concerns
- Easier to maintain
- Better for multiple servers
Method 2: Inline in plugin.json
Add mcpServers field to plugin.json:
{
"name": "my-plugin",
"version": "1.0.0",
"mcpServers": {
"plugin-api": {
"command": "${CLAUDE_PLUGIN_ROOT}/servers/api-server",
"args": ["--port", "8080"]
}
}
}
Benefits:
- Single configuration file
- Good for simple single-server plugins
MCP Server Types
stdio (Local Process)
Execute local MCP servers as child processes. Best for local tools and custom servers.
Configuration:
{
"filesystem": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/allowed/path"],
"env": {
"LOG_LEVEL": "debug"
}
}
}
Use cases:
- File system access
- Local database connections
- Custom MCP servers
- NPM-packaged MCP servers
Process management:
- Claude Code spawns and manages the process
- Communicates via stdin/stdout
- Terminates when Claude Code exits
SSE (Server-Sent Events)
Connect to hosted MCP servers with OAuth support. Best for cloud services.
Configuration:
{
"asana": {
"type": "sse",
"url": "https://mcp.asana.com/sse"
}
}
Use cases:
- Official hosted MCP servers (Asana, GitHub, etc.)
- Cloud services with MCP endpoints
- OAuth-based authentication
- No local installation needed
Authentication:
- OAuth flows handled automatically
- User prompted on first use
- Tokens managed by Claude Code
HTTP (REST API)
Connect to RESTful MCP servers with token authentication.
Configuration:
{
"api-service": {
"type": "http",
"url": "https://api.example.com/mcp",
"headers": {
"Authorization": "Bearer ${API_TOKEN}",
"X-Custom-Header": "value"
}
}
}
Use cases:
- REST API-based MCP servers
- Token-based authentication
- Custom API backends
- Stateless interactions
WebSocket (Real-time)
Connect to WebSocket MCP servers for real-time bidirectional communication.
Configuration:
{
"realtime-service": {
"type": "ws",
"url": "wss://mcp.example.com/ws",
"headers": {
"Authorization": "Bearer ${TOKEN}"
}
}
}
Use cases:
- Real-time data streaming
- Persistent connections
- Push notifications from server
- Low-latency requirements
Environment Variable Expansion
All MCP configurations support environment variable substitution:
${CLAUDE_PLUGIN_ROOT} - Plugin directory (always use for portability):
{
"command": "${CLAUDE_PLUGIN_ROOT}/servers/my-server"
}
User environment variables - From user's shell:
{
"env": {
"API_KEY": "${MY_API_KEY}",
"DATABASE_URL": "${DB_URL}"
}
}
Best practice: Document all required environment variables in plugin README.
MCP Tool Naming
When MCP servers provide tools, they're automatically prefixed:
Format: mcp__plugin_<plugin-name>_<server-name>__<tool-name>
Example:
- Plugin:
asana - Server:
asana - Tool:
create_task - Full name:
mcp__plugin_asana_asana__asana_create_task
Using MCP Tools in Commands
Pre-allow specific MCP tools in command frontmatter:
---
allowed-tools: [
"mcp__plugin_asana_asana__asana_create_task",
"mcp__plugin_asana_asana__asana_search_tasks"
]
---
Wildcard (use sparingly):
---
allowed-tools: ["mcp__plugin_asana_asana__*"]
---
Best practice: Pre-allow specific tools, not wildcards, for security.
Lifecycle Management
Automatic startup:
- MCP servers start when plugin enables
- Connection established before first tool use
- Restart required for configuration changes
Lifecycle:
- Plugin loads
- MCP configuration parsed
- Server process started (stdio) or connection established (SSE/HTTP/WS)
- Tools discovered and registered
- Tools available as
mcp__plugin_...__...
Viewing servers:
Use /mcp command to see all servers including plugin-provided ones.
Authentication Patterns
OAuth (SSE/HTTP)
OAuth handled automatically by Claude Code:
{
"type": "sse",
"url": "https://mcp.example.com/sse"
}
User authenticates in browser on first use. No additional configuration needed.
Token-Based (Headers)
Static or environment variable tokens:
{
"type": "http",
"url": "https://api.example.com",
"headers": {
"Authorization": "Bearer ${API_TOKEN}"
}
}
Document required environment variables in README.
Environment Variables (stdio)
Pass configuration to MCP server:
{
"command": "python",
"args": ["-m", "my_mcp_server"],
"env": {
"DATABASE_URL": "${DB_URL}",
"API_KEY": "${API_KEY}",
"LOG_LEVEL": "info"
}
}
Integration Patterns
Pattern 1: Simple Tool Wrapper
Commands use MCP tools with user interaction:
# Command: create-item.md
---
allowed-tools: ["mcp__plugin_name_server__create_item"]
---
Steps:
1. Gather item details from user
2. Use mcp__plugin_name_server__create_item
3. Confirm creation
Use for: Adding validation or preprocessing before MCP calls.
Pattern 2: Autonomous Agent
Agents use MCP tools autonomously:
# Agent: data-analyzer.md
Analysis Process:
1. Query data via mcp__plugin_db_server__query
2. Process and analyze results
3. Generate insights report
Use for: Multi-step MCP workflows without user interaction.
Pattern 3: Multi-Server Plugin
Integrate multiple MCP servers:
{
"github": {
"type": "sse",
"url": "https://mcp.github.com/sse"
},
"jira": {
"type": "sse",
"url": "https://mcp.jira.com/sse"
}
}
Use for: Workflows spanning multiple services.
Security Best Practices
Use HTTPS/WSS
Always use secure connections:
✅ "url": "https://mcp.example.com/sse"
❌ "url": "http://mcp.example.com/sse"
Token Management
DO:
- ✅ Use environment variables for tokens
How to use mcp-integration on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add mcp-integration
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches mcp-integration from GitHub repository anthropics/claude-plugins-official and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate mcp-integration. Access the skill through slash commands (e.g., /mcp-integration) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★25 reviews- ★★★★★Maya Thompson· Dec 24, 2024
mcp-integration has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Hassan Martinez· Dec 16, 2024
mcp-integration fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Maya Shah· Nov 15, 2024
Solid pick for teams standardizing on skills: mcp-integration is focused, and the summary matches what you get after install.
- ★★★★★Jin Brown· Nov 7, 2024
I recommend mcp-integration for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Min Nasser· Oct 26, 2024
Solid pick for teams standardizing on skills: mcp-integration is focused, and the summary matches what you get after install.
- ★★★★★Kiara Okafor· Oct 6, 2024
I recommend mcp-integration for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Piyush G· Sep 5, 2024
mcp-integration reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Shikha Mishra· Aug 24, 2024
mcp-integration is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Yash Thakker· Jul 15, 2024
Useful defaults in mcp-integration — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Sakshi Patil· Jul 7, 2024
Keeps context tight: mcp-integration is the kind of skill you can hand to a new teammate without a long onboarding doc.
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