Claude Code Integration

Complete guide to integrating the MCP Confluence ADF server with Claude Code for seamless documentation workflows.

Overview

Claude Code provides a powerful environment for working with Confluence content through natural language commands. The MCP Confluence ADF server exposes all functionality through conversational interfaces.

Installation & Setup

Step 1: Install MCP Server

# Install globally via npm/yarn
yarn global add mcp-confluence-adf

# Or use npx for testing
npx mcp-confluence-adf --help

Step 2: Configure Claude Code

claude mcp add --scope user mcp-confluence-adf npx mcp-confluence-adf

Option B: Manual Configuration

Edit ~/.config/claude/settings.json:

Step 3: Restart Claude Code

Close and reopen Claude Code to load the MCP server.

Step 4: Verify Installation

In Claude Code, ask:

You should see the full list of MCP tools loaded.

Natural Language Workflows

Authentication Workflows

First-Time Setup

Check Authentication Status

Reset Authentication

Content Management Workflows

Download and Edit Workflow

Create New Documentation

Bulk Content Operations

Template-Driven Workflows

Template Discovery

Template-Based Creation

Custom Template Development

Search and Discovery Workflows

Content Discovery

Space Exploration

Advanced Integration Patterns

Automated Documentation Pipelines

API Documentation Pipeline

Code Documentation Integration

Content Quality Workflows

Documentation Review Process

Style Guide Enforcement

Migration and Backup Workflows

Content Migration

Backup and Archival

Custom Workflow Development

Creating Reusable Workflows

Workflow Definition

Workflow Execution

Integration with External Systems

Git Integration

Slack Integration

Performance and Optimization

Efficient Content Operations

Batch Processing

  • Multiple downloads: Process pages in parallel

  • Bulk uploads: Update multiple pages efficiently

  • Rate limit handling: Automatic throttling and retry

  • Progress tracking: Real-time status updates

Caching Strategy

  • Template caching: Avoid re-parsing YAML templates

  • Content caching: Store frequently accessed content

  • Search result caching: Cache expensive CQL queries

  • Token caching: Minimize authentication requests

Error Handling and Recovery

Automatic Recovery

Validation and Verification

Monitoring and Maintenance

Health Monitoring

System Health Checks

Performance Monitoring

Maintenance Tasks

Regular Maintenance

Updates and Upgrades

Best Practices

Workflow Organization

  • Consistent naming: Use clear, descriptive names for templates and workflows

  • Logical grouping: Organize content by team, project, or function

  • Version control: Track changes to templates and configurations

  • Documentation: Document custom workflows and integrations

Security Considerations

  • Credential management: Secure storage of OAuth tokens

  • Access control: Appropriate Confluence permissions

  • Audit logging: Track all content modifications

  • Regular reviews: Periodic security and access audits

Performance Guidelines

  • Batch operations: Group multiple operations when possible

  • Rate limit awareness: Design workflows within API limits

  • Error resilience: Handle failures gracefully with retries

  • Progress visibility: Provide feedback for long-running operations

Troubleshooting

Common Integration Issues

MCP Server Not Loading

Authentication Failures

Template Generation Errors

Getting Help

Built-in Help

Community Support

  • GitHub Issues: Report bugs and feature requests

  • Documentation: Comprehensive guides and examples

  • Community Forum: Share workflows and get help

Next Steps

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