- Fixed health check to use correct /healthz endpoint instead of /health
- Added MCP version (mcpVersion) and supported n8n version (supportedN8nVersion) to health check response
- Added versionNote field with instructions for AI agents about manual version verification
- n8n API limitation: instance version cannot be determined automatically
- Updated axios usage for healthz endpoint access with proper error handling
- Added workflowNodeType field to all node-returning MCP tools
- AI agents now receive both internal format (nodes-base.webhook) and workflow format (n8n-nodes-base.webhook)
- Created getWorkflowNodeType() utility to construct proper n8n format from package name
- Solves issue where AI agents would search nodes and use wrong format in workflows
- No database changes required - uses existing package_name field
- Updated: search_nodes, get_node_info, get_node_essentials, get_node_as_tool_info, validate_node_operation
- Updated CHANGELOG.md with comprehensive documentation of the changes
This completes the fix for issue #71, ensuring AI agents can seamlessly create workflows
with the correct node type format without manual intervention.
- Add centralized normalizeNodeType utility to handle prefix conversion
- n8n-nodes-base.* → nodes-base.*
- @n8n/n8n-nodes-langchain.* → nodes-langchain.*
- Update all 9 affected MCP tools to use normalized node types
- AI agents can now use node types directly from n8n workflow exports
- Maintains backward compatibility with existing shortened prefixes
- Add comprehensive test coverage for all affected methods
Fixes#71🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Remove examples from get_node_essentials responses
- Remove examples from validate_node_operation when errors occur
- Update documentation to reflect removal of examples
- Keep helpful format hints in get_node_for_task (different purpose)
The auto-generated examples were misleading AI agents with incorrect
configurations (e.g., Slack "channel" vs "select" property). Tools
now focus on validation and error messages instead of examples.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Redesigned documentation to be utilitarian and AI-agent focused
- Removed all pleasantries, emojis, and conversational language
- Added concrete numbers throughout (528 nodes, 108 triggers, 264 AI tools)
- Updated all tool descriptions with practical, actionable information
- Enhanced examples with actual return structures and usage patterns
- Made Code node guides prominently featured in overview
- Verified documentation accuracy through extensive testing
- Standardized format across all 30+ tool documentation files
Documentation now optimized for token efficiency while maintaining
clarity and completeness for AI agent consumption.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Migrated all 40 MCP tools documentation to modular structure
- Created comprehensive documentation with both essentials and full details
- Organized tools by category: discovery, configuration, validation, templates, workflow_management, system, special
- Fixed all TODO placeholders with informative, precise content
- Each tool now has concise description, key tips, and full documentation
- Improved documentation quality: 30-40% more concise while maintaining usefulness
- Fixed TypeScript compilation issues and removed orphaned content
- All tools accessible via tools_documentation MCP endpoint
- Build successful with zero errors
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Switch from package.json to package.runtime.json in runtime stage
- Reduces image size by 82% (from ~1.5GB to ~280MB)
- 10x faster builds (1-2 minutes vs 12 minutes)
- No functional changes - uses pre-built database from git
- Aligns Railway image with main Dockerfile optimization
This dramatically improves Railway deployment performance while
maintaining full functionality.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add Railway-specific Docker image build to CI/CD workflow
- Builds n8n-mcp-railway image alongside standard image
- Railway image optimized for AMD64 architecture
- Automatically published to ghcr.io on main branch pushes
- Create comprehensive Railway deployment documentation
- Step-by-step deployment guide with security best practices
- Claude Desktop connection instructions via mcp-remote
- Troubleshooting guide for common issues
- Architecture details and single-instance design explanation
- Update README with Railway documentation link
- Removed inline Railway content to keep README focused
- Added link to dedicated Railway deployment guide
This enables zero-configuration cloud deployment of n8n-mcp
with automatic HTTPS, global access, and built-in monitoring.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Rolled back README.md to remove Railway deployment instructions while keeping the codebase changes intact. This addresses any confusion about deployment methods without breaking existing Railway deployments.
- Add Railway as Option 4 in Quick Start section per review request
- Include brief explanation of Railway platform
- Add deploy button with proper placement
- Document setup instructions and environment variables
- Add AUTH_TOKEN configuration note
- Include database availability requirements note
- Configure Claude Desktop integration via mcp-remote
This commit implements the documentation requirements from PR #53 review,
completing the Railway deployment feature.
