Implement a Model Context Protocol (MCP) server that enables AI tools like Claude Code to understand and work with flow workspaces. This would provide AI assistants with deep context about flow configurations, syntax, and workspace structure.
MCP Server Capabilities
Resource Access
- Workspace Discovery: List and access registered flow workspaces
- Executable Inspection: Read flow files and executable configurations
- Schema Reference: Provide flow's JSON schemas for syntax validation
- Log Access: Read execution history and error logs for debugging context
Tool Functions
- Syntax Validation: Check flow file syntax and configuration validity
- Executable Lookup: Search for executables by name, verb, or workspace
- Workspace Analysis: Analyze workspace structure and organization
- Best Practice Suggestions: Recommend improvements based on flow conventions
Example AI Interactions
User: "This flow executable is failing, can you help debug it?"
AI: [Accesses workspace via MCP] "I can see the executable configuration and recent logs. The issue is in the `params` section - you're missing the `envKey` field..."
User: "Help me create a deployment executable for this project"
AI: [Reads existing workspace patterns] "Based on your other executables, here's a deployment flow file that follows your workspace's conventions..."
User: "Is my flow file syntax correct?"
AI: [Validates against schema] "The syntax looks good, but you might want to add a `timeout` field since this executable runs external commands..."
Implement a Model Context Protocol (MCP) server that enables AI tools like Claude Code to understand and work with flow workspaces. This would provide AI assistants with deep context about flow configurations, syntax, and workspace structure.
MCP Server Capabilities
Resource Access
Tool Functions
Example AI Interactions