UML-MCP
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Alternatives to UML-MCP
No user-submitted related servers found.
Related Servers
- AlicenseAqualityCmaintenanceEnables AI assistants to generate and validate 30+ UML and other diagram types (Mermaid, D2, Graphviz, BPMN, C4, and more) through the Model Context Protocol, returning rendered SVG, PNG, PDF, JPEG, or base64 output via Kroki, PlantUML, and Mermaid backends.4Apache 2.0
- AlicenseAqualityAmaintenanceGenerates UML and other diagrams (30+ types) via the Model Context Protocol, supporting multiple output formats and deployment options.4110MIT
- AlicenseAqualityDmaintenanceMCP server for rendering diagrams (Mermaid, Graphviz, PlantUML, D2) via Kroki. Provides tools to render diagrams and list available engines.26 npmMIT
- AlicenseAqualityDmaintenanceGenerates GCP architecture diagrams, sequence diagrams, flow charts, and class diagrams using Python diagrams DSL via MCP.31Apache 2.0
- AlicenseNot gradedqualityBmaintenanceEnables AI assistants to render, validate and convert diagrams-as-code — Mermaid, PlantUML, C4, Graphviz, D2, Structurizr, BPMN and Excalidraw — into images with shareable links, using either the public or a self-hosted Kroki service.MIT
- AlicenseNot gradedqualityDmaintenanceProvides MCP tools to validate Mermaid diagram syntax, render diagrams to SVG, and get documentation links.73 npm16MIT
TDQS
Scored across 5 tools
Each tool has a mostly distinct role: listing types, single generate, batch generate, validate, and render. The main ambiguity is between generate_uml and render_diagram, but the description explicitly steers usage ('use this instead of generate_uml when the figure should appear in chat'), which resolves most misselection risk.
All names follow a snake_case verb_noun pattern (list_diagram_types, generate_uml, generate_uml_batch, validate_uml, render_diagram). Minor inconsistency: three tools use the '_uml' resource suffix while render_diagram uses '_diagram', but the convention remains readable and predictable.
Five tools is well-scoped for a diagram generation server, with each tool earning its place (discovery, single gen, batch gen, validation, image rendering). No obvious padding or redundancy.
The core lifecycle (discover types, generate, batch, validate, render to image/file) is covered with no dead ends. Minor gaps exist around managing generated artifacts (e.g., listing or cleaning up previously written output files), but agents can work around this.