Model Context Protocol server that gives AI assistants the power to build, edit, deploy, and manage full-stack web applications through VULK.
VULK MCP server has 9 tools with complete input schemas and descriptions. Naming follows verb_noun convention (generate, edit, list, get, files, deploy, models, usage, subscribe). Descriptions are detailed (100-300 chars) and explain WHAT the tool does and WHEN to use it. However, output schemas are NOT documented, responses are inferred from code comments rather than explicit schema declarations. Parameter descriptions are present but lack format constraints (e.g., projectId format, prompt length limits). Error handling exists but lacks recovery guidance. No tool annotations (readOnlyHint/destructiveHint) despite clear risk classifications in metadata.
Deploy a VULK project to production on Cloudflare Pages. Returns the live deployment URL.
Modify an existing VULK project using natural language. Describe the changes and VULK's AI will update the relevant files. Works like a senior developer taking instructions.
Read the source code of any VULK project. Returns all files with their content, language, and paths.
Build a complete web application from a text description. VULK's AI generates all files (React components, pages, routing, styling, API endpoints, database schemas) and deploys a live preview. Generation takes 1-5 minutes depending on complexity. Returns the generated files, preview URL, and editor URL.
Get detailed information about a specific VULK project including status, deployment URLs, file count, and generation metadata.
Output schemas not documented. Tool descriptions mention return values (files, URLs, tokens) but no explicit schema definition visible in code. LLMs cannot plan downstream operations without knowing response structure.
No tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite clear risk classifications in metadata. generate/edit/deploy are WRITE operations; list/get/files/models/usage/subscribe are READ_ONLY. Annotations enable LLM safety reasoning.
Parameter constraints missing. 'prompt' parameter lacks length limits; 'model' lacks enum of valid options (claude-sonnet-4-20250514, gpt-4o, gemini-2.5-pro, deepseek-chat). Free-form strings invite hallucinated values.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 59 | 2026-07-28+ | v2 |
List your VULK projects. Returns project IDs, descriptions, creation dates, and status.
List available AI models and their capabilities, pricing, and speed.
Get a checkout link to upgrade your VULK plan.
Check API usage, credits, and rate limits for your VULK account.
Error handling lacks recovery guidance. Code catches errors (generationError, stream failures) but returns generic failure messages without actionable next steps. LLM cannot self-correct or retry intelligently.
No pagination support documented. 'list' and 'files' tools may return large result sets but lack limit/offset/page parameters or total count. Large responses risk context window exhaustion.