Full-stack Model Context Protocol platform for building, testing, and deploying MCP servers and agentic AI applications
Interactive worlds for people and AI to build, learn and solve problems together
Stage
Not given
Not given
Founded
Not given
Not given
Based in
US
Not given
Pricing model
Freemium
Free
Pricing
Hobby: free forever with 30k requests/mo; Pro: $30/org/mo with 300k requests/mo included and $0.10 per 1k extra requests beyond 300k; Enterprise: custom pricing starting at $250/mo with dedicated infrastructure and custom SLAs.
Not given
Key features
Open-source TypeScript SDK with declarative decorators and Zod validation
NitroStudio desktop IDE for visual tool testing and AI reasoning visualization
NitroCloud serverless hosting with auto-scaling and custom domains
GitHub integration for one-click deployment
Enterprise authentication modules
White-label NitroChat AI interface
99.99% uptime SLA on NitroCloud
API to MCP conversion tools
Photo to brick set conversion with 3D preview and parts list
Workbench for collaborative brick-by-brick editing
MCP server integration with Claude Code, Codex, Cursor and more
Build structure checking and validation
OAuth sign-in with no token storage
Offline build access with local browser storage
Export digital manuals and wanted parts lists
Interactive environments for AI training
Consent-based human demonstration recording
What makes it different
Designed for teams building custom MCP tools from their own APIs, not a catalog of prebuilt connectors
Complete full-stack platform combining SDK, IDE, and cloud hosting in one suite
Open-source core with npm-installable packages for SDK, CLI, and UI widgets
80,000 deployments and 13 million requests already in production
Enterprise-grade security with AES-256 encryption and JWT authentication
Checked outcomes - part connections and build validity are verified by software before delivery
Real objects from photos - converts pictures into buildable brick sets with actual parts that exist
People and AI work on the same design simultaneously within the workbench
Measurable AI learning with transfer testing across different worlds