01
Pick the first use case
Choose the customer workflow where agent access is most likely to create immediate value.
Validate AI access before building a platform team.
0mcp gives startups a fast, controlled path from existing API to hosted MCP server. You can test customer demand for AI workflows without pulling engineers away from core product work.
Startup launch
public-api.openapi.yaml12 operations
create_lead
sync_record
fetch_report
Startup API
MCP beta
Agent request
call fetch_report for beta customer status 200 | latency 142ms | beta-ready
Fast validation
Lean engineering effort
Production path
Move from customer request to working MCP quickly.
02
Expose only what is needed
Turn a small set of proven API operations into MCP tools instead of opening the whole API.
03
Learn from usage
Measure calls and failures before investing in a larger AI integration roadmap.
A credible MCP launch without unnecessary buildout.
Early teams need speed, but customer-facing integrations still need judgment. 0mcp keeps the first launch small, inspectable, and ready to improve as usage becomes clearer.
Keep the scope narrow
Start with the few actions customers actually ask agents to perform.
Avoid throwaway infrastructure
Use hosted MCP infrastructure instead of building protocol, deployment, and observability from scratch.
Grow into production
Add revisions, analytics, and customer-facing polish when demand proves itself.
Spend runway on customer value.
Build MCP from scratch
Start from your existing API
Protocol learning curve
Hosted MCP endpoint
Speculative platform work
Use-case-first rollout
Hard-to-measure beta
Operational usage signals
Test MCP demand with your current API
Launch a focused agent surface before committing to a larger platform build.