The shortest accurate answer
Product / application
│
│ UHP: execute through a harness
▼
Complete agent harness
│
│ MCP: reach tools / resources / prompts
▼
External capabilities
The protocols can be used in the same system. A UHP configured harness may itself be configured with MCP servers; the two standards address different interfaces.
Side-by-side
| Dimension | UHP | MCP |
|---|---|---|
| Primary boundary | Client/product → complete harness runtime | AI host/client → MCP server capabilities |
| Main purpose | Execute agent work consistently across harnesses | Expose tools, resources and prompts consistently |
| Execution lifecycle | Tasks, sessions, streaming, cancellation, files, errors, results | Protocol interactions and capability primitives; not a complete harness runtime contract |
| Core transport | HTTP; SSE for streaming | JSON-RPC data layer with supported transports |
| Typical participant | Product backend, CLI or CI client | AI application host plus MCP clients and servers |
| Can coexist? | Yes. UHP harness configurations can include MCP servers. | |
Why the confusion happens
Both protocols appear in agent infrastructure discussions and both reduce bespoke integrations. But “integration” happens at several levels. MCP focuses on context and capability exchange. Its official architecture explicitly says it does not dictate how AI applications use LLMs or manage the provided context. UHP is deliberately about the higher execution contract around a complete harness.
Example
Imagine a product that lets users ask an agent to inspect a repository, query an issue tracker and produce a patch. The product could send the job through UHP to a configured Claude Code or Codex harness. Inside that harness, MCP could expose GitHub, Sentry or internal database tools. UHP gives the product a stable task/session/file lifecycle; MCP gives the harness a stable capability interface.
When UHP adds value
- You need to switch or compare complete harness runtimes without rewriting product execution logic.
- You need common task states, session continuation, artifacts and cancellation across those runtimes.
- You are building infrastructure that manages harnesses as shared execution backends.
When MCP adds value
- You want an AI application to discover and use external tools or context through a standard interface.
- You want tool providers to expose focused capabilities independently of the AI host.
- You need a broad ecosystem of interoperable external integrations.