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mcp-c is a fully managed runtime for MCP applications—whether that is a full mcp-agent workflow, a FastMCP server, or a custom ChatGPT App backend. You write agents using the same decorators you use locally—@app.tool, @app.async_tool, @app.workflow—and deploy them with a single CLI command. The platform bundles your code, provisions containerized MCP servers, executes workflows on Temporal, secures secrets, and streams telemetry to your dashboard or OTEL backend.

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Key capabilities

  • Agents as MCP servers – every MCPApp is exposed as an MCP server with standard transports (SSE + streamable HTTP). Tools, resources, and prompts remain discoverable via the MCP APIs.
  • Temporal-backed durability – @app.async_tool and @app.workflow map to Temporal workflows with built-in retries, pause/resume, human input, and memoized state.
  • Automatic container orchestration – stdio MCP servers defined in mcp_agent.config.yaml run in hardened containers with lifecycle management, health checks, and auto-restarts.
  • Managed secrets – deployment secrets are encrypted at rest; per-user secrets are collected via mcp-agent cloud configure and scoped to that user’s execution.
  • Observability out-of-the-box – structured logs, traces, token counts, and workflow telemetry are available via the CLI or your own OTEL endpoint.
  • Simple client integration – mcp-agent install writes correct configs for Claude Desktop, Cursor, VS Code, and ChatGPT Apps.

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Architecture at a glance

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Deployment lifecycle

  1. Bundle – the CLI snapshots your directory, applying .mcpacignore and generating mcp_agent.deployed.secrets.yaml.
  2. Upload – artefacts are sent to the deployment service along with metadata (name, description, git commit, semantic version if provided).
  3. Build – containers are built for the application runtime and declared MCP stdio servers.
  4. Provision – infrastructure spins up in an isolated namespace; TLS certificates and routing are configured automatically.
  5. Health gate – Temporal workers and MCP endpoints must report healthy before the deployment is marked online.
  6. Operate – logs, traces, workflow state, and metrics become available through CLI and OTEL endpoints.

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Execution model

  • Synchronous tools (@app.tool) run inline in the MCP server process and return results immediately. Use them for quick lookups or wrappers around MCP servers.
  • Asynchronous tools (@app.async_tool) enqueue a Temporal workflow and return {workflow_id, run_id}. Callers poll workflows-get_status until completion.
  • Workflow classes (@app.workflow, @app.workflow_run) define reusable long-running units. The platform generates the MCP tools (workflows-<Name>-run, workflows-cancel, etc.) automatically.
  • Human input & signals – workflows can pause on await context.request_human_input(...) or custom signals. Temporal keeps state durable during waits.
  • Multi-agent orchestration – routers, evaluator-optimizer loops, and deep orchestrator patterns are distributed across worker tasks but remain addressable through MCP tool APIs.

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Security & authentication

  • Bearer tokens – default authentication mode. API keys created via mcp-agent login are scoped per user and can be rotated.
  • Unauthenticated mode – enable with mcp-agent deploy <name> --no-auth for public endpoints (required for ChatGPT Apps).
  • OAuth 2.1 (preview) – the platform’s authorization server follows the MCP OAuth specification, enabling end-to-end user authentication for enterprise scenarios. See Authentication →.
  • Downstream OAuth – use mcp_agent.config.yaml to configure client credentials for the agent’s outbound connections (e.g., Linear MCP server). Tokens are stored via the built-in token manager (token_store supports in-memory and Redis backends).

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Observability

  • Logs – stream with mcp-agent cloud logger tail <identifier>; filter by time, severity, regex; export as JSON or YAML.
  • Traces – enable OpenTelemetry exporters in mcp_agent.config.yaml or forward to your collector via mcp-agent cloud logger configure.
  • Token accounting – the runtime counts prompt+completion tokens when using supported AugmentedLLM providers.
  • Workflow insights – inspect status, history, and memo data via CLI (mcp-agent cloud workflows describe) or Temporal Web UI (coming soon to the console).

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Tooling surface

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When to use mcp-agent cloud

  • You want durable, resumable agents without operating Temporal or container infrastructure yourself.
  • You need to ship MCP servers to a broader audience (internal marketplace, customer-facing tools, ChatGPT Apps).
  • You require centralised observability and secrets management for all deployments.
  • You are ready to standardise on MCP for tool integration and agent orchestration.
If you need to run in regulated environments or on-premises, use the same app with your own Temporal cluster—see Durable agents →.

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Next steps