Runner MCP

Runner MCP

Confirmed cross-product role (2026-10-20): Runner-MCP is the bounded authenticated execution and MCP access boundary, not the company cockpit or a second technical orchestrator. AIfordable owns the company-wide business cockpit and customer/product/registration/desired-state data; Runner-Fabric owns the technical F10/F17 Cockpit and is the sole technical coordinator. Existing Agent Bus relay code is retained as a transport, not a duplicate scheduler. Runner-MCP's optional Fabric bridge remains narrow, with no automatic expansion of authority. Cross-product role contract.

Let an AI run tests and controlled staging operations on your own server without giving it a general-purpose remote shell.

Runner MCP is a security-first, self-hosted Model Context Protocol service. You configure the projects and operations that exist; the AI selects from those bounded capabilities instead of supplying arbitrary shell commands, executables, filesystem paths or service names.

_An AIfordable project — secure software. Built with AI. Fairly priced._

Runner MCP is currently alpha and in maintenance mode. The canonical public release is v0.1.3, available on PyPI, the official MCP Registry and GitHub Releases. The core alpha scope is complete; new work is limited to security fixes, bugs, compatibility, and concrete operational needs rather than speculative feature growth.

Start here: 5-minute demo · Quickstart · Security model · Threat model

One-time new-host staging (local operator only): Source-pinned host bootstrap. This does not activate a service or confer remote authority.

The difference in one table

Runner MCP is intentionally not a sandbox for untrusted code and does not claim to replace every operator maintenance tool. Its goal is narrower: routine AI-assisted development and staging operations should not require handing the AI an unrestricted shell.

Why Runner MCP exists

AI-assisted development becomes much more useful when the assistant can verify changes against real projects. But routine tasks such as reading a safe file, running a known test suite or checking a staging service do not require the authority of a general-purpose remote shell.

Runner MCP turns those routine operations into explicit capabilities. The operator configures projects and named actions locally; the AI client selects from those capabilities instead of supplying arbitrary commands, executable paths or private infrastructure values.

The original motivation was practical: reduce the day-to-day dependency on broad remote-control tooling while keeping useful development and staging automation.

How it works

flowchart LR

A[AI client] -->|private MCP transport| R[Runner MCP] A -->|optional bounded requests| G[GitHub mailbox] G -->|strict allow-listed bridge| R H[Human operator] -->|approval / emergency stop| R R --> P[Configured projects] R --> T[Predefined test profiles] R --> O[Controlled staging operations]

Runner MCP is the local safety boundary. GitHub can be used for source collaboration and, optionally, as a bounded mailbox transport; it is not turned into a mechanism for sending arbitrary shell commands.

The normal authority model is deliberately asymmetric: read-only inspection is easier, mutating staging actions are narrower, higher-risk actions require short-lived local approval, and production mutations remain disabled.

Optional Runner Fabric work-unit bridge

Runner Fabric remains a separate product and orchestration/control-plane owner. When both products run on the same host, Runner MCP can optionally expose a loopback-only transport bridge with three coarse tools — run, inspect and cancel a bounded Fabric work-unit. This lets an AI hand off a whole repository-change workflow without receiving generic GitHub/Git/shell primitives.

The bridge is disabled by default and does not change Runner MCP's authority model. See Optional Runner Fabric bridge.

For ChatGPT/Codex clients, Runner MCP can also render a private first-party plugin package that reuses this existing MCP boundary without embedding bearer-token values or adding another control plane. See First-party ChatGPT/Codex control path.

I just want to use it

You do not need to understand the Python source code for the basic workflow.

Starting with v0.1.1, the shortest persistent install is:

uv tool install aifordable-runner-mcp

runner-mcp setup runner-mcp doctor runner-mcp guide runner-mcp status

uvx aifordable-runner-mcp ... is also suitable for package discovery and short-lived evaluation, but a persistent tool install is the clearer choice for a self-hosted service with private configuration and autostart.

To install directly from source instead:

git clone https://github.com/Blacksp1d3r/runner-mcp.git

cd runner-mcp ./install.sh

Start with QUICKSTART.md for the guided installation.

Connectivity remains loopback-first. runner-mcp setup in public mode records an external HTTPS resource/auth identity only; it does not expose a bind address, install TLS, edit DNS/firewalls, configure a reverse proxy or create a tunnel. runner-mcp guide reports only a generic connectivity category without printing the configured hostname or private URL. For supported OpenAI products, private access can use Secure MCP Tunnel; an external HTTPS reverse proxy remains an operator-managed alternative.

If Runner MCP runs under a dedicated service account while you log in with a separate operator account, install a local operator wrapper. Skip this step when Runner MCP runs under the same account you use interactively:

./install-operator.sh SERVICE_USER

runner-mcp guide

The wrapper keeps the private configuration with the service account and delegates through sudo; it does not copy credentials into the operator account.

For a user-to-developer path, see docs/USING_AND_EXTENDING.md. Contributors can start with CONTRIBUTING.md.

For the zero-additional-service-cost GitHub mailbox pattern, see docs/GITHUB_MAILBOX_BRIDGE.md. The public package now includes both a transport-neutral processor and a fixed-host GitHub transport; completion feedback and watcher resilience are documented in docs/COMPLETION_FEEDBACK.md and docs/WATCHER_RESILIENCE.md. Bounded multi-project scheduling and capacity controls are documented in docs/CONCURRENCY.md.

Dependency/build/interpreter contract changes are deliberately refused by self-update and require a local bootstrap/manual upgrade; see self-update compatibility.

Runner MCP is developed as an AIfordable project. Public launch readiness is tracked in docs/LAUNCH_READINESS.md. See also the changelog, release checklist and prepared launch copy.

Useful commands:

runner-mcp setup

runner-mcp doctor runner-mcp guide runner-mcp status runner-mcp emergency-stop on runner-mcp emergency-stop status runner-mcp emergency-stop off runner-mcp autostart preflight runner-mcp autostart status runner-mcp project list runner-mcp project add ... runner-mcp test-profile list PROJECT runner-mcp test-profile add PROJECT NAME --preset pytest runner-mcp service-config list PROJECT runner-mcp service-config add PROJECT ALIAS --unit UNIT runner-mcp database-config list runner-mcp database-config add PROJECT runner-mcp retention preview PROJECT runner-mcp database restore-plan PROJECT BACKUP_ID runner-mcp migration-config add PROJECT --preset alembic runner-mcp deployment-config list runner-mcp deployment-config add PROJECT --release-root PATH --service ALIAS runner-mcp github-mailbox status runner-mcp github-watcher once runner-mcp plugin-package registered-app --app-id APP_ID --output PRIVATE_DIR runner-mcp plugin-package http --url MCP_URL --output PRIVATE_DIR runner-mcp serve

MCP/mailbox clients can also use:

runtime_status

runtime_doctor

self_update

self_update_status

The emergency stop is intentionally easy to activate and harder to clear.

What works today

Current implemented foundations include:

• authenticated MCP over Streamable HTTP;
• project allow-listing;
• safe project file listing, metadata and paged reads;
• path traversal, symlink and secret-file protection;
• request IDs, rate limiting and audit logging;
• DNS-rebinding protection;
• external operator emergency stop;
• rollback-retention policy using both minimum count and minimum age;
• local read-only retention preview with strict release/backup validation and no deletion authority;
• explicit separation between code rollback and database restore;
• controlled asynchronous test jobs;
• commit-pinned self-update for the canonical Runner MCP project, gated by an unchanged Python/build/dependency compatibility contract, fixed lint/unit validation and internal self-reexec;
• bounded fair multi-project test scheduling with per-project opt-in parallelism;
• safe queue, worker and job observability without infrastructure disclosure;
• allow-listed systemd-user staging service status/start/stop/restart;
• optional private service health checks;
• private PostgreSQL backups with safe metadata listing;
• local read-only PostgreSQL restore preflight without database mutation;
• controlled migration status/apply with mandatory pre-migration backup;
• staging-only release planning and asynchronous deployment jobs;
• clean-Git release archives, atomic activation and health-gated rollback;
• safe release history plus one-step asynchronous staging rollback;
• built-in allow-listed project adapters with safe capability inspection;
• strict MCP tool arguments: unknown fields are rejected rather than ignored;
• short-lived human approval gates for migration, deploy and rollback;
• staging-only mutation policy; production remains read-only;
• private ChatGPT connectivity can use OpenAI Secure MCP Tunnel without an inbound public port;
• predefined test profiles only;
• test timeout, cance