Runner MCP

Runner MCP

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. The canonical public distribution is available on PyPI, the official MCP Registry and GitHub Releases. The first fully aligned cross-registry release is v0.1.2.

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

The difference in one table

Approach	AI can inspect/run configured work	Arbitrary shell is the normal interface	Explicit audit/safety boundary
SSH / broad remote-control tool	Yes	Yes	Depends on the surrounding setup
Runner MCP	Yes	No	Yes — allow-lists, bounded output, emergency stop and approval gates

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.

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 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 serve

# MCP/mailbox clients can also use:
# runtime_status
# runtime_doctor
# self_update <full-main-commit>
# self_update_status <job-id>

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, cancellation and process-group cleanup;
• scrubbed, bounded test logs;
• recovery of interrupted test-job metadata after restart;
• interactive setup, status and doctor commands;
• CLI project management without manual YAML editing;
• CLI test-profile management with pytest, Ruff and custom presets;
• non-root local installer and guided Quickstart.

What Runner MCP deliberately does not do

Runner MCP is not intended to provide:

• a general remote shell;
• arbitrary commands supplied by an AI client;
• automatic production database restore;
• database restore execution in the current implementation;
• automatic multi-release rollback cascades;
• unrestricted service control;
• untrusted public-fork execution on a privileged persistent runner.

Production actions remain out of scope until stronger approval and isolation controls are implemented.

Safety model

Runner MCP is built around:

• deny by default;
• explicit project, service and test-profile allow-lists;
• no arbitrary shell as a normal interface;
• no secrets in MCP tool output;
• strict path validation;
• auditable actions;
• least-privilege runtime permissions;
• explicit risk classes for operational tools;
• external operator emergency stop;
• retention confirmation before operator actions;
• one code rollback step per approved action;
• database restore always requiring explicit human approval;
• DNS-rebinding protection tied to the configured MCP resource URL.

See:

• security/SECURITY_BASELINE.md
• security/THREAT_MODEL.md
• security/OPERATOR_SAFETY.md
• security/TEST_EXECUTION.md

Public repository rule

This repository must never contain real infrastructure details.

Do not commit:

• real IP addresses or hostnames;
• internal domains;
• private service endpoints or ports;
• usernames;
• absolute deployment paths from a real installation;
• database endpoints;
• credentials or tokens;
• private keys;
• database dumps or backups.

Public examples use placeholders only. Real values belong in private local configuration or an appropriate secret store.

Controlled test execution

An MCP client may choose only a configured project and named test profile.

A test profile defines the exact executable, exact argument array, project-relative working directory, timeout, log limit and explicitly allowed environment variables.

Commands are launched with shell=False.

Running a test still executes project code. Until stronger sandboxing exists, only trusted repository revisions should be tested.

Rollback philosophy

Code rollback and database recovery are separate operations.

Code releases are retained using both:

• a minimum number of releases;
• a minimum age.

A release is eligible for cleanup only when both conditions permit it.

A single approved rollback action may move back exactly one code release. Runner MCP must stop, health-check and reassess before another rollback.

Database restore is never an automatic side effect of code rollback and always requires explicit human approval.

Development

Target runtime: Python 3.12+.

Core technologies:

• official MCP Python SDK;
• Pydantic;
• PyYAML;
• Starlette;
• Uvicorn;
• pytest;
• Ruff.

The implementation uses Streamable HTTP for MCP.

Do not connect a privileged persistent self-hosted runner to untrusted public pull-request code.

Project status

The current implementation state is tracked in:

• handover/CURRENT_STATE.md
• roadmap/ROADMAP.md

Runner MCP v0.1.0 is the first public alpha release. The project remains under active development. The public GitHub mailbox stack now includes protocol-v1 validation, replay lifecycle, fixed-host transport, an incremental restart-safe watcher, a loopback-only MCP executor, bounded concurrent request handling, fail-closed missing-result recovery and a private-config runtime/CLI. Shared-watcher migration, exactly-once completion feedback, clean-Linux demo validation, non-root autostart packaging and privacy-safe connectivity guidance are complete. Ongoing alpha hardening and post-release work are tracked in the roadmap and release checklist.