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Octopilot

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Model Context Protocol (MCP) server for Octopilot — enables AI agents to detect, generate, build, and wire up new repositories end-to-end using the Octopilot CI

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Описание

Model Context Protocol (MCP) server for Octopilot — enables AI agents to detect, generate, build, and wire up new repositories end-to-end using the Octopilot CI/CD toolchain.

README

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Model Context Protocol (MCP) server for Octopilot — enables AI agents to detect, generate, build, and wire up new repositories end-to-end using the Octopilot CI/CD toolchain.

What it does

Tool Description
detect_project_contexts Parse skaffold.yaml → pipeline-context JSON (languages, versions, matrix)
generate_skaffold_yaml Generate a skaffold.yaml for given artifacts
generate_ci_workflow Full .github/workflows/ci.yml using the standard octopilot pipeline
onboard_repository One-call onboarding: detect → generate files → return next steps
run_op_build Run op build via local binary or ghcr.io/octopilot/op container
list_actions All Octopilot GitHub Actions from the bundled registry
get_action_details Full spec, inputs, examples, gotchas for one action

op promote-image is intentionally not exposed. Image promotion between environments is operationally sensitive and must only run through a GitHub Actions workflow (with audit trail, OIDC credentials, and environment protection rules). Use generate_ci_workflow to produce the workflow that handles promotion safely.

Option A — Hosted (zero install)

Connect directly to the public server at https://mcp.octopilot.app — no cloning, no Python, no pip required.

# Cursor
fastmcp install cursor https://mcp.octopilot.app --name octopilot

# Claude Desktop
fastmcp install claude https://mcp.octopilot.app --name octopilot

Available hosted tools (stateless, no local dependencies):

Tool Description
list_actions Browse the Octopilot GitHub Actions registry
get_action_details Full spec, inputs, examples, gotchas for one action
generate_skaffold_yaml Generate a skaffold.yaml for given artifacts
generate_ci_workflow Full .github/workflows/ci.yml for a project

Need detect_project_contexts, onboard_repository, or run_op_build? Those tools need Docker and local filesystem access — use Option B below.


Option B — Local install (full suite)

# Clone and install
git clone https://github.com/octopilot/octopilot-mcp
cd octopilot-mcp
uv sync

Usage

Register with your IDE (one command, FastMCP 3 CLI)

Docker or Colima is the only external dependency. Most tools are pure Python; run_op_build pulls ghcr.io/octopilot/op:latest automatically with --pull always, so you always run the latest release.

# Cursor
uv run fastmcp install cursor src/octopilot_mcp/server.py --name octopilot

# Claude Desktop
uv run fastmcp install claude src/octopilot_mcp/server.py --name octopilot

Development (hot-reload)

uv run fastmcp dev src/octopilot_mcp/server.py --reload

Inspect tools from the terminal

# List all available tools
uv run fastmcp list src/octopilot_mcp/server.py

# Call a tool directly
uv run fastmcp call src/octopilot_mcp/server.py tool_list_actions

Run as a server directly

uv run octopilot-mcp

Manual JSON config (alternative to fastmcp install)

{
  "mcpServers": {
    "octopilot": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/octopilot-mcp", "octopilot-mcp"]
    }
  }
}

Pin to a specific op release (optional):

{
  "mcpServers": {
    "octopilot": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/octopilot-mcp", "octopilot-mcp"],
      "env": { "OP_IMAGE": "ghcr.io/octopilot/op:v1.0.0" }
    }
  }
}

Environment variables

Variable Default Description
OP_IMAGE ghcr.io/octopilot/op:latest Pin to a specific op release for reproducibility

Example agent interaction

User: Onboard this Rust API project to use Octopilot CI.

Agent: [calls onboard_repository("/path/to/my-api", "ghcr.io/my-org")]
       → Detected: rust (stable) in api/
       → Generated: skaffold.yaml, .github/workflows/ci.yml
       → Next steps: add .pre-commit-config.yaml, push changes

Development

uv sync --all-extras

# Run tests
uv run pytest tests/ -v

# Run with coverage
uv run pytest tests/ --cov=src/octopilot_mcp --cov-report=term-missing

Tool module coverage target: ≥95% (actions, detect, generate, op_runner). See CONTRIBUTING.md for details.

Resources

The server also exposes MCP resources for agent context:

  • octopilot://actions — Full actions registry JSON
  • octopilot://pipeline-context-schema — JSON Schema for pipeline-context
  • octopilot://docs/getting-started — Plain-text onboarding guide
  • octopilot://docs/skaffold-patterns — Common skaffold.yaml patterns

from github.com/octopilot/octopilot-mcp

Установить Octopilot в Claude Desktop, Claude Code, Cursor

Рекомендуется · одна команда, все IDE
unyly install octopilot-mcp

Ставит в Claude Desktop, Claude Code, Cursor и VS Code — сам разбирается с npx, uvx и сборкой из исходников.

Впервые? Поставь CLI: curl -fsSL https://unyly.org/install | sh

Или настроить вручную

Выполни в терминале:

claude mcp add octopilot-mcp -- uvx --from git+https://github.com/octopilot/octopilot-mcp octopilot-mcp

FAQ

Octopilot MCP бесплатный?

Да, Octopilot MCP бесплатный — установка в пару кликов через Unyly без оплаты.

Нужен ли API-ключ для Octopilot?

Нет, Octopilot работает без API-ключей и переменных окружения.

Octopilot — hosted или self-hosted?

Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.

Как установить Octopilot в Claude Desktop, Claude Code или Cursor?

Открой Octopilot на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.

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