AI Tools

flow exposes your automation to AI coding tools through the Model Context Protocol. Your assistant can discover, run, and write executables the same way you do — with full access to workspace context, secrets handling, and execution history.

MCP Server

Setup

Add this to your MCP client configuration (Claude Code, Cursor, Cline, or any MCP-compatible client):

{
  "mcpServers": {
    "flow": {
      "command": "flow",
      "args": ["mcp"]
    }
  }
}

The server runs over stdio. That's the entire setup.

What's available

Tools

Tool	Description
get_info	Current workspace, schema URLs, and documentation index
list_workspaces	All registered workspaces
get_workspace	Details and config for a specific workspace
switch_workspace	Change the active workspace
list_executables	Browse executables — filterable by tag, verb, workspace
get_executable	Full definition and metadata for a specific executable
execute	Run an executable by ref
get_execution_logs	Output from recent runs
sync_executables	Refresh cached workspace and executable state
write_flowfile	Create or update a .flow file, validated before writing

Prompts

Structured prompts the assistant can invoke for common tasks:

Prompt	Description
generate_executable	Generate a new executable for a described task
generate_project_executables	Generate a full automation set for a project
debug_executable	Debug a failing executable
migrate_automation	Convert existing Makefile, npm scripts, or shell scripts to flow
explain_flow	Explain flow concepts and configuration

Wiring It Up for Your Project

Connecting flow via MCP gives your assistant the tools — but it won't automatically reach for them over plain shell commands. If you ask it to run tests, it might just run go test ./... instead of your test executable.

The solution is a small instruction file committed to your repo. Claude Code calls these background skills: they load into context on every session without any manual invocation.

Create .claude/skills/flow-context/SKILL.md:

---
name: flow-context
description: This project uses flow for automation. When asked to build, test, run, deploy, lint, or perform any dev task, check for a flow executable before falling back to raw shell commands.
user-invocable: false
---

This repository uses flow for automation. The `mcp__flow__*` MCP tools are available.

When the user asks to build, test, deploy, lint, generate, or run anything:

1. Call `mcp__flow__list_executables` first — check whether a flow executable already handles the task
2. If found, use `mcp__flow__execute` with the matching verb and name
3. Only fall back to shell if no flow executable exists for the task

Call `mcp__flow__get_info` at the start of a session or when you need schema URLs to author .flow files.

user-invocable: false keeps it out of the / command menu — it's not something you invoke, it's just always there. Update the body with the specific executables your project uses.

llms.txt

flow publishes an llms.txt index following the llmstxt.org standard — a plain-text map of all documentation pages and schemas. Tools that support it can pull the full docs in one request.

https://flowexec.io/llms.txt

The get_info MCP tool returns this URL as well, so a connected assistant can find it without any prior knowledge of the site.

JSON Schemas

Every flow file type has a published JSON schema. Adding a yaml-language-server comment gives you validation and autocomplete in any schema-aware editor:

# yaml-language-server: $schema=https://flowexec.io/schemas/flowfile_schema.json
executables:
  - verb: deploy
    name: staging
    serial:
      execs:
        - cmd: npm run build
        - ref: infra/k8s:apply-staging

File	Schema
*.flow / *.flow.yaml	flowfile_schema.json
flow.yaml (workspace)	workspace_schema.json
*.flow.tmpl (template)	template_schema.json
User config	config_schema.json