Agiflow MCP Server

AgiFlow AI Plugin

Official AgiFlow plugin for AI coding clients. Drive AgiFlow project management — planning, grooming, execution, and review — directly from your AI coding tool.

Works with Claude Code, Codex, Cursor, Antigravity, and Gemini CLI.

Installation

This repo is a self-contained, multi-client plugin bundle. Until it is published to each client's marketplace, load it as a local plugin directory.

Claude Code

git clone <your-remote>/agiflow-ai-plugin
claude --plugin-dir ./agiflow-ai-plugin

The bundled .mcp.json wires the AgiFlow MCP server automatically. Use /mcp inside Claude Code to check the connection.

Antigravity (Google)

Place the plugin folder in one of Antigravity's plugin locations, then restart:

# Workspace-level (this project only)
mkdir -p .agents/plugins && cp -R /path/to/agiflow-ai-plugin .agents/plugins/

# Global (all workspaces)
mkdir -p ~/.gemini/config/plugins && cp -R /path/to/agiflow-ai-plugin ~/.gemini/config/plugins/

Antigravity reads the root plugin.json marker, the skills/, and mcp_config.json automatically.

Cursor

Add manually in Cursor Settings → MCP / Plugins, pointing at this folder. Cursor's stable surface is MCP config — the bundled .mcp.json provides it.

Codex

Add the AgiFlow plugin marketplace, then install the plugin from that marketplace:

codex plugin marketplace add AgiFlow/ai-plugin
codex plugin add agiflow-ai-plugin@agiflow

For local development, point Codex at this checkout as a marketplace root:

codex plugin marketplace add ./agiflow-ai-plugin
codex plugin add agiflow-ai-plugin@agiflow

Gemini CLI

gemini extensions install <your-remote>/agiflow-ai-plugin

The bundled gemini-extension.json connects the AgiFlow MCP server via mcp-remote.

How to develop

git clone <your-remote>/agiflow-ai-plugin
claude --plugin-dir ./agiflow-ai-plugin

• Add new workflow instructions under skills/<name>/SKILL.md.
• Keep shared guidance in references/ (e.g. references/agiflow-agents.md).
• See references/plugin-types.md for per-client manifest notes.

Features

This plugin connects to the AgiFlow MCP server (https://agiflow.io/api/v1/mcp) and exposes AgiFlow tools across these categories:

• Projects — create, inspect, and update projects and their statuses
• Tasks — create, list, get, update, reorder, and batch-create tasks
• Work units — group tasks into deliverable features/epics and track progress
• Workflows — acquire/release locks and coordinate multi-agent runs
• Members — list and assign agent members to work
• Comments — document decisions and progress on tasks
• Vault — read and set scoped configuration entries

Bundled skills

The plugin ships 10 workflow skills that mirror AgiFlow's scrum pipeline. Your AI client loads them on demand when your request matches their description — you generally don't invoke them by name:

Skill	Phase	Use it to…
getting-started	orient	get coached on where to start and which workflow fits
project-plan	Planning	break requirements into vertical-slice tasks (Planning status)
refine-task	Planning	turn a vague task into an autonomous-ready spec
backlog-grooming	Planning → Todo	verify, prioritize, and promote tasks into work units
run-work	Todo → Done	execute a whole work unit end-to-end in one session
run-task	Todo → Done	execute a single task through to Review
review-work	Review	verify acceptance criteria and file follow-ups
triage	diagnose	classify project issues by severity and recommend actions
daily-standup	report	a read-only pulse of done / in-progress / blocked / next
orchestrate	dispatch	route the highest-priority ready work to agents

Shared guidelines (status model, transitions, tags, work-unit sizing) live in references/agiflow-agents.md.

Example usage

> Plan a feature: add per-user notification preferences
> Groom the backlog and promote the ready tasks to Todo

> Run task DXX-2
> Execute the checkout work unit end-to-end

> Review the auth work unit against its acceptance criteria
> Give me a daily standup for this project

> Why is this project stuck?
> What should an agent pick up next?

Self-hosted

For a self-hosted AgiFlow instance, point the MCP wiring at your endpoint via the AGIFLOW_AI_PLUGIN_MCP_URL environment variable (consumed by gemini-extension.json):

export AGIFLOW_AI_PLUGIN_MCP_URL="https://mcp.your-agiflow-instance.com/api/v1/mcp"

For other clients, edit the server URL in .mcp.json, mcp.json, and mcp_config.json.

Documentation

• AgiFlow: https://agiflow.io
• Plugin client compatibility: references/plugin-types.md

License

MIT