How do I use AIConductor?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@AIConductor refine user onboarding flow" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

AIConductor

by MohanTn

Overview Schema Related Servers Score Discussions

TypeScript

Local

AIConductor

An open-source Model Context Protocol (MCP) server that orchestrates multi-stakeholder feature refinement and development execution workflows for AI-assisted software teams.

License: MIT Docker Node.js TypeScript

Overview

AIConductor gives your AI coding agent a structured, auditable pipeline — from raw feature idea to merged code. It exposes 39 MCP tools that any MCP-compatible agent (Claude, Copilot, Cursor, Cline, etc.) can call to drive tasks through two workflows:

Feature Refinement — Break a feature into discrete tasks, then route each task through a sequential stakeholder approval chain before any code is written.
Development Execution — Drive approved tasks through a Developer → Code Reviewer → QA lifecycle with full audit history.

Related MCP server: Spec MCP Server

Features


Multi-Stakeholder Reviews	Product Director → Architect → UI/UX Expert → Security Officer approval chain
Development Pipeline	Developer → Code Reviewer → QA → Done with `NeedsChanges` feedback loops
Real-time Dashboard	Kanban board at `localhost:5111` with live WebSocket updates
Multi-Repository	Manage tasks across multiple codebases from a single server
Refinement Reports	Generate markdown/HTML/JSON reports of the full refinement process
Workflow Checkpoints	Save and restore workflow state; rollback the last stakeholder decision
Task Execution Planning	Dependency analysis with parallelisation suggestions
Zero External Dependencies	Everything persisted in a local SQLite database

Prerequisites

Docker and Docker Compose
An MCP-compatible AI agent (Claude Desktop, VS Code Copilot, Cursor, Cline, etc.)

Quick Start

git clone https://github.com/your-org/aiconductor.git
cd aiconductor
docker compose up -d

The MCP server and dashboard are now running. Connect your AI agent by adding the following to your MCP config:

Claude Desktop — ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "aiconductor": {
      "command": "docker",
      "args": ["exec", "-i", "-e", "DISABLE_DASHBOARD=true", "aiconductor-mcp", "node", "dist/bundle.js"]
    }
  }
}

VS Code — .vscode/mcp.json or user settings

{
  "mcp.servers": {
    "aiconductor": {
      "command": "docker",
      "args": ["exec", "-i", "-e", "DISABLE_DASHBOARD=true", "aiconductor-mcp", "node", "dist/bundle.js"]
    }
  }
}

Restart your agent. Open the dashboard at http://localhost:5111.

Workflows

Two slash-command workflows are included in .github/prompts/ and can be invoked directly from your agent.

`/refine-feature` — Feature Refinement

Turns a plain-text feature description into stakeholder-approved, implementation-ready tasks.

Feature Description
  │
  ├─ Scope determination & context gathering
  ├─ Attachment analysis (images, docs, spreadsheets)
  ├─ Clarification questions
  ├─ SMART acceptance criteria generation
  ├─ Test scenario generation
  ├─ Task breakdown (5–8 tasks)
  │
  └─ Batched stakeholder review cycle
       │
       ├─ Product Director  →  Architect  →  UI/UX Expert  →  Security Officer
       │        │                  │               │                  │
       │     reject             reject          reject             reject
       │        └──────────────────┴───────────────┴──────────────────┘
       │                                  ▼
       │                         NeedsRefinement → restart
       │
       └─ All tasks reach ReadyForDevelopment ✓

Tasks are processed in batches per role — a single role adoption covers all tasks in one pass, dramatically reducing context overhead.

`/dev-workflow` — Development Execution

Drives ReadyForDevelopment tasks through implementation to Done.

ReadyForDevelopment
  └─→ InProgress ─→ InReview ─→ InQA ─→ Done ✓
           │             │          │
           └─────────────┴──────────┘
                    NeedsChanges → back to InProgress

Each stage is handled by a distinct role: Developer (implements & tests), Code Reviewer (approves or requests changes), QA (verifies acceptance criteria).

Dashboard

Open http://localhost:5111 in your browser.

Kanban Board	Feature Details

Kanban board — Task cards arranged by workflow status; empty columns collapse to a slim strip so all columns fit on screen without horizontal scrolling
Real-time updates — WebSocket connection pushes task state changes instantly to all open browser tabs
Detail panel — Per-feature acceptance criteria, test scenarios, clarifications, and refinement step progress
Multi-repo switcher — Switch between registered repositories from the sidebar
Reviewer presence — See which reviewers are currently active on a feature

MCP Tools Reference

Orchestration

Tool	Description
`get_next_step`	Returns the next role, system prompt, and required output fields for a task — the primary orchestration driver
`get_workflow_snapshot`	Compressed overview of all task statuses and roles for a feature (~5 KB vs ~50 KB for full fetch)
`get_task_execution_plan`	Dependency analysis with optimal execution order and parallelisable phases
`get_similar_tasks`	Find comparable tasks from past features to aid estimation
`get_workflow_metrics`	Cycle time, throughput, and bottleneck statistics

Stakeholder Reviews

Tool	Description
`add_stakeholder_review`	Submit an approve/reject review with role-specific structured fields
`validate_review_completeness`	Pre-flight check that all required fields are present before submitting
`get_task_status`	Current status, completed/pending reviews, and allowed transitions
`get_review_summary`	Completion percentage and stakeholder progress across all tasks
`validate_workflow`	Dry-run validation — check if a transition can proceed
`rollback_last_decision`	Undo the most recent stakeholder decision on a task

Development Pipeline

Tool	Description
`transition_task_status`	Move a task through development stages (InProgress → InReview → InQA → Done)
`batch_transition_tasks`	Transition multiple tasks atomically in a single call
`get_next_task`	Get the next task to work on, optionally filtered by status
`get_tasks_by_status`	List all tasks matching a specific status
`verify_all_tasks_complete`	Assert every task in a feature has reached Done
`update_acceptance_criteria`	Mark individual acceptance criteria as verified
`batch_update_acceptance_criteria`	Verify multiple criteria in one call

Feature & Task Management

Tool	Description
`create_feature`	Create a new feature with slug, name, and description
`update_feature`	Update feature metadata (name, description)
`get_feature`	Load full feature data including all tasks, criteria, and scenarios
`list_features`	List all features in a repository with task counts
`delete_feature`	Remove a feature and all associated tasks, reviews, and transitions
`add_task`	Add a task to a feature with acceptance criteria and test scenarios
`update_task`	Modify task properties (title, description, criteria, scenarios, dependencies)
`delete_task`	Remove a task and all its data

Refinement Tracking

Tool	Description
`update_refinement_step`	Record progress through the 8-step refinement workflow
`get_refinement_status`	Full refinement progress including step completion and criteria
`add_feature_acceptance_criteria`	Add feature-level acceptance criteria (before tasks are created)
`add_feature_test_scenarios`	Add feature-level test scenarios
`add_clarification`	Record a clarification question and answer
`add_attachment_analysis`	Store analysis results for an attached file or design
`generate_refinement_report`	Export the full refinement process as markdown, HTML, or JSON

Checkpoint Management

Tool	Description
`save_workflow_checkpoint`	Save current workflow state with a description
`list_workflow_checkpoints`	List all saved checkpoints for a feature
`restore_workflow_checkpoint`	Resume from a previously saved checkpoint

Repository Management

Tool	Description
`register_repo`	Register a new repository namespace
`list_repos`	List all registered repositories with task counts
`get_current_repo`	Auto-detect the repository from the current working directory

Stakeholder Roles

Role	Focus Areas	Key Output Fields
Product Director	Market fit, user value, acceptance criteria quality	`marketAnalysis`, `competitorAnalysis`, `quickSummary`
Architect	Technical feasibility, design patterns, technology choices	`technologyRecommendations`, `designPatterns`
UI/UX Expert	Usability, accessibility, user behaviour	`usabilityFindings`, `accessibilityRequirements`, `userBehaviorInsights`
Security Officer	Security requirements, compliance, risk assessment	`securityRequirements`, `complianceNotes`

Project Structure

src/
├── index.ts                 # MCP server — tool definitions and request handling
├── AIConductor.ts     # Business logic for all workflow operations
├── WorkflowValidator.ts     # State machine — validates transitions and returns role prompts
├── DatabaseHandler.ts       # SQLite CRUD operations
├── rolePrompts.ts           # System prompts for each stakeholder role
├── websocket.ts             # WebSocket server — real-time event broadcasting
├── dashboard.ts             # Express web server (port 5111)
├── types.ts                 # TypeScript interfaces
└── client/                  # React SPA (Vite)

.github/prompts/
├── refine-feature.prompt.md # Feature refinement workflow
└── dev-workflow.prompt.md   # Development execution workflow

Database:

Docker: /data/tasks.db (persistent volume task-review-data)
Local: ./tasks.db in project root

Local Development

npm install
npm run dev          # Watch mode — recompiles on change
npm run build        # Production build (server + client)
npm test             # Run all tests with coverage
npm run lint         # TypeScript and code quality lint
npm run dashboard    # Start dashboard standalone (port 5111)

To rebuild the Docker image after code changes:

docker compose up -d --build

CI/CD Pipeline

All pull requests automatically run through our GitHub Actions CI workflow, which includes:

Build — TypeScript compilation with npm run build
Lint — Code quality checks with npm run lint
Test — Jest tests with coverage tracking via npm test
Coverage — Coverage metrics uploaded to Codecov

See CONTRIBUTING.md for details on running these checks locally, understanding failures, and our branch protection rules.

Configuration

Variable	Default	Description
`DATABASE_PATH`	`./tasks.db`	SQLite file location (`/data/tasks.db` in Docker)

To reset all data:

docker compose down -v && docker compose up -d

License

MIT

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quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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AIConductor

Overview

Features

Prerequisites

Quick Start

Workflows

/refine-feature — Feature Refinement

/dev-workflow — Development Execution

Dashboard

MCP Tools Reference

Orchestration

Stakeholder Reviews

Development Pipeline

Feature & Task Management

Refinement Tracking

Checkpoint Management

Repository Management

Stakeholder Roles

Project Structure

Local Development

CI/CD Pipeline

Configuration

License

Maintenance

Resources

Looking for Admin?

Latest Blog Posts

MCP directory API

`/refine-feature` — Feature Refinement

`/dev-workflow` — Development Execution