Which integrations are available for this server?

Enables automatic git repository initialization, committing generated code, and applying tags for production-ready releases. Generates GitHub Actions CI workflow files as part of the project output to enable continuous integration for the generated code. Allows using local Ollama models as the LLM provider for the pipeline, enabling offline or private AI-powered development. Allows using OpenAI's GPT models as the LLM provider for reasoning and code generation in the pipeline.

How do I use Dev Team MCP?

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., "@Dev Team MCP Create a FastAPI endpoint for user registration with password hashing" 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.

Dev Team MCP

by th1234th

Overview Schema Related Servers Score Discussions

Python

Local

Dev Team MCP — Autonomous AI Software Development Team

Submit a ticket → get production-ready code.
An autonomous MCP server that runs a full software development pipeline: Planner → Coder → Tester → Reviewer → Delivery
Runs on port 8002 alongside:
DevOps MCP (:8000) — Docker, K8s, AWS, Git
Dev Agent MCP (:8001) — Code generation in 18 languages

Architecture

┌─────────────────────────────────────────────────────────────────────┐
│  Claude Code / Copilot Chat                                         │
│                                                                     │
│  MCP Stdio Adapters:                                                │
│  ├── devops   → mcp-server      :8000  (DevOps — infra/git)        │
│  ├── dev      → mcp-dev-agent   :8001  (Code gen — 18 languages)   │
│  └── devteam  → dev-team-mcp    :8002  (Dev Team — full pipeline)  │
│                      │                                              │
│            ┌─────────▼─────────┐                                   │
│            │   ORCHESTRATOR    │                                    │
│            └─────────┬─────────┘                                   │
│                      │                                              │
│    ┌─────────────────┼─────────────────┐                           │
│    ▼                 ▼                 ▼                 ▼          │
│ PLANNER           CODER            TESTER           REVIEWER        │
│ Decompose         Generate         Write &          Security +      │
│ ticket into       production       run tests        perf + style    │
│ plan + stack      code files       fix failures     checklist       │
│    │                 │                 │                 │          │
│    └─────────────────┴─────────────────┴─────────────────┘         │
│                                │                                    │
│                      ┌─────────▼─────────┐                         │
│                      │  DELIVERY.md      │                         │
│                      │  git repo + tag   │                         │
│                      └───────────────────┘                         │
└─────────────────────────────────────────────────────────────────────┘

Each agent uses:

LLM (Ollama / OpenAI / Anthropic) for reasoning
Dev Agent :8001 for high-quality code generation and reviews
DevOps MCP :8000 for git operations and deployments

Related MCP server: dev-loop-mcp

Quick Start

1. Install

cd ~/dev-team-mcp
make install

2. Configure

cp .env.example .env
# Edit .env — choose LLM_PROVIDER (default: ollama)

3. Run

# Start this server
make dev        # Dev server on :8002 with auto-reload

# Or start all three MCP servers at once
make start-all

4. Verify

curl http://localhost:8002/health | jq

How to Use

Submit a ticket (async)

curl -X POST http://localhost:8002/ticket \
  -H "Content-Type: application/json" \
  -d '{
    "title": "Add JWT authentication to FastAPI app",
    "description": "Add JWT-based auth with login endpoint, token refresh, and protected routes. Use python-jose.",
    "language": "python",
    "framework": "fastapi",
    "priority": "high",
    "labels": ["feature", "security"]
  }'

Response:

{
  "ticket_id": "a1b2c3d4e5f6",
  "status": "pending",
  "message": "Ticket accepted. Pipeline started: Planner -> Coder -> Tester -> Reviewer. Poll GET /ticket/a1b2c3d4e5f6 for status."
}

Poll for status

curl http://localhost:8002/ticket/a1b2c3d4e5f6 | jq

{
  "ticket_id": "a1b2c3d4e5f6",
  "status": "done",
  "output_path": "/home/user/dev-team-mcp/workspace/add-jwt-auth-a1b2c3d4e5f6",
  "review_score": 88,
  "plan_summary": "Implement JWT authentication..."
}

Get all generated files

curl http://localhost:8002/ticket/a1b2c3d4e5f6/artifacts | jq

Get a specific file

curl http://localhost:8002/ticket/a1b2c3d4e5f6/artifacts/auth/jwt.py | jq .content

Blocking / sync mode (for small tasks)

curl -X POST http://localhost:8002/ticket/sync \
  -H "Content-Type: application/json" \
  -d '{"title": "Hello world script", "description": "Python script that prints hello world and current time"}'

MCP Tools (for Claude Code)

Once registered in ~/.claude/settings.json, you can use these tools directly in Claude:

Tool	Description
`devteam_submit_ticket`	Submit a ticket for autonomous development
`devteam_submit_sync`	Submit and wait (blocking)
`devteam_ticket_status`	Check pipeline progress
`devteam_ticket_logs`	Stream agent log entries
`devteam_list_tickets`	List all tickets
`devteam_list_artifacts`	List generated files
`devteam_get_artifact`	Get a specific file content
`devteam_review_report`	Get code review score + checklist
`devteam_cancel_ticket`	Cancel a ticket
`devteam_health`	Health check

Pipeline Stages

1. Planner Agent

Reads the ticket and produces a structured JSON plan
Chooses the minimal viable tech stack
Breaks work into numbered steps with file targets
Flags security concerns (OWASP Top-10)
Defines test strategy and CI/CD approach

2. Coder Agent

Generates production-ready code for every file in the plan
Uses Dev Agent :8001 when available (falls back to direct LLM)
Applies OWASP mitigations (input validation, parameterised queries, no hardcoded secrets)
Auto-retries with alternative approach on failure

3. Tester Agent

Generates unit + integration tests for every source file
Runs tests via the language's native test runner
On failure: asks LLM to fix the implementation and re-runs (loop)
Tracks coverage percentage

4. Reviewer Agent

Full security audit (uses Dev Agent :8001 + LLM bundle review)
Performance analysis
Documentation completeness check
Production readiness checklist (12 criteria)
Auto-fixes medium/low issues
Approves (score ≥ 75) or rejects with detailed feedback

Delivery

All files written to workspace/<slug>-<ticket_id>/
Git repository initialised with commit history
DELIVERY.md — full handover document
production-ready-<id> git tag applied when approved

Output Structure

workspace/add-jwt-auth-a1b2c3d4e5f6/
├── DELIVERY.md          ← Human-readable handover
├── auth/
│   ├── jwt.py           ← JWT implementation
│   ├── routes.py        ← Protected routes
│   └── test_jwt.py      ← Unit tests
├── main.py              ← FastAPI app
├── requirements.txt
└── .github/
    └── workflows/
        └── ci.yml       ← GitHub Actions CI

Configuration

Env var	Default	Description
`LLM_PROVIDER`	`ollama`	`ollama` / `openai` / `anthropic`
`OLLAMA_MODEL`	`mistral`	Any Ollama model (codestral, llama3.1, etc.)
`OPENAI_API_KEY`	—	Required when `LLM_PROVIDER=openai`
`ANTHROPIC_API_KEY`	—	Required when `LLM_PROVIDER=anthropic`
`DEVOPS_MCP_URL`	`http://localhost:8000`	DevOps MCP server URL
`DEV_AGENT_URL`	`http://localhost:8001`	Dev Agent MCP server URL
`DEV_TEAM_PORT`	`8002`	This server's port
`MAX_RETRIES`	`3`	Max LLM retries per step
`MAX_TEST_FAILURES`	`5`	Max test fix iterations
`WORKSPACE_DIR`	`./workspace`	Where generated repos land

Running Tests

make test

Full 3-Server Stack

# Terminal 1: DevOps MCP (infrastructure)
cd ~/mcp && make dev

# Terminal 2: Dev Agent MCP (code gen)
cd ~/mcp-dev-agent && make dev

# Terminal 3: Dev Team MCP (autonomous pipeline)
cd ~/dev-team-mcp && make dev

Or use the convenience target:

cd ~/dev-team-mcp && make start-all

Generated by Dev Team MCP — autonomous AI software development pipeline

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quality - not tested

maintenance

How are these scores calculated?

Resources

GitHub Repository

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