Which integrations are available for this server?

Integrates with Google Cloud Vertex AI for accessing AI models (DeepSeek, Kimi, MiniMax) in a multi-candidate agentic coding pipeline with planning, code generation, and quality gating.

How do I use Petamind 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., "@Petamind MCP generate a React component for a user profile card with dark mode support" 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.

Petamind MCP

A Claude Code MCP server for a multi-candidate agentic coding loop: reasoner plan → generate patches → deterministic gates → mandatory vision scoring → pick the best winner.

Poetiq-style refinement loop (descriptive, not affiliated): This project uses “Poetiq-style” descriptively to refer to iterative refinement loops (generate → critique → refine → verify). It is not affiliated with Poetiq.

Setup guide: docs/MCP_PETAMIND_MCP.md. Vertex setup: docs/VERTEX_SETUP.md. Troubleshooting: docs/TROUBLESHOOTING.md.

MCP Quick Start (Claude Code)

Option A (recommended): install from PyPI via `pipx`

pipx install petamind-mcp petamind-setup

Then add the MCP server to Claude Code (user scope):

claude mcp add-json --scope user petamind-mcp '{"command":"petamind-mcp","args":[]}'

Notes:

petamind-setup installs Playwright Chromium (required for the mandatory vision loop).
You do not need Google Cloud credentials to use petamind_eval_patch with vision_provider=client (default).

Option B: install from a git clone (contributors / hacking)

From this repo root:

./scripts/setup.sh

Then follow docs/MCP_PETAMIND_MCP.md to add the server to Claude Code via .mcp.json or claude mcp add-json.

Minimal Claude Code config (user scope)

claude mcp add-json --scope user petamind-mcp '{ "command": "'"$(pwd)"'/.venv/bin/python", "args": ["-m", "petamind_mcp.mcp_server"] }'

Included: Synthetic UI Dataset Factory

This repo also includes a production-grade synthetic dataset generator for UI/UX design tasks (landing pages, directories, dashboards) using Next.js App Router + TypeScript + Tailwind.

Features

Multi-model pipeline: Uses Vertex AI (DeepSeek, Kimi, MiniMax) and OpenRouter (Devstral, vision models)
Quality gating: Only winners pass through to training data (build success + vision score threshold)
Resumable: SQLite caching for model responses, task state persistence
Two output tracks: public/ (publishable models only) and private/ (all models)
No contamination: Chain-of-thought/thinking never stored; only structured specs + code

Claude Code MCP (agentic coding)

This repo also ships an MCP server (petamind-mcp) that exposes a multi-candidate patch/test/vision loop to Claude Code. Setup guide: docs/MCP_PETAMIND_MCP.md.

Quick Start

1. Environment Setup

# Create virtual environment python3 -m venv .venv source .venv/bin/activate # Install dependencies pip install -e . # Or with uv (faster) uv pip install -e . # Install Playwright browsers playwright install chromium

2. Configure Environment Variables

cp .env.example .env # Edit .env with your credentials

Required:

GOOGLE_CLOUD_PROJECT: Your GCP project ID
GOOGLE_CLOUD_REGION: Region for Vertex AI (e.g., us-central1)
OPENROUTER_API_KEY: Your OpenRouter API key

Optional:

GCS_BUCKET: For cloud backup of outputs

3. Authenticate with Google Cloud

gcloud auth application-default login

4. Run

# Smoke test (3 tasks end-to-end) make smoke # Full run (public models only) make run_public # Full run (all models including private) make run_private # Resume a previous run titan-factory run --resume <run_id> # Export training data make export RUN_ID=<run_id>

Configuration

Edit config/config.yaml to customize:

models: planner: provider: vertex model: deepseek-ai/deepseek-v3.2-maas publishable: true ui_generators: - provider: vertex model: moonshotai/kimi-k2-thinking-maas publishable: true variants: 2 - provider: vertex model: minimaxai/minimax-m2-maas publishable: true variants: 2 patcher: provider: openrouter model: mistralai/devstral-2512:free publishable: true vision_judge: provider: openrouter model: null # Falls back to heuristic scorer publishable: false pipeline: vision_score_threshold: 8.0 max_fix_rounds: 2 polish_loop_enabled: true tasks_per_niche: 7 budget: concurrency_vertex: 5 concurrency_openrouter: 10 requests_per_min_vertex: 60 requests_per_min_openrouter: 100 max_total_tasks: null # Run all stop_after_usd: null # No limit export: holdout_niches: 12 validation_split: 0.08

Pipeline Stages

Niche/Task Generation: Creates 100 niches × 7 tasks = 700+ tasks
Planning: DeepSeek generates UI_SPEC JSON for each task
UI Generation: Kimi + MiniMax generate code candidates (2 variants each)
Validation: Next.js build with Devstral-powered fix loops
Rendering: Playwright captures screenshots at 3 viewport sizes
Scoring: Vision judge (or heuristic fallback) scores candidates
Selection: Best candidate per task selected for training
Export: Winners exported to train.jsonl / valid.jsonl

Output Structure

out/<run_id>/ ├── cache.db # SQLite response cache ├── manifest.db # Task state tracking ├── prompts/ │ ├── niches.json │ └── tasks.jsonl ├── renders/ │ └── <task_id>/ │ └── <candidate_id>/ │ ├── 375x812.png │ ├── 768x1024.png │ └── 1440x900.png ├── rich_records.jsonl # All candidates (for audit) ├── selected_records.jsonl # Winners only ├── public/ │ ├── train.jsonl │ └── valid.jsonl └── private/ ├── train.jsonl └── valid.jsonl

Training Data Format

Each line in train.jsonl:

{ "messages": [ {"role": "system", "content": "You are Titan 4 Design..."}, {"role": "user", "content": "<task prompt>"}, {"role": "assistant", "content": "{\"ui_spec\": ..., \"files\": [...]}"} ] }

Page Types Covered

landing: Marketing landing pages
directory_home: Directory homepage with search
city_index: City-specific listing pages
category_index: Category-specific listing pages
listing_profile: Individual listing detail pages
admin_dashboard: Admin/analytics dashboards
edit: Refactor/edit tasks (20% of dataset)

Development

# Run tests pytest tests/ # Type checking mypy src/ # Format ruff format src/ tests/ ruff check src/ tests/

Architecture Notes

Provider abstraction: Clean interface for Vertex AI and OpenRouter
Deterministic IDs: Tasks have stable IDs from hash(niche_id + page_type + seed)
JSON strictness: Safe extraction with fallback parsing
Async throughout: Uses asyncio for concurrent model calls
No thinking storage: Only structured UI_SPEC and final code stored

License

MIT

Petamind MCP

Petamind MCP

MCP Quick Start (Claude Code)

Option A (recommended): install from PyPI via `pipx`

Option B: install from a git clone (contributors / hacking)

Minimal Claude Code config (user scope)

Included: Synthetic UI Dataset Factory

Features

Claude Code MCP (agentic coding)

Quick Start

1. Environment Setup

2. Configure Environment Variables

3. Authenticate with Google Cloud

4. Run

Configuration

Pipeline Stages

Output Structure

Training Data Format

Page Types Covered

Development

Architecture Notes

License

Resources

Appeared in Searches

New MCP Servers

Latest Blog Posts

MCP directory API

Petamind MCP

MCP Quick Start (Claude Code)

Option A (recommended): install from PyPI via pipx

Option B: install from a git clone (contributors / hacking)

Minimal Claude Code config (user scope)

Included: Synthetic UI Dataset Factory

Features

Claude Code MCP (agentic coding)

Quick Start

1. Environment Setup

2. Configure Environment Variables

3. Authenticate with Google Cloud

4. Run

Configuration

Pipeline Stages

Output Structure

Training Data Format

Page Types Covered

Development

Architecture Notes

License

Resources

Appeared in Searches

New MCP Servers

Latest Blog Posts

MCP directory API

Option A (recommended): install from PyPI via `pipx`