Borrowing it
Nothing to install: this file belongs to krystian-ai/ai-image-gen-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/krystian-ai/ai-image-gen-mcp/main/CLAUDE.mdgit clone --depth 1 https://github.com/krystian-ai/ai-image-gen-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/krystian-ai/ai-image-gen-mcp/claude-md)<a href="https://agentmods.dev/instructions/krystian-ai/ai-image-gen-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/krystian-ai/ai-image-gen-mcp/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/krystian-ai/ai-image-gen-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/krystian-ai/ai-image-gen-mcp/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.01005 | $0.01005 |
| Opus 5 | $0.00502 | $0.00502 |
| Sonnet 5 | $0.00201 | $0.00201 |
| Haiku 4.5 | $0.00101 | $0.00101 |
Grade A, and why
ai-image-gen-mcp CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
This is an MCP (Model Context Protocol) server for AI image generation. The project is currently in its initial planning phase with comprehensive documentation but no implementation yet.
Development Commands
Setup
# Create virtual environment
python -m venv .venv && source .venv/bin/activate
# Install dependencies
pip install -e .[image,dev]
# Copy environment variables
cp .env.example .env
# Edit .env and add your OpenAI API key
Running the Server
# Via MCP CLI
mcp-imageserve stdio
# Direct Python execution
python -m ai_image_gen_mcp.server --transport=stdio
Code Quality
The project will use pre-commit hooks with:
- black (code formatting)
- ruff (linting)
- mypy (type checking)
Architecture
The MCP server follows a modular architecture:
- MCP Server Layer: Handles JSON-RPC 2.0 protocol, validates requests, manages authentication
- Model Router: Strategy pattern for switching between AI models (GPT-Image-1, DALL·E, Stable Diffusion)
- Object Storage: Stores generated images and provides signed URLs
- Async Queue: Celery/Kafka for GPU task management
- Post-Processing Pipeline: Image enhancement and watermarking
Key MCP Primitives
- Tools:
generate_image,upscale_image,inpaint_image - Resources: Generated assets, prompt logs, experiment metadata
- Prompts: Reusable templates for image generation
Important Notes
- Python Version: 3.11+ required
- MVP Model: GPT-Image-1 (OpenAI) - requires OPENAI_API_KEY
- Configuration: Uses
.envfile for API keys and settings - Target MVP Date: September 30, 2025
Current Status
The project now has a complete MVP implementation:
- MCP Server: Implemented with FastMCP, exposes
generate_imagetool and model resources - Multiple Models:
- DALL-E 3 (default) - High quality, supports sizes and styles
- DALL-E 2 - Previous generation, supports multiple images
- GPT-Image-1 - Uses Responses API (slower, timeout issues)
- Local Storage: Saves generated images with metadata to configurable cache directory
- Configuration: Environment-based configuration via .env file
- Testing: Comprehensive test suite with 100% test pass rate
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 137 lines · 1,005 tokens per session scan A 79b198bd8823
ai-image-gen-mcp CLAUDE.md is an instructions file published in the GitHub repository krystian-ai/ai-image-gen-mcp (3 stars, last pushed 1y ago), licensed MIT. It adds 1,005 tokens to every session, about $0.0050 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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