Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/aigentive/openai-image-mcp/claude-mdgit clone --depth 1 https://github.com/aigentive/openai-image-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/aigentive/openai-image-mcp/claude-md)<a href="https://agentmods.dev/instructions/aigentive/openai-image-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/aigentive/openai-image-mcp/claude-md.svg" alt="Measured on agentmods" 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 | $0.00879 | $0.00879 |
| Opus 5 | $0.00439 | $0.00439 |
| Sonnet 5 | $0.00176 | $0.00176 |
| Haiku 4.5 | $0.00088 | $0.00088 |
Grade A, and why
openai-image-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 5d 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 — 105 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.
Development Commands
Environment Setup
# Install dependencies
poetry install
# Run tests
poetry run pytest
# Run specific test file
poetry run pytest tests/test_server.py
# Run with coverage
poetry run pytest --cov=src/openai_image_mcp
# Code formatting
poetry run black .
poetry run isort .
Package Operations
# Build package
poetry build
# Publish to PyPI
poetry publish
# Version bump
poetry version patch # or minor/major
MCP Server Testing
# Run server locally
poetry run python -m openai_image_mcp.server
# Test with Claude Code
claude --mcp-config mcp-config.private.json --mcp-debug
Core Architecture
Session-Based Conversational Design
The system is built around multi-turn conversations with persistent memory, not traditional stateless API calls. Key architectural patterns:
- Session Manager: Thread-safe session lifecycle with O(1) UUID lookup
- Conversation Builder: Automatic context trimming at 50 turns, preserves image references
- Responses API Client: Uses OpenAI Responses API (requires
openai>=1.82.0) with exponential backoff retry logic - File Organizer: Structured storage with comprehensive metadata preservation
Component Interaction Flow
- MCP Tool Call →
server.py(13 available tools) - Session Creation →
session_manager.py(UUID-based, thread-safe) - Context Building →
conversation_builder.py(multi-turn history) - API Call →
responses_client.py(OpenAI Responses API with tools) - Image Processing →
image_processor.py(download, save, metadata) - File Organization →
file_organizer.py(structured directories, metadata JSON)
MCP Tools Organization
Session Management (5 tools): create_image_session, generate_image_in_session, get_session_status, list_active_sessions, close_session
Image Generation (5 tools): generate_image, edit_image, generate_product_image, generate_ui_asset, analyze_and_improve_image
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.
- 5d ago First seen · 105 lines · 879 tokens per session scan A fd4051397db9
openai-image-mcp CLAUDE.md is an instructions file published in the GitHub repository aigentive/openai-image-mcp (1 stars, last pushed 1y ago), licensed MIT. It adds 879 tokens to every session, about $0.0044 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.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).