Siray Image MCP
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Alternatives to Siray Image MCP
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AlicenseBqualityBmaintenanceEnables MCP-compatible agents to discover models, generate images and videos, use Prompt Studio, upload references, poll tasks, and check balances via the Callirra API.13108 npm1MIT- AlicenseAqualityAmaintenanceEnables AI image and video generation using Midjourney through the AceDataCloud API. It supports comprehensive features including image creation, transformation, blending, editing, and video generation directly within MCP-compatible clients.16219 PyPI10MIT
- AlicenseNot gradedqualityDmaintenanceEnables AI agents to generate professional storyboards and videos from scripts or creative descriptions via MCP-compatible clients.21 npmMIT
- FlicenseNot gradedqualityBmaintenanceProvides an MCP interface to a unified image generation gateway, enabling AI agents to generate images through multiple free and low-cost providers, list sessions and history, and manage provider configurations.-
- AlicenseNot gradedqualityCmaintenanceEnables coding agents to generate AI images directly through MCP tools, handling payment via the x402 protocol with USDC on Base and no API keys.MIT
- AlicenseAqualityBmaintenanceImage tools for coding agents: generate from text, edit one reference image, remove backgrounds, upscale 2x, and create reference-based sprite animations. The MCP server, CLI and REST API share one key, credit balance and request history. Needs a DreamLayer API key and paid credits; sprite sheets are beta.7482 npmMIT
TDQS
Scored across 3 tools
The two generation tools are clearly separated by purpose: generate_image handles general image creation/editing, while generate_backdrop is a specialized, measurement-driven operation for background plates. list_models is entirely distinct, so an agent can confidently select the right tool by name and description.
All tool names follow the same verb_noun snake_case pattern: generate_image, generate_backdrop, and list_models. There are no mixed conventions, vague single-word names, or inconsistent prefixes.
Three tools is well-scoped for a narrow image-generation MCP: one general generation tool, one specialized backdrop tool, and one model discovery tool. Each tool earns its place, and the count is within the ideal range.
The server covers the core image-generation lifecycle: discovering available models, generating images from prompts, supporting image-to-image/editing via the image argument, and providing a dedicated backdrop workflow with built-in validation. There are no obvious dead ends or missing critical operations for an image generation domain.