Replicate MCP Server
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Related Servers
- AlicenseNot gradedqualityCmaintenanceEnables LLM applications to generate, edit, describe, upscale, remix, reframe, and replace backgrounds in images using the Ideogram AI API.9 npmMIT
- AlicenseBqualityDmaintenanceEnables users to generate images from text prompts using Replicate's model, with configurable parameters and full MCP protocol compliance.1120ISC
- FlicenseAqualityBmaintenanceEnables LLM chat UIs that lack native image generation to create images inline through a Venice AI API endpoint.1-
- AlicenseNot gradedqualityDmaintenanceEnables AI image generation and editing using Replicate's official models like Flux, SDXL, and Seedream, with tools to search models and generate images.18,274 npm6Apache 2.0
- AlicenseNot gradedqualityFmaintenanceEnables AI agents to generate, edit, and analyze images using Google's Gemini image generation models including Nano Banana Pro (gemini-3-pro-image-preview).124 npm18MIT
- AlicenseNot gradedqualityBmaintenanceEnables coding agents and other MCP clients to generate images from text prompts and edit, restyle, or extend existing images through an OpenAI-compatible /v1/images API. Results are returned both inline as image content blocks and as files saved to disk, so agents can view and reuse them within a normal task.25 npm1MIT
TDQS
Scored across 4 tools
generate_image and generate_image_batch are clearly distinct by cardinality, and each has a unique input type. However, edit_image and generate_from_reference_image overlap: both take an image and text prompt and return a modified/new image, which could cause misselection.
All tool names use snake_case with a clear verb_noun or verb_preposition_noun structure. The generate_ prefix is consistent across three tools, and edit_image is a natural semantic variation.
Four tools is well within the ideal 3-15 range for a focused image generation service. It covers key workflows without redundancy, though it could arguably support more operations.
The set covers single generation, batch generation, reference-based generation, and editing, which are core for image generation. Missing operations like listing available models or retrieving generation status are minor gaps an agent could work around.