MCP server

Image Processing

ai.pictomancer/image-processing

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Image processing for AI agents: resize, convert, compress, crop, and web-ready AI-generated images.

Source

At a glance

Type

MCP server

Publisher

pictomancer.ai (verified by the MCP Registry)

Status

Working at last check

Tools

11

Token cost

About 12,438 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http image-processing https://api.pictomancer.ai/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.image-processing]
url = "https://api.pictomancer.ai/mcp"

Gemini CLI

{"mcpServers":{"image-processing":{"httpUrl":"https://api.pictomancer.ai/mcp"}}}

Cursor

{"mcpServers":{"image-processing":{"url":"https://api.pictomancer.ai/mcp"}}}

VS Code

{"servers":{"image-processing":{"type":"http","url":"https://api.pictomancer.ai/mcp"}}}

Antigravity

agy mcp add image-processing https://api.pictomancer.ai/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"image-processing","server_url":"https://api.pictomancer.ai/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"image-processing","server_url":"https://api.pictomancer.ai/mcp"}

Tools

• analyze_image ~262 tokens
  Analyze an image Fetch an image from a URL or base64 and return its metadata: size in bytes, pixel dimensions, source format, and what it costs every supported vision model in tokens. Always free. Dimensions are omitted if the image header cannot be read. Also reports whether the input carries a C2
• compress_image ~1530 tokens
  Compress an image Re-encode an image with quality/format options to reduce file size. Supports jpeg, png, webp, tiff, gif. Instead of a q number you can set quality_target (0-1]: the smallest file with SSIM at or above the target, searched on the worker (jpeg, webp, avif; flat surcharge; outcome re
• convert_image ~1565 tokens
  Convert image format Convert an image to a different format (jpeg, png, webp, tiff, gif, avif). Optionally set quality, strip metadata, enable lossless mode (webp, avif), or tune encoder effort (avif). Instead of a q number you can set quality_target (0-1]: the smallest file with SSIM at or above t
• crop_image ~1726 tokens
  Crop an image Extract a rectangular region from an image, in one of three mutually exclusive modes. Manual: give the top-left corner (x, y) and dimensions (width, height) in pixels. Smart crop: give 'gravity' (attention, entropy, centre) plus width and height; the window is picked automatically, cl
• estimate_cost ~655 tokens
  Estimate the price of an operation Returns the exact USD price this API would charge for an operation on an input of the given size, without fetching or processing anything. Free. Use it before paying: send X-Max-Cost-USD on the real request to have the API refuse (412) instead of charging more tha
• get_format_info ~94 tokens
  Get supported formats and options Returns supported output formats and their configurable options. ### Responses: **200**: Successful Response (Success Response) Content-Type: application/json
• image_pipeline ~1100 tokens
  Run a multi-step image pipeline Chain multiple operations (resize, compress, convert, crop) in sequence. The image is fetched once, then each operation is applied to the output of the previous one. Max 10 operations per pipeline. ### Responses: **200**: Processed image binary (Success Response) C
• optimize_for_vision ~1355 tokens
  Resize an image for a vision model Resize an image to the largest size a given vision model still benefits from, and report what it costs that model in tokens before and after. Every provider downscales oversized input before counting tokens, so this alone saves bytes and upload latency rather than
• optimize_generated_image ~1419 tokens
  Optimize an AI-generated image for the web The step after image generation. gpt-image, DALL-E, Flux, Midjourney and Stable Diffusion hand back 2-8 MB PNGs; this returns the same picture as a web-ready webp (default), avif, jpeg or png, metadata stripped, transparency kept on webp/avif/png. Optional
• remove_image_background ~1066 tokens
  Remove the background from an image Cut the subject (person, product, animal, object) out of a photo and return it on a transparent background as png (default), webp or avif. Optional q (1-100) for webp/avif. Runs a segmentation model (U2Net) on CPU: expect a few seconds per image, not milliseconds
• resize_image ~1666 tokens
  Resize an image Scale an image by a factor, or fill an exact box. Use 'scale' for uniform scaling, or 'scale_x'/'scale_y' for independent axes (float factors, e.g. 0.5 = half size). Alternatively set 'width'+'height' for fill mode: resize and smart-crop to those exact dimensions in one call (option

Browse

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