ZBS Index What actually exists in applied AI, with the source next to it

mcp server

image-processing

Image processing for AI agents. Resize, convert, compress, and pipeline images.

Description as published by the maintainer. Source

  • version 0.3.0
  • slowing

slowing — Registry entry last updated 2026-05-03.

What this server can do

8 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.

analyze_image(source)
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. ### Responses: **200**: Successful Response (Success Response) Content-Type: application/json **Example Response:** ```json { "size_bytes": 1 } ``` Required: source.
compress_image(q, strip, format, source, autorot, delivery, quality_target)
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 reported in X-Pictomancer-Quality-* headers). If the output is not smaller than the input, the request is free (X-Pig-Billed: 0) and does not consume free-tier quota. ### Responses: **200**: Processed image binary (Success Response) Content-Type: application/json Content-Type: image/jpeg **Example Response:** ```json "string" ``` Content-Type: image/png **Example Response:** ```json "string" ``` Content-Type: image/webp **Example Response:** ```json "string" ``` Required: source.
convert_image(q, strip, effort, format, source, autorot, delivery, lossless, quality_target)
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 the target, searched on the worker (jpeg, webp, avif; flat surcharge; outcome reported in X-Pictomancer-Quality-* headers). ### Responses: **200**: Processed image binary (Success Response) Content-Type: application/json Content-Type: image/jpeg **Example Response:** ```json "string" ``` Content-Type: image/png **Example Response:** ```json "string" ``` Content-Type: image/webp **Example Response:** ```json "string" ``` Required: source, format.
crop_image(x, y, trim, width, format, height, source, autorot, gravity, delivery, threshold)
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, clamped to the source if the target is larger. Trim: set 'trim: true' (optional 'threshold') to remove a uniform background border via content detection; the applied rect is reported in X-Pictomancer-Trim-* headers. ### Responses: **200**: Processed image binary (Success Response) Content-Type: application/json Content-Type: image/jpeg **Example Response:** ```json "string" ``` Content-Type: image/png **Example Response:** ```json "string" ``` Content-Type: image/webp **Example Response:** ```json "string" ``` Required: source.
get_format_info
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(source, delivery, operations)
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) Content-Type: application/json Content-Type: image/jpeg **Example Response:** ```json "string" ``` Content-Type: image/png **Example Response:** ```json "string" ``` Content-Type: image/webp **Example Response:** ```json "string" ``` Required: source, operations.
optimize_for_vision(q, format, source, delivery, max_tokens, target_model)
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 tokens. Pass max_tokens to trade resolution for tokens: that lever is continuous on Claude, unavailable on OpenAI (cost follows the aspect ratio alone), and on Gemini reaches only a flat 258. An image already within budget is returned untouched and free (X-Pig-Billed: 0). ### Responses: **200**: Processed image binary (Success Response) Content-Type: application/json Content-Type: image/jpeg **Example Response:** ```json "string" ``` Content-Type: image/png **Example Response:** ```json "string" ``` Content-Type: image/webp **Example Response:** ```json "string" ``` Required: source, target_model.
resize_image(scale, width, format, height, source, autorot, gravity, scale_x, scale_y, delivery)
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 (optional 'gravity', default attention). The two modes are mutually exclusive. ### Responses: **200**: Processed image binary (Success Response) Content-Type: application/json Content-Type: image/jpeg **Example Response:** ```json "string" ``` Content-Type: image/png **Example Response:** ```json "string" ``` Content-Type: image/webp **Example Response:** ```json "string" ``` Required: source.

Last successful function declaration observed on . Source: https://api.pictomancer.ai/mcp. We list what the server declared; we do not call any of these functions.

Endpoint status observed on . Source: https://api.pictomancer.ai/mcp.

Signals

These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.

Signal Value What it measures Window Observed Source
Latest published version 0.3.0 Latest version string the maintainer published to the registry. as of fetch Model Context Protocol
Registry record last updated 2026-05-03 When the registry record was last updated by its maintainer. point in time Model Context Protocol
First listed in the MCP Registry 2026-05-03 Date this server was first published to the official MCP Registry. Not a usage or quality measure. point in time Model Context Protocol
mcp tools declared 8 tools Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. as of probe api.pictomancer.ai
mcp endpoint status ok The server listed 8 functions when asked. as of probe api.pictomancer.ai

Where to get it

This record as data

Every field on this page, with its source and observation date, is in the catalog JSON. Fetch the whole kind at once instead of parsing this HTML.

GET /api/v1/entries/mcp_server.json

Sources

  1. Tools declared by the MCP server at https://api.pictomancer.ai/mcp — api.pictomancer.ai, observed , trust tier 1.
  2. Official MCP Registry — Model Context Protocol, observed , trust tier 1.