Convert an image between formats (JPEG ↔ PNG ↔ WebP). No quality loss beyond format defaults. Free tier: 20 calls/day per IP. API key at https://ko-fi.com/yugutou/shop for more calls / larger files.
| Name | Required | Description | Default |
|---|---|---|---|
| format | Yes | Target format. | |
| file_base64 | Yes | The image file contents, base64-encoded. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses 'No quality loss beyond format defaults' and provides rate-limiting details (20 calls/day per IP) and upgrade information, adding useful behavioral context beyond what is obvious.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact, with four short sentences each conveying critical information: purpose, quality, rate limit, and upgrade path. No fluff or redundancy, and the key action is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter tool with no output schema, the description covers purpose, quality, rate limits, and file-size upgrade path. It lacks explicit mention of the output format or response structure, but given the tool's simplicity and the absence of an output schema, this is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters (format enum and base64 input). The description repeats the format list but doesn't add meaning beyond the schema. Baseline 3 is appropriate since the schema already documents both parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Convert an image between formats' with specific formats (JPEG ↔ PNG ↔ WebP), making the verb and resource explicit. This distinguishes it from sibling tools (compress_image, inspect_image, optimize_image) which have different operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: conversion between formats, with free tier limits and an upgrade path. While it doesn't explicitly name alternatives or exclude other tools, the verb 'convert' and format list imply when to use this tool versus compression or optimization.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The tools are mostly distinct: compress_image reduces file size, convert_image changes formats, inspect_image reads metadata, and optimize_image performs lossless optimization. Minor overlap exists because compress_image also supports format conversion, but descriptions clarify the primary intent.
All four tools follow a consistent verb_noun pattern (compress_image, convert_image, inspect_image, optimize_image). The naming is uniform, predictable, and easy to understand.
Four tools is well-scoped for an image compression server. Each tool covers a distinct core operation without unnecessary bloat, and the count feels appropriate for the domain.
The surface covers the primary operations: compress, convert, inspect, and optimize. Minor gaps exist such as missing resizing or batch processing, but the stated purpose (image compression for AI) is well covered with no obvious dead ends.