Lossless image optimization. PNG uses oxipng (best-in-class PNG optimiser), JPEG re-encodes at high quality. Free tier: 20 calls/day per IP.
| Name | Required | Description | Default |
|---|---|---|---|
| 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?
With no annotations provided, the description carries the burden of behavioral disclosure. It discloses that the optimization is lossless, uses oxipng for PNG, re-encodes JPEG at high quality, and has a free tier limit. However, it does not describe the response format, file size limits, or error behavior, leaving some behavioral aspects ambiguous.
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 two sentences: the first states purpose and processing details, the second discloses the rate limit. Every sentence earns its place, with no redundancy or fluff. It is appropriately concise and 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?
The tool is simple with one parameter, and the description covers purpose and processing details. However, since there is no output schema, the description should explain what the tool returns (e.g., optimized base64 string). It also lacks mention of input constraints like file size limits, leaving the description incomplete for a full understanding of the expected input and output behavior.
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?
The input schema fully describes the single parameter (file_base64) with 100% coverage, so the baseline is 3. The description adds no additional parameter semantics, such as accepted image formats or maximum file size. Since schema coverage is complete, this is acceptable but not enhanced.
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 the tool performs lossless image optimization, and specifies processing methods for PNG (oxipng) and JPEG re-encoding. It does not explicitly reference sibling tools to distinguish itself, but the specific focus on losslessness and optimizer names makes the purpose clear.
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 no guidance on when to use this tool versus alternatives like compress_image or convert_image. It only mentions the free tier rate limit, which is a constraint rather than a usage recommendation. No exclusion criteria or alternative tool references are provided.
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.