format-converterConvert between 11 image formats including HEIC, WebP, PNG, JPG, AVIF.
| Name | Required | Description | Default |
|---|---|---|---|
| image | Yes | Base64-encoded image to convert | |
| quality | No | Output quality (1-100, for lossy formats) | |
| outputFormat | No | Target output format | png |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / $schemaAdded value: +"http://json-schema.org/draft-07/schema#"Input schema / properties / imageAdded value: +{
+ "description": "Base64-encoded image to convert",
+ "type": "string"
+}Input schema / properties / outputFormatAdded value: +{
+ "default": "png",
+ "description": "Target output format",
+ "enum": [
+ "png",
+ "jpeg",
+ "webp",
+ "avif",
+ "gif",
+ "tiff"
+ ],
+ "type": "string"
+}Input schema / properties / qualityAdded value: +{
+ "default": 80,
+ "description": "Output quality (1-100, for lossy formats)",
+ "maximum": 100,
+ "minimum": 1,
+ "type": "number"
+}Input schema / requiredAdded value: +[
+ "image"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only states conversion and lists formats, omitting details about output format constraints, quality semantics, input limitations, or failure modes. An agent cannot anticipate side effects or error conditions.
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 a single, front-loaded sentence with no redundancy. The claim of 11 formats is unsupported by the schema and potentially misleading, which keeps it from a perfect score.
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 conversion tool with no output schema and no annotations, the description lacks critical context about return values, input size limits, format compatibility, or error handling. The schema covers parameter syntax only, not runtime 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?
Schema coverage is 100%, so the baseline is 3. The description adds no parameter meaning beyond the schema; the '11 formats' mention could even confuse expectations about the outputFormat enum, which contains only 6 options.
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 uses a clear verb ('Convert') and identifies the resource (image formats), distinguishing it from sibling image tools like resizers or compressors. However, it claims support for 11 formats while the schema only lists 6 output formats, creating ambiguity about actual capabilities.
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?
No guidance is provided on when to use this tool versus alternatives. Sibling tools such as image-compressor, image-resizer, and pdf-to-image exist, but the description gives no differentiating context or exclusion criteria.
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.
Multiple tools have overlapping purposes, such as bg-remover, bg-remover-pro, pro-matting, and takumi all performing background removal, and upscaler/upscaler-pro being redundant. With 202 tools, an agent may easily select the wrong one despite detailed descriptions.
Most tools use a consistent kebab-case format with descriptive names like pdf-compress, image-resizer, and tax-return-calc. Exceptions like 'takumi', 'pro-matting', and '-pro' suffixes (bg-remover-pro, upscaler-pro) are minor deviations relative to the total.
202 tools is an extreme mismatch for an MCP server, far exceeding the typical 3-15 well-scoped range. The sheer volume makes it unwieldy for an agent to efficiently navigate and select the right tool.
The tool set provides extensive coverage across many domains, including PDF operations (20+ tools), image editing, financial calculations, e-commerce fee estimation, and YouTube utilities. Minor gaps exist in cross-tool integration, but the breadth is highly comprehensive for the apparent purpose.