Process an image: resize, convert (png/jpeg/webp), compress, or square thumbnail. Input and output are base64.
| Name | Required | Description | Default |
|---|---|---|---|
| size | No | ||
| width | No | ||
| format | No | ||
| height | No | ||
| product | Yes | ||
| quality | No | ||
| image_b64 | Yes | Source image, base64 |
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, the description carries the full behavioral burden; it usefully discloses that input and output are base64 and lists the transformations. However, it does not mention side effects, output format details, defaults, or limitations, so some behavioral uncertainty remains.
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, tightly written sentence that front-loads the core action and then presents a clear list of operations. Every phrase carries useful information, and there is no redundant or filler text.
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 has 7 parameters and no output schema, but the description only covers the general operation and base64 exchange. It omits parameter combination rules, size semantics, quality defaults, and the exact form of the output base64, leaving too much to inference.
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 description adds meaning by mapping the 'product' enum to human-readable operations and mentioning the convert formats. But with only 14% schema coverage, parameters like size, width, height, and quality remain unexplained in both the schema and the description, which is inadequate for a 7-parameter tool.
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 states a specific action ('Process an image') and enumerates the exact supported operations: resize, convert with formats, compress, and square thumbnail. This clearly distinguishes the tool from sibling tools like analyze_text, fetch_url, and generate_qr_code.
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 implies usage when image manipulation is needed, but it does not explicitly state when to prefer this tool over alternatives or provide any exclusion conditions. Sibling names make the domain distinction obvious, but the description itself offers no direct guidance.
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.
Each tool targets a different input type and operation: text analysis, URL fetching, QR generation, and image processing. There is no meaningful overlap, so an agent can confidently choose the right tool.
All tools follow the same snake_case verb_noun pattern: analyze_text, fetch_url, generate_qr_code, process_image. The naming is predictable and consistent across the server.
Four tools is a small but reasonable set for a general-purpose utility server. Each tool covers a distinct real-world use case, though the collection feels slightly minimal.
The individual tools are self-contained and have no obvious dead ends for their described operations. However, the tools are unrelated and the overall scope is unclear, so it is hard to assess whether the set is complete; common additions like PDF handling or OCR are noticeably absent.