Analyze an image from a file path, URL, or data URL by asking a question. Get a clear answer from a vision model to understand the image content.
Analyze an image using a vision model
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | ||
| question | No | Question about the image | |
| image_path | Yes | File path, URL, or data URL |
Changes observed during successful MCP inspections.
v1.8.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds that it uses a vision model, but does not disclose performance characteristics, required permissions, or output format. It provides minimal behavioral context beyond annotations.
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. Every word contributes to clarity, making it highly efficient.
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?
Given no output schema, the description should hint at the return type (e.g., text, structured data). It also omits limitations like image size or supported formats. The tool is simple, but the description is too sparse for complete understanding.
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 description coverage is 67% (2 of 3 parameters have descriptions). The tool description adds no additional meaning beyond the schema, so it meets the baseline expectation without improvement.
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 'Analyze an image using a vision model' clearly specifies a verb (analyze) and resource (image), and distinguishes it from sibling tools like analyze_audio and analyze_video.
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 such as generate_image or when not to use it. The description lacks any usage context or conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.