ai_visionAnalyze an image with Claude: OCR, chart reading, screenshot explanation. ~$0.05.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | What to do with the image | |
| image | Yes | Public URL of the image (PNG/JPEG/WebP/GIF, max 4MB) |
| Name | Required | Description | Default |
|---|---|---|---|
| result | No |
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?
The description adds cost information (~$0.05) and the use of Claude, which provides behavioral context beyond the annotations (readOnlyHint=false, openWorldHint=true). However, it does not disclose potential side effects like data handling or response format details. With annotations present, this is adequate but not comprehensive.
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 sentence that front-loads the purpose, includes examples, and even mentions cost. Every word earns its place; no fluff or redundancy.
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 two parameters and an output schema exists, so return values are presumably covered. The description covers purpose, cost, and example uses. It could mention privacy or image handling, but for a tool of this complexity, the description is largely complete.
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 'q' (What to do with the image) and 'image' (Public URL with format/max size). The description adds example tasks (OCR, chart reading) that hint at the use of 'q', but the schema already carries the parameter definitions. Baseline of 3 is appropriate.
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's function: 'Analyze an image with Claude' followed by specific use cases (OCR, chart reading, screenshot explanation). This provides a specific verb and resource, and differentiates it from sibling tools like ai_image or ai_music by focusing on image analysis.
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 for image analysis tasks but does not explicitly state when to use this tool over alternatives. There is no mention of when not to use it, nor any reference to sibling tools, leaving guidance implied rather than explicit.
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.
Most tools have clearly distinct purposes (image, music, video, vision, voice, etc.), but some overlap exists: ask_ai vs ask_ai_pro differ only in model strength, and web_search vs research_report both involve search with AI responses. Descriptions help clarify, though an agent could misselect in edge cases.
Tool names follow a mostly consistent snake_case pattern, with many using an 'ai_' prefix for generation tasks. However, name styles vary between verb_noun (call_endpoint, remove_bg) and noun_verb (crypto_prices, domain_info), and ask_ai/ask_ai_pro break the ai_ prefix convention. Minor deviations, but the overall pattern is readable.
At 16 tools, the server is slightly above the ideal 3-15 range but remains well-scoped for a multi-purpose utility server. Each tool has a distinct function, and the count feels manageable rather than overwhelming.
The server covers a broad set of capabilities (AI generation, web search, crypto, domain info), but it lacks lifecycle management for generated assets—there are no list/get/delete operations for previously created media, and the domain appears to be a collection of paid endpoints rather than a cohesive service.