vision_get_server_status
Retrieve server status to verify cloud vision model configuration and view current model and settings.
Instructions
查询服务状态:云端视觉模型是否已配置、当前模型与配置项。
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Retrieve server status to verify cloud vision model configuration and view current model and settings.
查询服务状态:云端视觉模型是否已配置、当前模型与配置项。
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It indicates a read-only query operation, but lacks details on network calls, caching, or potential impacts. Adequate for a simple check, but not rich.
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?
Single sentence with clear, front-loaded information. No wasted words, every part adds value.
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 tool with no parameters and no output schema, the description covers the basic purpose and what it returns. Could specify output format, but it's fairly complete for a simple query.
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?
No parameters exist, so baseline is 4. Description doesn't add parameter info but is not necessary. Schema coverage is 100% (empty) so no deduction.
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?
Description clearly states it queries server status, including whether the cloud vision model is configured, current model, and configuration items. This differentiates from sibling tools like vision_analyze_image which are for content 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?
Description implies usage for checking service status, but does not explicitly state when to use versus alternatives or provide exclusions. Sibling tool names suggest different purposes, but no direct guidance is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/ganyu123456/mcp-multivision-server'
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