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vision-router-mcp

vision-router-mcp

by wotonger
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

vision_list_models

List all vision model configurations and their enabled/API key status to identify available options for image analysis without exposing API keys.

Instructions

列出所有视觉模型配置及其启用和 API Key 配置状态。不会返回 API Key。系统没有默认视觉模型，调用 vision_analyze 时必须明确指定 provider_id。

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "vision_list_models"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It explicitly states that API keys are not returned and that there is no default vision model, which are important behavioral traits. It does not disclose auth requirements or response format, but for a simple listing tool these are less critical.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, two sentences long, with the main purpose front-loaded. Every sentence adds value: the first states what is listed, the second adds a security-relevant caveat and usage context.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter listing tool, the description provides sufficient context: it names the output scope, notes that API keys are not returned, and gives usage guidance about provider_id. It could mention the structure of the list, but this is a minor gap given the simple nature of the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, so the baseline is 4. The description adds no parameter-specific information, but none is needed. The schema coverage is effectively 100% since there are no params to document.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses the specific verb '列出' (list) and names the resource '所有视觉模型配置' (all vision model configurations), making it clear what the tool does. It also distinguishes from sibling tools by mentioning vision_analyze's requirement for an explicit provider_id, implying this tool is used to discover available models.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description indirectly guides usage by stating that vision_analyze has no default model and requires explicit provider_id, suggesting this tool is useful for finding that ID. However, it does not explicitly say 'use this tool when you need to list models' or contrast with vision_get_setup_guide or vision_test_connection, so it's clear but not fully explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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