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Glama
LZMW

MCP Vision Server

by LZMW
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
VISION_MODELYes模型名称glm-4v
VISION_API_KEYYesAPI 密钥
VISION_TIMEOUTNo请求超时（秒）120
VISION_BASE_URLYesAPI 基础 URL
VISION_LOG_LEVELNo日志级别INFO
VISION_MAX_TOKENSNo最大输出 tokens4096
VISION_MAX_HISTORYNo对话历史最大保存数50
VISION_TEMPERATURENo温度参数0.7
VISION_HISTORY_PATHNo历史文件路径~/.mcp-vision/history.json
VISION_MAX_IMAGE_SIZENo最大图像大小（字节）20971520
VISION_ENABLE_PERSISTENCENo启用持久化true

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": true
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
analyze_imageA

分析图像内容

这是核心工具，用于分析图像并返回详细描述。

使用场景：

  • 图像内容识别与描述

  • 文字提取（OCR）

  • 代码截图识别

  • 数据图表分析

  • 技术图表理解

参数说明：

  • image: 支持本地文件路径（如 C:/path/to/image.png）或Base64编码

  • prompt: 分析指令，告诉AI你想了解图像的什么内容

示例：

# 基础图像描述
analyze_image(image="C:/screenshots/desktop.png", prompt="描述这张截图的内容")

# OCR文字提取
analyze_image(image="C:/docs/scan.png", prompt="提取图片中的所有文字")

# 代码识别
analyze_image(image="C:/code/snippet.png", prompt="识别并转录图片中的代码")

返回内容：

  • status: 执行状态（"success"或"error"）

  • result: 分析结果

  • image_info: 图像信息（类型、大小等）

chat_visionA

两轮对话式图像问答

支持基于图像的两轮对话：

  • 第一轮：根据图像和本地AI的询问信息进行回复

  • 第二轮：如果本地AI对图像画面细节有进一步追问，则回答

使用场景：

  • 深度图像分析

  • 迭代式问题探索

  • 复杂图像理解

参数说明：

  • image: 图像输入（路径或Base64）

  • question: 问题

  • session_id: 会话ID（用于第二轮对话，首次调用可不提供）

  • is_new_conversation: 是否开始新对话（设为true会创建新会话）

两轮对话流程：

  1. 第一轮：调用时不传session_id，AI分析图像并回复，返回会话ID

  2. 第二轮：传入session_id继续追问图像细节，AI回答后对话结束

  3. 超过两轮将无法继续，需开始新对话

示例：

# 第一轮对话
result1 = chat_vision(
    image="C:/chart.png",
    question="这个图表显示什么数据？"
)
session_id = result1["session_id"]

# 第二轮对话（追问细节，对话结束后无法继续）
if result1["remaining_turns"] > 0:
    result2 = chat_vision(
        image="C:/chart.png",
        question="数据有什么趋势？",
        session_id=session_id
    )

返回内容：

  • status: 执行状态

  • answer: 回答

  • session_id: 会话ID

  • conversation_turn: 当前对话轮次（1或2）

  • remaining_turns: 剩余对话轮次

  • can_continue: 是否可以继续对话

get_statusB

获取服务器状态

返回当前服务器配置和运行状态信息。

返回内容：

  • server_name: 服务器名称

  • provider: AI服务提供商

  • model: 当前使用的模型

  • chat_manager: 对话管理器状态

  • config: 当前配置信息

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.9/5.0

Scored across 3 tools

Disambiguation5/5

The three tools have clearly distinct purposes with no overlap: analyze_image for general image analysis, chat_vision for conversational image Q&A, and get_status for server monitoring. Each tool serves a unique function in the vision analysis workflow, making it easy for an agent to select the right one.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (analyze_image, chat_vision, get_status) with clear, descriptive verbs. The naming is uniform and predictable across the entire toolset, making it easy to understand each tool's function at a glance.

Tool Count4/5

Three tools is appropriate for a vision server's core functionality, covering analysis, conversation, and status checking. However, the count feels slightly minimal—additional tools like batch processing or image preprocessing could enhance completeness, but the current set is well-scoped for basic operations.

Completeness4/5

The toolset covers essential vision tasks: analysis, iterative questioning, and server status. Minor gaps exist, such as lacking tools for image manipulation (e.g., resize, crop) or batch processing, but agents can work around these with the provided tools for most common use cases in image understanding.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources