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Orion Vision MCP Server

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Orion Vision MCP 服务器🚀

🔌与 Cline、Cursor、Claude Desktop 和任何其他 MCP 客户端兼容！

Orion Vision MCP 还兼容任何 MCP 客户端

模型上下文协议 (MCP) 是一种开放标准，使 AI 系统能够与各种数据源和工具无缝交互，促进安全的双向连接。

Orion Vision MCP 服务器提供：

  • 与 Azure 表单识别器/文档智能无缝集成

  • 文档分析和表单数据提取功能

  • 支持各种文件类型（收据、发票、身份证件等）

  • 使用 TypeScript 进行类型安全操作

先决条件🔧

在开始之前，请确保您已：

  • Azure 表单识别器/文档智能端点和密钥

  • Claude Desktop 或 Cursor

  • Node.js（v20 或更高版本）

  • 已安装 Git（仅在使用 Git 安装方法时才需要）

Related MCP server: pdf-mcp

Orion Vision MCP 服务器安装⚡

使用 NPX 运行

npx -y orion-vision-mcp@latest

通过 Smithery 安装

要通过 Smithery 自动为 Claude Desktop 安装 Orion Vision MCP Server：

npx -y @smithery/cli install @orion-vision/mcp --client claude

配置 MCP 客户端 ⚙️

配置 Cline 🤖

在 Cline 中设置 Orion Vision MCP 服务器的最简单方法是通过市场单击一下：

  1. 在 VS Code 中打开 Cline

  2. 点击侧边栏中的 Cline 图标

  3. 导航到“MCP 服务器”选项卡（4 个方块）

  4. 搜索“Orion Vision”并点击“安装”

  5. 出现提示时，输入 Azure 表单识别器凭据

或者，您可以在 Cline 中手动设置 Orion Vision MCP 服务器：

  1. 打开 Cline MCP 设置文件：

# For macOS:
code ~/Library/Application\ Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json

# For Windows:
code %APPDATA%\Code\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json

  1. 将 Orion Vision 服务器配置添加到文件中：

{
  "mcpServers": {
    "orion-vision-mcp": {
      "command": "npx",
      "args": ["-y", "orion-vision-mcp@latest"],
      "env": {
        "AZURE_FORM_RECOGNIZER_ENDPOINT": "your-endpoint-here",
        "AZURE_FORM_RECOGNIZER_KEY": "your-key-here"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

  1. 如果 Cline 已运行，请保存文件并重新启动它。

配置光标🖥️

注意：需要 Cursor 版本 0.45.6 或更高版本

要在 Cursor 中设置 Orion Vision MCP 服务器：

  1. 打开游标设置

  2. 导航至“功能”>“MCP 服务器”

  3. 点击“+ 添加新的 MCP 服务器”按钮

  4. 填写以下信息：

    • 名称：输入服务器的昵称（例如，“orion-vision-mcp”）

    • 类型：选择“命令”作为类型

    • 命令：输入运行服务器的命令：GXP5

    重要提示：用 Azure 表单识别器凭据替换your-endpoint和your-key

配置 Claude 桌面应用程序🖥️

对于 macOS：

# Create the config file if it doesn't exist
touch "$HOME/Library/Application Support/Claude/claude_desktop_config.json"

# Opens the config file in TextEdit
open -e "$HOME/Library/Application Support/Claude/claude_desktop_config.json"

对于 Windows：

code %APPDATA%\Claude\claude_desktop_config.json

添加 Orion Vision 服务器配置：

{
  "mcpServers": {
    "orion-vision-mcp": {
      "command": "npx",
      "args": ["-y", "orion-vision-mcp@latest"],
      "env": {
        "AZURE_FORM_RECOGNIZER_ENDPOINT": "your-endpoint-here",
        "AZURE_FORM_RECOGNIZER_KEY": "your-key-here"
      }
    }
  }
}

在 Claude 桌面应用程序中的使用

安装完成并配置好 Claude 桌面应用程序后，您必须完全关闭并重新打开 Claude 桌面应用程序才能看到 orion-vision-mcp 服务器。您应该会在应用程序左下角看到一个锤子图标，表示可用的 MCP 工具。

Orion Vision 示例

  1. 分析文档：

Analyze the document at "https://example.com/document.pdf" using Azure Form Recognizer.

  1. 提取表单数据：

Extract data from the invoice at "https://example.com/invoice.pdf".

  1. 进程 ID 文件：

Process the ID document at "https://example.com/id.pdf" and extract relevant information.

故障排除🛠️

常见问题

  1. 未找到服务器

    • 通过运行npm --version来验证 npm 安装

    • 检查 Claude Desktop 配置语法

    • 通过运行node --version确保正确安装了 Node.js

  2. Azure 表单识别器凭据问题

    • 确认 Azure 表单识别器端点和密钥有效

    • 检查配置中的凭据是否正确设置

    • 验证凭证周围没有空格或引号

  3. 文档处理问题

    • 验证文档 URL 是否可访问

    • 检查文档格式是否受支持

    • 确保文档未损坏或未受密码保护

致谢✨

  • MCP 规范的模型上下文协议

  • 克劳德桌面版的 Anthropic

  • Microsoft Azure 用于表单识别器/文档智能

Available Tools

2 tools
analyze-documentC

Analyzes a document using Azure Form Recognizer and returns structured data

ParametersJSON Schema
NameRequiredDescriptionDefault
modelIdNoOptional model ID for custom models
urlYesURL of the document to analyze

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the analysis method and output type but omits critical details like authentication needs, rate limits, processing time, error handling, or what 'structured data' entails, leaving significant gaps for a tool performing external API calls.

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 a single, efficient sentence with zero waste, clearly front-loading the core functionality. Every word contributes to understanding the tool's purpose without redundancy.

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

Completeness2/5

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

Given the complexity of document analysis with an external service, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, output format, error cases, and usage context, which are essential for effective tool invocation.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents both parameters (modelId and url). The description adds no additional parameter semantics beyond what the schema provides, such as examples or constraints, meeting the baseline for high coverage.

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

Purpose4/5

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

The description clearly states the action ('analyzes') and resource ('a document') using Azure Form Recognizer, with the outcome of returning structured data. It distinguishes from the sibling 'extract-form-data' by specifying the analysis method, though not explicitly contrasting their use cases.

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

Usage Guidelines2/5

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 the sibling 'extract-form-data' or other alternatives. The description implies usage for document analysis but lacks context on prerequisites, constraints, or comparative scenarios.

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

extract-form-dataC

Extracts structured data from forms using Azure Form Recognizer

ParametersJSON Schema
NameRequiredDescriptionDefault
formTypeYesType of form to analyze
urlYesURL of the form document to analyze

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the technology (Azure Form Recognizer), it doesn't describe what happens during extraction - whether it's a read-only operation, if it modifies data, authentication requirements, rate limits, or error handling. For a tool with no annotation coverage, this leaves significant behavioral gaps.

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 extremely concise - a single sentence that directly states the tool's purpose without any unnecessary words. It's front-loaded with the core functionality and doesn't waste space on redundant information. Every word earns its place in this minimal description.

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

Completeness2/5

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

Given that there's no output schema and no annotations, the description should provide more context about what the tool returns and how it behaves. For a data extraction tool with 2 required parameters, the description is too minimal - it doesn't explain the extraction results format, error conditions, or practical usage examples. The completeness is inadequate for the tool's complexity.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it doesn't explain how 'formType' affects extraction results or provide examples of valid URLs. With complete schema coverage, the baseline score of 3 is appropriate.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Extracts structured data from forms using Azure Form Recognizer'. It specifies the action (extracts), resource (structured data from forms), and technology (Azure Form Recognizer). However, it doesn't explicitly differentiate from its sibling 'analyze-document', which might have overlapping functionality.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus its sibling 'analyze-document' or other alternatives. It doesn't mention prerequisites, limitations, or specific scenarios where this tool is preferred. The only implied context is that it works with forms, but no explicit usage instructions are given.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updatesv1.0.0
    • First observedanalyze-document
    • First observedextract-form-data
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

C2.6/5.0

Scored across 2 tools

Disambiguation1/5

The two tools have nearly identical purposes: both use Azure Form Recognizer to extract structured data from documents/forms. 'analyze-document' and 'extract-form-data' are functionally indistinguishable, with no clear boundary between them. This high ambiguity will cause agents to misselect between tools.

Naming Consistency3/5

Both tools use kebab-case naming, which is consistent. However, the verb choices ('analyze' vs 'extract') are different despite similar functionality, creating minor inconsistency. The naming pattern is readable but not perfectly aligned in purpose.

Tool Count2/5

With only 2 tools, the server feels thin for a vision/document processing domain. A typical MCP server for this scope would include more operations like text extraction, image analysis, or OCR configuration. The minimal tool count limits functionality and suggests incomplete coverage.

Completeness2/5

For a vision/document processing server, there are significant gaps: no image analysis tools, no OCR configuration, no batch processing, and no support for different document types beyond forms. The surface is severely limited, focusing only on form data extraction without broader vision capabilities.

Maintenance

ActivityInactive
ResponsivenessNo issues

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