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Orion Vision MCP Server

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Orion Vision MCP サーバー 🚀

🔌 Cline、Cursor、Claude Desktop、その他の MCP クライアントと互換性があります。

Orion Vision MCPは、あらゆるMCPクライアントと互換性があります。

モデル コンテキスト プロトコル (MCP) は、AI システムがさまざまなデータ ソースやツールとシームレスに対話し、安全な双方向接続を実現できるようにするオープン スタンダードです。

Orion Vision MCP サーバーは以下を提供します。

  • Azure Form Recognizer / Document Intelligence とのシームレスな統合

  • ドキュメント分析およびフォームデータ抽出機能

  • さまざまな種類のドキュメント（領収書、請求書、身分証明書など）のサポート

  • TypeScriptによる型安全な操作

前提条件🔧

始める前に、次のものを用意してください。

  • Azure Form Recognizer / Document Intelligence エンドポイントとキー

  • クロードデスクトップまたはカーソル

  • Node.js (v20以上)

  • Git がインストールされている (Git インストール方法を使用する場合のみ必要)

Related MCP server: pdf-mcp

Orion Vision MCP サーバーのインストール ⚡

NPXで実行

npx -y orion-vision-mcp@latest

Smithery経由でインストール

Smithery 経由で Claude Desktop 用の Orion Vision MCP Server を自動的にインストールするには:

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

MCP クライアントの設定 ⚙️

Cline の設定 🤖

Cline で Orion Vision MCP サーバーをセットアップする最も簡単な方法は、マーケットプレイスから 1 回のクリックでセットアップすることです。

  1. VS CodeでClineを開く

  2. サイドバーのClineアイコンをクリックします

  3. 「MCPサーバー」タブ（4つの四角）に移動します

  4. 「Orion Vision」を検索し、「インストール」をクリックします

  5. プロンプトが表示されたら、Azure Form Recognizer の資格情報を入力します。

あるいは、Cline で Orion Vision MCP サーバーを手動で設定することもできます。

  1. Cline MCP 設定ファイルを開きます。

# 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. Orion Vision サーバー構成をファイルに追加します。

{
  "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. ファイルを保存し、Cline がすでに実行されている場合は再起動します。

カーソルの設定 🖥️

注: カーソルバージョン0.45.6以上が必要です

Cursor で Orion Vision MCP サーバーを設定するには:

  1. カーソル設定を開く

  2. 機能 > MCP サーバーに移動します

  3. 「+新しいMCPサーバーを追加」ボタンをクリックします。

  4. 以下の情報を入力してください。

    • 名前: サーバーのニックネームを入力します（例：「orion-vision-mcp」）

    • タイプ: タイプとして「コマンド」を選択

    • コマンド: サーバーを実行するコマンドを入力してください: GXP5

    重要: your-endpointとyour-key Azure Form Recognizer の資格情報に置き換えてください

Claude デスクトップ アプリの設定 🖥️

macOSの場合:

# 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"

Windowsの場合:

code %APPDATA%\Claude\claude_desktop_config.json

Orion Vision サーバー構成を追加します。

{
  "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"
      }
    }
  }
}

Claude デスクトップアプリでの使用 🎯

インストールが完了し、Claudeデスクトップアプリの設定が完了したら、Claudeデスクトップアプリを完全に閉じてから再度開き、orion-vision-mcpサーバーを確認してください。アプリの左下にハンマーアイコンが表示され、利用可能なMCPツールが表示されます。

オリオンビジョンの例

  1. ドキュメントを分析する:

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

  1. フォームデータの抽出:

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

  1. プロセスIDドキュメント:

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

トラブルシューティング 🛠️

よくある問題

  1. サーバーが見つかりません

    • npm --versionを実行して npm のインストールを確認します。

    • Claude Desktop の設定構文を確認する

    • node --versionを実行して Node.js が正しくインストールされていることを確認します。

  2. Azure Form Recognizer の資格情報の問題

    • Azure Form Recognizer のエンドポイントとキーが有効であることを確認します

    • 構成で資格情報が正しく設定されていることを確認します

    • 資格情報の周囲にスペースや引用符がないことを確認してください

  3. 文書処理の問題

    • ドキュメントのURLにアクセスできることを確認する

    • ドキュメント形式がサポートされているか確認してください

    • 文書が破損していないか、パスワードで保護されていないか確認してください

謝辞✨

  • MCP仕様のモデルコンテキストプロトコル

  • クロード・デスクトップのAnthropic

  • フォーム認識 / ドキュメント インテリジェンス向け Microsoft Azure

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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