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Cognitive-Stack

Orion Vision MCP Server

by Cognitive-Stack
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TypeScript
Remote

Servidor MCP de Orion Vision 🚀

🔌 ¡ Compatible con Cline, Cursor, Claude Desktop y cualquier otro cliente MCP!

Orion Vision MCP también es compatible con cualquier cliente MCP

El Protocolo de Contexto de Modelo (MCP) es un estándar abierto que permite que los sistemas de IA interactúen sin problemas con diversas fuentes de datos y herramientas, lo que facilita conexiones bidireccionales seguras.

El servidor MCP de Orion Vision proporciona:

  • Integración perfecta con Azure Form Recognizer/Document Intelligence

  • Capacidades de análisis de documentos y extracción de datos de formularios

  • Soporte para varios tipos de documentos (recibos, facturas, documentos de identidad, etc.)

  • Operaciones de tipos seguros con TypeScript

Prerrequisitos 🔧

Antes de comenzar, asegúrese de tener:

  • Punto de conexión y clave de Azure Form Recognizer/Document Intelligence

  • Escritorio o cursor de Claude

  • Node.js (v20 o superior)

  • Git instalado (solo es necesario si se utiliza el método de instalación Git)

Related MCP server: pdf-mcp

Instalación del servidor Orion Vision MCP ⚡

Corriendo con NPX

npx -y orion-vision-mcp@latest

Instalación mediante herrería

Para instalar Orion Vision MCP Server para Claude Desktop automáticamente a través de Smithery:

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

Configuración de clientes MCP ⚙️

Configurando Cline 🤖

La forma más sencilla de configurar el servidor Orion Vision MCP en Cline es a través del marketplace con un solo clic:

  1. Abrir Cline en VS Code

  2. Haga clic en el icono de Cline en la barra lateral

  3. Vaya a la pestaña "Servidores MCP" (4 cuadrados)

  4. Busca "Orion Vision" y haz clic en "instalar".

  5. Cuando se le solicite, ingrese sus credenciales de Azure Form Recognizer

Alternativamente, puede configurar manualmente el servidor MCP de Orion Vision en Cline:

  1. Abra el archivo de configuración de Cline MCP:

# 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. Agregue la configuración del servidor Orion Vision al archivo:

{
  "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. Guarde el archivo y reinicie Cline si ya está ejecutándose.

Configurando el cursor 🖥️

Nota : Requiere la versión de Cursor 0.45.6 o superior

Para configurar el servidor MCP de Orion Vision en Cursor:

  1. Abrir configuración del cursor

  2. Vaya a Características > Servidores MCP

  3. Haga clic en el botón "+ Agregar nuevo servidor MCP"

  4. Complete la siguiente información:

    • Nombre : Ingrese un apodo para el servidor (por ejemplo, "orion-vision-mcp")

    • Tipo : Seleccione "comando" como tipo

    • Comando : Ingrese el comando para ejecutar el servidor: GXP5

    Importante : reemplace your-endpoint y your-key con sus credenciales de Azure Form Recognizer

Configurando la aplicación Claude Desktop 🖥️

Para 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"

Para Windows:

code %APPDATA%\Claude\claude_desktop_config.json

Agregue la configuración del servidor Orion Vision:

{
  "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"
      }
    }
  }
}

Uso en la aplicación de escritorio Claude 🎯

Una vez completada la instalación y configurada la aplicación de escritorio de Claude, debe cerrarla por completo y volver a abrirla para ver el servidor orion-vision-mcp. Debería ver un icono de martillo en la esquina inferior izquierda de la aplicación, que indica las herramientas MCP disponibles.

Ejemplos de la visión de Orión

  1. Analizar un documento :

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

  1. Extraer datos del formulario :

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

  1. Documento de identificación del proceso :

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

Solución de problemas 🛠️

Problemas comunes

  1. Servidor no encontrado

    • Verifique la instalación de npm ejecutando npm --version

    • Compruebe la sintaxis de configuración de Claude Desktop

    • Asegúrese de que Node.js esté instalado correctamente ejecutando node --version

  2. Problemas con las credenciales de Azure Form Recognizer

    • Confirme que su punto de conexión y clave de Azure Form Recognizer sean válidos

    • Compruebe que las credenciales estén configuradas correctamente en la configuración.

    • Verifique que no haya espacios ni comillas alrededor de las credenciales

  3. Problemas de procesamiento de documentos

    • Verifique que la URL del documento sea accesible

    • Compruebe que el formato del documento sea compatible

    • Asegúrese de que el documento no esté dañado ni protegido con contraseña.

Agradecimientos ✨

  • Protocolo de contexto de modelo para la especificación MCP

  • Antrópico para Claude Desktop

  • Microsoft Azure para Form Recognizer / Document Intelligence

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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