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Azure Resource Graph MCP Server

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MCP-Server-Demo

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Anforderungsfluss

Azure Resource Graph MCP-Server

Dies ist ein Model Context Protocol (MCP)-Server, der Zugriff auf Azure Resource Graph-Abfragen bietet. Mithilfe von Resource Graph-Abfragen können Sie Informationen zu Azure-Ressourcen in Ihren Abonnements abrufen.

Merkmale

  • Abfragen von Azure-Ressourcen mithilfe von Resource Graph-Abfragen

  • Die Standardabfrage gibt die Ressourcen-ID, den Namen, den Typ und den Standort zurück.

  • Unterstützt benutzerdefinierte Resource Graph-Abfragen

  • Verwendet Azure DefaultAzureCredential zur Authentifizierung

Related MCP server: Azure DevOps MCP Server

Voraussetzungen

  • Node.js installiert

  • Azure-Abonnement

  • Azure CLI installiert und angemeldet oder andere Azure-Anmeldeinformationen konfiguriert

Ausführen des MCP-Servers

Sie können den MCP-Server entweder mit Cursor IDE oder Visual Studio Code ausführen.

Option 1: Cursor-IDE-Integration

So integrieren Sie den MCP-Server in die Cursor IDE:

  1. Klonen Sie dieses Repository auf Ihren lokalen Computer (z. B. C:\YOUR_WORKSPACE\azure-resource-graph-mcp-server ).

  2. Erstellen Sie das Projekt:

npm install
npm run build
  1. Öffnen Sie die Cursoreinstellungen (JSON) und fügen Sie die folgende Konfiguration hinzu:

{
  "mcpServers": {
    "azure-resource-graph-mcp-server": {
      "command": "node",
      "args": [
        "C:\\YOUR_WORKSPACE\\azure-resource-graph-mcp-server\\build\\index.js"
      ],
      "env": {
        "SUBSCRIPTION_ID": "xxxxxx-xx-xx-xx-xxxxxx"
      },
    }
  }
}

Hinweis : Stellen Sie sicher, dass Sie den Pfad aktualisieren, damit er mit dem Speicherort Ihres lokalen Repositorys übereinstimmt.

  1. Starten Sie Cursor IDE neu, um die Änderungen zu übernehmen

Option 2: VS Code-Integration

So integrieren Sie den MCP-Server in Visual Studio Code:

  1. Klonen Sie dieses Repository auf Ihren lokalen Computer

  2. Erstellen Sie das Projekt:

npm install
npm run build
  1. Öffnen Sie die VS Code-Einstellungen (JSON), indem Sie Ctrl+Shift+P drücken, geben Sie „Einstellungen (JSON)“ ein und wählen Sie „Einstellungen: Benutzereinstellungen öffnen (JSON)“.

  2. Fügen Sie die folgende Konfiguration hinzu:

{
    "mcp": {
        "servers": {
            "azure-resource-graph": {
                "type": "stdio",
                "command": "node",
                "args": [
                    "C:\\YOUR_WORKSPACE\\azure-resource-graph-mcp-server\\build\\index.js"
                ],
                "env": {
                  "SUBSCRIPTION_ID": "xxxxxx-xx-xx-xx-xxxxxx"
                },
            }
        }
    }
}

Hinweis : Stellen Sie sicher, dass Sie den Pfad aktualisieren, damit er mit dem Speicherort Ihres lokalen Repositorys übereinstimmt.

  1. Speichern Sie die Datei settings.json

  2. Starten Sie VS Code neu, um die Änderungen anzuwenden

Der MCP-Server kann jetzt in VS Code mit Cursorintegration verwendet werden.

Verwendung

Der Server stellt das folgende Tool bereit:

Abfrageressourcen

Ruft Ressourcen und ihre Details aus Azure Resource Graph ab.

Parameter:

  • subscriptionId (optional): Azure-Abonnement-ID (standardmäßig die konfigurierte ID)

  • query (optional): Benutzerdefinierte Ressourcendiagrammabfrage (Standard: „Ressourcen | Projekt-ID, Name, Typ, Standort“)

Umgebungs-Setup

  1. Stellen Sie zunächst sicher, dass Sie bei der Azure CLI angemeldet sind, indem Sie Folgendes ausführen:

    az login

    Dieser Schritt ist für die lokale Entwicklung von entscheidender Bedeutung, da DefaultAzureCredential automatisch Ihre Azure CLI-Anmeldeinformationen verwendet.

  2. Richten Sie Ihre Umgebungsvariablen ein:

    • Kopieren Sie .env.example nach .env

    • Aktualisieren Sie AZURE_SUBSCRIPTION_ID in .env mit Ihrer tatsächlichen Abonnement-ID

    • Andere Variablen ( AZURE_TENANT_ID , AZURE_CLIENT_ID , AZURE_CLIENT_SECRET ) sind bei Verwendung der Azure CLI-Authentifizierung optional

  3. Stellen Sie sicher, dass Sie die richtigen Azure-Anmeldeinformationen konfiguriert haben. Der Server verwendet DefaultAzureCredential, das Folgendes unterstützt:

    • Azure-Befehlszeilenschnittstelle

    • Verwaltete Identität

    • Visual Studio Code-Anmeldeinformationen

    • Umgebungsvariablen

  4. Wenn Sie Umgebungsvariablen verwenden, richten Sie Folgendes ein:

    • AZURE_SUBSCRIPTION_ID

    • AZURE_TENANT_ID

    • AZURE_CLIENT_ID

    • AZURE_CLIENT_SECRET

Fehlerbehandlung

Der Server umfasst eine robuste Fehlerbehandlung für:

  • Fehler bei der Initialisierung des Azure-Clients

  • Abfrageausführungsfehler

  • Ungültige Abfragen oder Parameter

Entwicklung

So arbeiten Sie an diesem Projekt:

  1. Nehmen Sie Änderungen im src Verzeichnis vor

  2. Erstellen mit npm run build

  3. Testen Sie Ihre Änderungen, indem Sie den Server ausführen

Lizenz

Dieses Projekt ist unter der MIT-Lizenz lizenziert. Weitere Informationen finden Sie in der Datei LICENSE .

Available Tools

1 tool
query-resourcesB

Retrieves resources and their details from Azure Resource Graph. Use this tool to search, filter, and analyze Azure resources across subscriptions. It supports Kusto Query Language (KQL) for complex queries to find resources by type, location, tags, or properties. Useful for infrastructure auditing, resource inventory, compliance checking, and understanding your Azure environment's current state.

ParametersJSON Schema
NameRequiredDescriptionDefault
subscriptionIdNoAzure subscription ID
queryNoResource Graph query, defaults to listing all resources

TDQS

B3.4/5.0
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. It mentions the tool retrieves details and supports KQL for queries, but it does not disclose critical behavioral traits such as whether it's read-only or destructive, authentication requirements, rate limits, or pagination behavior. This leaves significant gaps for an agent to understand how to invoke it safely and effectively.

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

Conciseness4/5

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

The description is appropriately sized and front-loaded, starting with the core purpose and usage. Each sentence adds value, such as explaining KQL support and use cases, with no redundant information. However, it could be slightly more concise by integrating the use cases more tightly with the main description.

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

Completeness3/5

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

Given the complexity of querying Azure resources with KQL and no annotations or output schema, the description is moderately complete. It covers the purpose, usage context, and parameter hints, but it lacks details on behavioral aspects like safety, response format, and error handling, which are important for an agent to use the tool effectively in this context.

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?

The schema description coverage is 100%, so the schema already documents both parameters (subscriptionId and query) adequately. The description adds some context by mentioning KQL for complex queries and default behavior, but it does not provide additional semantic details beyond what the schema offers, such as query format examples or subscription ID usage nuances.

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 clearly states the tool's purpose with specific verbs ('retrieves', 'search, filter, and analyze') and resources ('Azure resources'), and it distinguishes its scope by mentioning Azure Resource Graph and KQL. It provides concrete use cases like infrastructure auditing and compliance checking, making the purpose highly specific and actionable.

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

Usage Guidelines3/5

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

The description implies usage through phrases like 'Use this tool to search, filter, and analyze' and lists scenarios such as auditing and inventory, but it does not explicitly state when to use this tool versus alternatives or provide exclusions. With no sibling tools, the lack of comparative guidance is less critical, but it still lacks explicit when/when-not instructions.

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. 1 tool update
    • First observedquery-resources

TDQS

B3.4/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap in purpose. The tool 'query-resources' has a clearly defined and singular function, making it impossible for an agent to confuse it with another tool.

Naming Consistency5/5

Since there is only one tool, naming consistency is inherently perfect. The tool name 'query-resources' follows a clear verb_noun pattern, and there are no other tools to create inconsistency.

Tool Count2/5

A single tool for an Azure Resource Graph server feels thin and under-scoped. While the tool is powerful, typical MCP servers for cloud resource management offer multiple operations (e.g., list, get, filter, aggregate), making one tool insufficient for comprehensive coverage and likely to limit agent workflows.

Completeness2/5

The tool surface is severely incomplete for the domain of Azure resource management. It only provides a generic query function, missing essential operations like listing resource types, getting specific resources by ID, aggregating metrics, or managing tags, which are common in such systems and necessary for full agent functionality.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

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