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weidongxu-microsoft

Azure Java SDK MCP Server

azure_java_sdk_code_samples

Access code samples for Azure Java SDK packages to implement Azure services in Java applications. Provide the package name to retrieve working examples.

Instructions

Get code samples for Azure Java SDK, the package name usually starts with 'azure-'

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
packageYespackage name of the Azure SDK, for example: 'azure-ai-inference'

Implementation Reference

  • index.js:64-98 (handler)
    Handler logic that fetches the latest version of the Azure Java SDK package from Maven repository, retrieves its README.md, and returns the content as text.
    case "azure_java_sdk_code_samples":
      const { package: packageName } = args;
      if (!packageName) {
        throw new McpError(
          ErrorCode.InvalidRequest,
          "Package name is required"
        );
      }
      const groupId = "com.azure";
      const groupPath = groupId.replace(/\./g, "/");
      const artifactId = packageName;
      
      // Get latest version
      const artifactUrl = "https://repo1.maven.org/maven2/" + groupPath + "/" + artifactId;
      let response = await fetch(artifactUrl + "/maven-metadata.xml");
      const listData = await response.text();
      
      // Extract version from <latest>version</latest> tag
      const versionRegex = /<latest>(.*?)<\/latest>/;
      const versionMatch = listData.match(versionRegex);
      const latestVersion = versionMatch[1];
    
      // Get the readme.md of the SDK
      const readmeUrl = artifactUrl + "/" + latestVersion + "/" + artifactId + "-" + latestVersion + "-readme.md";
      response = await fetch(readmeUrl);
      const readmeData = await response.text();
    
      return {
        content: [
          {
            type: "text",
            text: readmeData,
          },
        ],
      };
  • Input schema defining the required 'package' parameter as a string.
    inputSchema: {
      type: "object",
      properties: {
        package: {
          type: "string",
          description: "package name of the Azure SDK, for example: 'azure-ai-inference'",
        },
      },
      required: ["package"],
    },
  • index.js:40-53 (registration)
    Registration of the tool in the ListToolsRequestSchema handler, including name, description, and input schema.
    {
      name: "azure_java_sdk_code_samples",
      description: "Get code samples for Azure Java SDK, the package name usually starts with 'azure-'",
      inputSchema: {
        type: "object",
        properties: {
          package: {
            type: "string",
            description: "package name of the Azure SDK, for example: 'azure-ai-inference'",
          },
        },
        required: ["package"],
      },
    },
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 states what the tool does but lacks critical behavioral details such as whether this is a read-only operation, if it requires authentication, rate limits, error handling, or what format the code samples are returned in. For a tool with zero annotation coverage, this is a significant gap.

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 with a single sentence that directly states the tool's purpose and includes a useful parameter hint. There is no wasted language, and it is front-loaded with the core functionality. Every word earns its place.

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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., sample code snippets, links, or documentation), behavioral traits, or error conditions. For a tool with no structured metadata, the description should provide more context to be fully helpful.

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%, with the parameter 'package' fully documented in the input schema. The description adds marginal value by noting that package names 'usually start with 'azure-'', which provides context beyond the schema's example. However, it doesn't elaborate on parameter constraints or usage, so it meets the baseline for high schema 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 tool's purpose with a specific verb ('Get') and resource ('code samples for Azure Java SDK'), making it immediately understandable. It distinguishes the target resource by specifying 'package name usually starts with 'azure-''. However, without sibling tools, differentiation from alternatives isn't explicitly needed, so it doesn't reach the highest score.

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 alternatives or in what context it should be applied. It mentions the package naming convention, but this is more of a parameter hint than usage guidance. No explicit when/when-not instructions or prerequisites are included.

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

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