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damonxue

OSSInsight MCP Server

get_developer_analysis

Analyze GitHub developer activity and contributions to understand their open source impact and coding patterns.

Instructions

Get detailed analysis of a GitHub developer, including their activity and contributions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
usernameYesGitHub username

Implementation Reference

  • The core handler function that fetches GitHub developer analysis data from the OSSInsight API (/users/{username}) or falls back to web scraping the OSSInsight page if the API fails.
    async function getDeveloperAnalysis(username: string): Promise<any> {
      try {
        // Get user information through API if available
        const userData = await apiRequest(`/users/${username}`);
        
        return {
          user_data: userData
        };
      } catch (error) {
        // If API fails, rely on web page data
        console.error(`API request failed, falling back to web scraping: ${error}`);
        const webUrl = `${OSSINSIGHT_WEB_URL}/analyze/user/${username}`;
        
        return {
          web_data: await scrapeOSSInsightPage(webUrl, {
            name: 'h1',
            bio: '.user-bio',
            repos: '.repos-count',
            // Add more selectors as needed
          }),
          web_url: webUrl
        };
      }
    }
  • Zod schema defining the input parameters for the tool: a required 'username' string.
    export const GetDeveloperAnalysisParamsSchema = z.object({
      username: z.string().describe("GitHub username")
    });
  • index.ts:292-296 (registration)
    Tool registration in the MCP listTools handler, providing name, description, and converted JSON schema.
    {
      name: "get_developer_analysis",
      description: "Get detailed analysis of a GitHub developer, including their activity and contributions.",
      inputSchema: zodToJsonSchema(GetDeveloperAnalysisParamsSchema)
    },
  • Dispatch handler in the MCP callTool request that parses arguments using the schema and invokes the getDeveloperAnalysis function.
    case "get_developer_analysis": {
      const args = GetDeveloperAnalysisParamsSchema.parse(request.params.arguments);
      const analysis = await getDeveloperAnalysis(args.username);
      return { content: [{ type: "text", text: JSON.stringify(analysis, null, 2) }] };
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'detailed analysis' but doesn't specify what that entails—e.g., whether it includes metrics like commit frequency, pull requests, or language usage. It also lacks information on rate limits, authentication needs, or response format, which are critical for a tool with no output schema.

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 a single, efficient sentence that front-loads the core purpose without unnecessary words. It could be slightly more structured by explicitly listing analysis components, but it avoids redundancy and stays focused, earning a high score for conciseness.

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 developer analysis and the absence of both annotations and an output schema, the description is incomplete. It doesn't explain what 'detailed analysis' includes, how results are returned, or any behavioral traits like data freshness or limitations. For a tool with no structured output documentation, this leaves significant gaps for an AI agent.

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 input schema has 100% description coverage, with the 'username' parameter clearly documented as 'GitHub username'. The description adds no additional parameter details beyond what the schema provides, such as format constraints or examples. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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: 'Get detailed analysis of a GitHub developer, including their activity and contributions.' It specifies the verb ('Get'), resource ('GitHub developer'), and scope ('detailed analysis'). However, it doesn't explicitly differentiate from sibling tools like 'get_repo_analysis' or 'natural_language_query', which prevents a perfect 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. It doesn't mention when to choose this over 'get_repo_analysis' for repository-level insights or 'natural_language_query' for broader queries. There's no context about prerequisites or exclusions, leaving usage decisions ambiguous.

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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