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report_cards

Access individual card data from a generated report for rendering. Provide the processing ID to extract specific insights.

Instructions

Get individual card data from a report for rendering.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
processing_idYes

Implementation Reference

  • Input schema definition for report_cards tool. Declares the tool name, description, and inputSchema requiring a 'processing_id' string parameter.
    { name: "report_cards", description: "Get individual card data from a report for rendering.", inputSchema: { type: "object", properties: { processing_id: { type: "string" } }, required: ["processing_id"] } },
  • src/index.js:43-63 (registration)
    Static tool catalog registration (STATIC_TOOLS array) where report_cards is registered as a fallback tool for inspection mode. The actual handler is proxied to a remote MCP server at api.mcpanalytics.ai.
    const STATIC_TOOLS = [
      { name: "about", description: "Get platform info, pricing, usage stats, or documentation.", inputSchema: { type: "object", properties: { topic: { type: "string", description: "Topic: platform, pricing, current_usage, manual, or a docs section" } }, required: ["topic"] } },
      { name: "discover_tools", description: "Find analysis tools matching your data or question. Semantic search across 50+ statistical and ML tools.", inputSchema: { type: "object", properties: { query: { type: "string", description: "Text query describing what you want to analyze" }, dataset: { type: "string", description: "Dataset UUID to match tools against" } } } },
      { name: "tools_schema", description: "Get JSON schema for a tool — column_mapping and module_parameters required before tools_run.", inputSchema: { type: "object", properties: { tool_name: { type: "string", description: "Name of the tool" } }, required: ["tool_name"] } },
      { name: "tools_run", description: "Execute an analysis tool. Returns a shareable interactive HTML report URL.", inputSchema: { type: "object", properties: { tool_name: { type: "string", description: "Name of the tool to execute" }, taskList: { type: "object", description: "Contains inputs: dataset, userContext, column_mapping, module_parameters" } }, required: ["tool_name", "taskList"] } },
      { name: "tools_info", description: "Get detailed information about a specific analysis tool — use cases, assumptions, data requirements.", inputSchema: { type: "object", properties: { tool_name: { type: "string", description: "Name of the tool" } }, required: ["tool_name"] } },
      { name: "datasets_upload", description: "Generate a secure upload token for CSV files. Returns UUID + curl command for the user.", inputSchema: { type: "object", properties: { expires_in: { type: "integer", description: "Token expiration in seconds", default: 300 } } } },
      { name: "datasets_list", description: "List and search uploaded datasets with fuzzy matching.", inputSchema: { type: "object", properties: { search: { type: "string", description: "Search by name, description, or tags" }, limit: { type: "integer", description: "Max results", default: 20 } } } },
      { name: "datasets_read", description: "Read dataset contents — preview rows, columns, and types.", inputSchema: { type: "object", properties: { uuid: { type: "string", description: "Dataset UUID" }, secret: { type: "string", description: "Dataset secret key" }, rows: { type: "integer", description: "Number of rows to preview", default: 10 } }, required: ["uuid"] } },
      { name: "datasets_download", description: "Generate a single-use download token for securely downloading datasets.", inputSchema: { type: "object", properties: { uuid: { type: "string", description: "Dataset UUID" } }, required: ["uuid"] } },
      { name: "datasets_update", description: "Update dataset metadata — name, description, tags, visibility.", inputSchema: { type: "object", properties: { uuid: { type: "string", description: "Dataset UUID" } }, required: ["uuid"] } },
      { name: "connectors_list", description: "List available data connectors — GA4, Google Search Console, and more.", inputSchema: { type: "object", properties: {} } },
      { name: "connectors_query", description: "Pull live data from a connected source using connector:// URIs.", inputSchema: { type: "object", properties: { uri: { type: "string", description: "Connector URI (e.g., connector://mcpanalytics_gsc/search_analytics?...)" } }, required: ["uri"] } },
      { name: "reports_list", description: "List analysis reports with metadata.", inputSchema: { type: "object", properties: { limit: { type: "integer", description: "Max results", default: 10 } } } },
      { name: "reports_search", description: "Search reports by job ID, tool name, or keyword.", inputSchema: { type: "object", properties: { query: { type: "string", description: "Search query" }, job_ids: { type: "array", items: { type: "string" }, description: "Filter by processing IDs" } } } },
      { name: "reports_view", description: "View a specific report by processing ID.", inputSchema: { type: "object", properties: { processing_id: { type: "string", description: "Processing ID from tools_run" } }, required: ["processing_id"] } },
      { name: "report_cards", description: "Get individual card data from a report for rendering.", inputSchema: { type: "object", properties: { processing_id: { type: "string" } }, required: ["processing_id"] } },
      { name: "agent_advisor", description: "Conversational AI that guides analysis and interprets results.", inputSchema: { type: "object", properties: { message: { type: "string", description: "Your question or request" } }, required: ["message"] } },
      { name: "billing", description: "Check credit balance, subscription status, or open billing portal.", inputSchema: { type: "object", properties: { action: { type: "string", enum: ["status", "portal", "usage"], description: "Billing action", default: "status" } } } },
      { name: "module_request", description: "Request a custom analysis module to be built for your use case.", inputSchema: { type: "object", properties: { description: { type: "string", description: "Describe the analysis you need" } }, required: ["description"] } },
    ];
  • Generic CallToolRequestSchema handler that proxies ALL tool calls (including report_cards) to a remote MCP client. There is no local handler; the remote server at api.mcpanalytics.ai/mcp executes the actual report_cards logic.
    server.setRequestHandler(CallToolRequestSchema, async (request) => {
      if (!remoteClient) {
        return {
          content: [
            {
              type: "text",
              text: "MCP Analytics API key required. Set MCP_ANALYTICS_API_KEY in your environment.\nGet a free key at https://app.mcpanalytics.ai",
            },
          ],
          isError: true,
        };
      }
    
      try {
        const result = await remoteClient.callTool({
          name: request.params.name,
          arguments: request.params.arguments || {},
        });
        return result;
      } catch (err) {
        return {
          content: [{ type: "text", text: `Error: ${err.message}` }],
          isError: true,
        };
      }
    });
Behavior2/5

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

No annotations provided; description only hints at rendering use case but does not disclose side effects, authentication needs, rate limits, or what 'individual card data' entails. The agent cannot infer safety or behavioral constraints.

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

Conciseness3/5

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

Single sentence is concise but omits critical details. Could include context about processing_id and output format without losing brevity.

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 no output schema and minimal description, the tool lacks sufficient information for correct invocation. Missing details about input semantics and return value structure.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 1 parameter (processing_id) with 0% description coverage. Description adds no explanation of what processing_id is, how to obtain it, or its format. Fails to compensate for schema gap.

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?

Description clearly states verb 'Get', resource 'individual card data from a report', and purpose 'for rendering'. It distinguishes from sibling tools like reports_list, reports_search, reports_view which operate on reports at a higher level.

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 on when to use this tool versus alternatives. Does not mention prerequisites, when not to use, or provide context about report processing state required.

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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