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connectors_list

List available data connectors including GA4 and Google Search Console to connect live data sources for analysis.

Instructions

List available data connectors — GA4, Google Search Console, and more.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • src/index.js:54-63 (registration)
    The tool 'connectors_list' is registered in the STATIC_TOOLS catalog with a name, description, and empty input schema (no parameters).
      { 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 handler that proxies all tool calls (including 'connectors_list') to the remote MCP server via remoteClient.callTool(). No local implementation — the actual logic lives on the remote server.
    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,
        };
      }
    });
  • The input schema for 'connectors_list' is an empty object (no parameters required).
    { name: "connectors_list", description: "List available data connectors — GA4, Google Search Console, and more.", inputSchema: { type: "object", properties: {} } },
Behavior2/5

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

No annotations provided, so the description carries full burden. It does not disclose whether the tool is read-only, requires authentication, or what happens on error. The minimal description leaves behavioral traits unclear.

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?

Extremely concise single sentence with no wasted words. It front-loads the purpose and includes a concrete example.

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 tool has no parameters and no output schema, the description is minimally adequate. However, more context (e.g., whether the list is dynamic or static, how to interpret the output) would improve completeness, especially given the number of sibling tools.

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

Parameters4/5

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

The input schema has no parameters, so the description naturally cannot add parameter details. However, it adds value by listing example connectors (GA4, Google Search Console), which gives context beyond the schema.

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 explicitly states the tool lists available data connectors and gives specific examples (GA4, Google Search Console), clearly distinguishing it from sibling tools like connectors_query.

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 such as connectors_query. The description only states what it does, without any when-to-use or when-not-to-use context.

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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