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get_signal_analytics

Read-only

Analyze aggregated product feedback signals over time to identify trends and patterns in customer insights from multiple communication platforms.

Instructions

Get aggregated analytics about signals over a time period.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
startDateYesStart date (YYYY-MM-DD format)
endDateYesEnd date (YYYY-MM-DD format)
groupByNoHow to group the analyticsday

Implementation Reference

  • Definition of the 'get_signal_analytics' tool, including its input schema and metadata.
    {
      name: 'get_signal_analytics',
      description: 'Get aggregated analytics about signals over a time period.',
      inputSchema: {
        type: 'object' as const,
        properties: {
          startDate: { type: 'string', description: 'Start date (YYYY-MM-DD format)' },
          endDate: { type: 'string', description: 'End date (YYYY-MM-DD format)' },
          groupBy: {
            type: 'string',
            enum: ['day', 'week', 'month', 'source', 'sentiment', 'category'],
            description: 'How to group the analytics',
            default: 'day',
          },
        },
        required: ['startDate', 'endDate'],
      },
      annotations: { readOnlyHint: true, destructiveHint: false, openWorldHint: true },
      _meta: { 'openai/visibility': 'public' },
    },
  • The handleProxyTool function, which routes calls for this tool to the main IdeaLift app's MCP handler via the idealiftClient.
    export async function handleProxyTool(
      toolName: string,
      args: Record<string, unknown>,
      chatgptSubjectId: string
    ): Promise<{ content: Array<{ type: string; text: string }>; isError: boolean }> {
      try {
        const response = await idealiftClient.mcpProxy(
          chatgptSubjectId,
          'tools/call',
          { name: toolName, arguments: args }
        );
    
        if (response.error) {
          return {
            content: [{ type: 'text', text: `Error: ${response.error.message}` }],
            isError: true,
          };
        }
    
        // The result from handleJsonRpcRequest for tools/call is { content: [...], isError?: boolean }
        const result = response.result as { content?: Array<{ type: string; text: string }>; isError?: boolean } | undefined;
    
        if (result?.content) {
          return {
            content: result.content,
            isError: result.isError || false,
          };
        }
    
        // Fallback: wrap the result as text
        return {
          content: [{ type: 'text', text: JSON.stringify(response.result, null, 2) }],
          isError: false,
        };
      } catch (error) {
        console.error(`[ProxyTool] Error calling ${toolName}:`, error);
        return {
          content: [{ type: 'text', text: `Proxy error: ${(error as Error).message}` }],
          isError: true,
        };
      }
    }
Behavior3/5

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

Annotations establish the read-only, non-destructive safety profile. The description adds that the operation is temporal ('over a time period') and involves aggregation, but it omits what specific metrics are returned (counts, averages, sentiment scores) or any rate limiting behavior, leaving significant behavioral gaps.

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 single sentence is efficiently structured with a front-loaded verb ('Get') and zero redundancy. However, the extreme brevity limits the description's utility given the absence of an output schema.

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?

Sufficient for a simple read-only tool with well-documented input parameters and safety annotations. However, lacking an output schema, the description should ideally specify what analytics structure is returned (e.g., counts, trends) rather than relying solely on the generic term 'analytics'.

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?

With 100% schema description coverage, the baseline is adequately met. The description mentions 'aggregated' which contextually supports the 'groupBy' parameter's purpose, but it adds no additional semantic detail regarding date format expectations or aggregation logic beyond what the schema already documents.

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 uses 'aggregated analytics' to clearly identify the operation (aggregation/statistics) and resource (signals), distinguishing it from the sibling tool 'list_signals' which presumably returns individual signal records rather than rollups. The temporal scope ('over a time period') further clarifies the analysis context.

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 select this tool versus 'list_signals' for raw data retrieval, nor does it mention the intended use case (e.g., reporting, trend analysis) or prerequisite conditions for the date range.

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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