Skip to main content
Glama
freelancernasimofficial

NasCoder Perplexity MCP Ultra-Pro

perplexity_analytics

Analyze performance metrics and usage data to monitor and optimize the integration's effectiveness and track operational insights.

Instructions

Get detailed analytics and performance metrics for the Perplexity MCP server

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Handler for the 'perplexity_analytics' tool call. Calls getAnalytics() on the MCP instance and returns the analytics data wrapped in the expected MCP response format.
    case "perplexity_analytics":
      const analytics = nascoderMCP.getAnalytics();
      return {
        content: [{
          type: "object",
          data: analytics
        }]
      };
  • index.js:674-682 (registration)
    Registration of the 'perplexity_analytics' tool in the TOOLS array, which is returned by the ListToolsRequestSchema handler. Includes name, description, and empty input schema since no parameters are needed.
    {
      name: "perplexity_analytics",
      description: "Get detailed analytics and performance metrics for the Perplexity MCP server",
      inputSchema: {
        type: "object",
        properties: {},
        required: []
      }
    },
  • Input schema for the 'perplexity_analytics' tool, which is an empty object since the tool requires no parameters.
    inputSchema: {
      type: "object",
      properties: {},
      required: []
    }
  • Helper method getAnalytics() that compiles and returns detailed performance metrics, including request counts, cache statistics, token usage, model usage, uptime, and memory usage.
    getAnalytics() {
      try {
        const analytics = {
          ...this.analytics,
          cacheStats: {
            keys: this.cache ? this.cache.keys().length : 0,
            hits: this.analytics.cacheHits,
            misses: this.analytics.cacheMisses,
            hitRate: this.analytics.cacheHits + this.analytics.cacheMisses > 0 
              ? (this.analytics.cacheHits / (this.analytics.cacheHits + this.analytics.cacheMisses) * 100) 
              : 0
          },
          uptime: process.uptime(),
          memoryUsage: process.memoryUsage(),
          version: '2.0.0',
          apiVersion: '2025'
        };
        return analytics;
      } catch (error) {
        this.logger.error('Failed to get analytics:', error.message);
        return {
          error: 'Failed to retrieve analytics',
          totalRequests: this.analytics?.totalRequests || 0,
          errors: this.analytics?.errors || 0
        };
      }
    }
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'gets' analytics and metrics, implying a read-only operation, but doesn't disclose any behavioral traits such as authentication requirements, rate limits, data freshness, or potential side effects (e.g., if it triggers data collection). The description is too vague to inform the agent about how the tool behaves beyond its basic purpose.

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 a single, efficient sentence: 'Get detailed analytics and performance metrics for the Perplexity MCP server.' It is front-loaded with the core purpose, uses clear language, and contains no redundant or unnecessary information. Every word earns its place by specifying what is retrieved and for what scope.

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's complexity (0 parameters, no output schema, no annotations), the description is minimally complete. It states what the tool does but lacks details on usage guidelines, behavioral transparency, or output expectations. While it covers the basic purpose adequately, it doesn't provide enough context for an agent to fully understand when and how to use this tool effectively, especially compared to siblings.

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 tool has 0 parameters, and the schema description coverage is 100% (since there are no parameters to describe). In such cases, the baseline score is 4, as there are no parameters for the description to compensate for or add meaning beyond the schema. The description doesn't need to discuss parameters, and it appropriately avoids doing so.

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 analytics and performance metrics for the Perplexity MCP server.' It uses a specific verb ('Get') and identifies the resource ('analytics and performance metrics') with a clear scope ('for the Perplexity MCP server'). However, it doesn't explicitly differentiate from sibling tools like perplexity_ask_pro or perplexity_models, which might also provide some metrics or data.

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 any prerequisites, context for usage, or exclusions. For example, it doesn't clarify if this is for monitoring server health, debugging, or general reporting, or how it differs from perplexity_ask_pro which might handle queries. This lack of guidance leaves the agent with minimal context for tool selection.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/freelancernasimofficial/nascoder-perplexity-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server