> ## Documentation Index > Fetch the complete documentation index at: https://docs.hasmcp.com/llms.txt > Use this file to discover all available pages before exploring further. # How can I view Tool Call Analytics to see my most frequently used tools? > Discovering usage trends and optimizing high-traffic endpoints. # Viewing Tool Call Analytics Understanding which infrastructure endpoints your AI Agents rely on most heavily is critical for prioritizing internal engineering resources. HasMCP provides a dedicated **Analytics Dashboard** exclusively for tracking tool consumption metrics. HasMCP Server Analytics Dashboard ### Analyzing Usage Trends Navigate to the "Analytics" tab in your Workspace. Here, you can segment tool execution metrics by default timeframes (24 Hours, 7 Days, 30 Days) or query any custom time window you desire. 1. **High-Level Usage Metrics**: Quickly view the total number of connected Clients, active Users, individual Sessions, and raw Tool Calls processed during that window. 2. **Performance Metrics**: HasMCP splits performance between external API requests and internal Tool executions. For both, it displays the total **Tokens** consumed, the raw **Payload Size** transferred (e.g., 1.5 MB), and the **Median Latency** (p50) in milliseconds. This allows developers to instantly spot sluggish database queries or massive hidden payloads. 3. **Savings with Interceptors**: This section explicitly visualizes exactly how much context payload optimization occurred *before* the data reached the LLM. It tracks your total **Token savings** and **Payload savings** percentages resulting from your JMESPath or Goja (JS) interceptors. 4. **Top Tools**: A ranked usage distribution list showing your most frequently executed tools alongside their exact call counts. This instantly highlights if your agents are polling Jira ten times more often than Salesforce. ### Cost Attribution By identifying the most frequently utilized endpoints, management can accurately allocate API costs to specific Provider targets. For example, if a specific Serper API tool consumes 80% of daily executions, engineering can decide to aggressively prune those payloads natively using **JMESPath** or **Goja (JS) Interceptors**. This context payload optimization happens *before* the data reaches the LLM. HasMCP automatically tracks the byte difference before and after interception, counting this reduction as compounding **Payload Savings** and **Token Savings** over time, directly driving down your corporate SaaS LLM costs.