Real-Time MCP Monitoring and Logging

Model Context Protocol (MCP) is a protocol that allows large language models (LLMs) to interact with external systems through a defined interface. These systems can include APIs, databases, filesystems, or other tools. MCP servers expose these capabilities as tools that LLMs can call in real time.

As more developers adopt MCP, the operational complexity increases. Many MCP servers are deployed as part of production workflows, automation pipelines, or agent systems. Once an MCP server is active and receiving requests from an LLM, it becomes important to observe how it behaves over time.

Why Implement Monitoring and Logging for MCP?

Monitoring and logging help you observe the behavior of an MCP server during execution. These systems track requests, responses, errors, and performance in real time. Because MCP servers act as bridges between LLMs and external systems, monitoring becomes important for understanding stability, detecting failures, and ensuring secure operation.

Unlike traditional APIs, MCP servers expose tools that can be selected dynamically by the LLM at runtime. This means any tool can be invoked depending on user input, without prior knowledge of which ones will be used. This variability makes it harder to anticipate errors or performance issues without monitoring in place.

Key reasons for implementing MCP monitoring:

• Security visibility: Tool invocations may access filesystems, APIs, or databases. Monitoring helps detect unauthorized access attempts or potential misuse.

• Performance tracking: Tool execution time, memory usage, and request volume can vary widely. Logs help identify latency spikes or slow-performing endpoints.

• Usage analytics: Monitoring shows which tools are used most often, how frequently they're called, and how users interact with them over time.

• Error detection: Real-time logs reveal failed tool calls, malformed requests, and server-side exceptions that aren't visible to the LLM user.

How MCP Servers Work in Real Time

An MCP server is a local or remote service that follows the Model Context Protocol. It receives structured requests from a language model and responds with structured outputs. The server exposes a set of capabilities, most commonly called tools, that the model can call.

A basic MCP server includes:

• A transport layer (stdio, HTTP, or WebSocket)

• A protocol handler that parses JSON-RPC requests

• A capability registry that lists available tools

• A dispatcher that routes requests to the correct function

Each tool is defined using a JSON schema for its inputs and outputs. These schemas are included in the server metadata so the model knows how to call the tool.

The communication flow begins when the LLM sends a message to the MCP server. The server responds with a list of available tools. If the model decides to use a tool, it sends a tools/call request with the tool name and arguments. The server executes the tool and returns the result.

Real-time monitoring in this context means observing the server as it processes requests. This includes logging each call, measuring latency, capturing errors, and tracking tool usage.

Essential Tools for MCP Monitoring

Several monitoring platforms can help track MCP server activity. These tools collect logs, metrics, and usage data about tool calls made by language models.

1. Splunk Integration for MCP Servers

Splunk can collect and analyze structured logs from MCP servers. To monitor an MCP server with Splunk:

1. Forward log files to a Splunk index using a universal forwarder

2. Create field extractions for JSON-RPC messages

3. Build dashboards to visualize call frequency and error rates

Splunk's search language (SPL) makes it easy to extract information from MCP logs. For example, this query shows tool call frequency:

index=* sourcetype=mcp-logs "tools/call" 
| rex field=_raw "\"name\":\"(?<tool_name>[^\"]+)\"

Splunk MCP integration works well for security monitoring because it can detect patterns like unexpected file access or suspicious network calls by filtering on tool names and parameters.

2. Azure Monitor Techniques for MCP

Azure Monitor can observe MCP servers hosted on Azure. Logs are sent to Log Analytics workspaces, where you can query them using Kusto Query Language (KQL).

To set up Azure Monitor for an MCP server:

1. Configure the MCP server to emit structured logs

2. Use the Azure Monitor agent to send logs to a workspace

3. Create KQL queries to analyze the logs

Important metrics to track in Azure Monitor include:

• Tool call count

• Average response time

• Error count by method

• Active sessions per hour

This KQL query shows tool usage patterns:

3. Tinybird Analytics for MCP Usage

Tinybird is a platform for real-time analytics using SQL over streamed event data. MCP server logs can be sent to Tinybird using HTTP or OpenTelemetry.

Setting up Tinybird for MCP monitoring involves:

1. Creating a Tinybird workspace

2. Using a logging handler to send events via the Tinybird Events API

3. Defining SQL transformations for metrics

Tinybird works well for high-volume MCP monitoring because it's designed for real-time ingestion and quick queries. Dashboards can be built using Grafana or by exposing Prometheus-format endpoints.

Example SQL to count tool calls:

SELECT
  tool_name,
  COUNT(*) AS call_count
FROM
  mcp_events
WHERE
  event_type = 'tools/call'
GROUP BY

Analyzing Logs and Metrics From MCP Servers

MCP server logs contain structured data about how tools are used. Each log entry typically includes a timestamp, message type (such as tools/call), tool name, arguments, and the result or error response.

A typical MCP server log entry looks like this:

{
  "timestamp": "2025-07-01T14:32:18.234Z",
  "method": "tools/call",
  "params": {
    "name": "getUserInfo",
    "arguments": {
      "user_id": "12345"
    }
  },
  "id": "req-8723",
  "response_time_ms": 212,
  "result": {
    "name": "John Doe",
    "email": "[email protected]"
  }
}

The most important metrics to track from these logs include:

• Request volume: Number of tools/call invocations per tool or time window

• Response times: How long each request takes to complete

• Error rates: Percentage of requests that return errors

• Tool selection patterns: Which tools are called most often and in what sequence

Some log patterns that might indicate problems:

• Sudden increases in error responses from a specific tool

• Long response times on certain operations

• Repeated identical requests without variation

At Stainless, when we generate SDKs for APIs that are also exposed via MCP, we include a logging layer that captures structured telemetry for each tool call. This helps developers analyze usage patterns and error trends using the same log structure whether the request came from an SDK or an LLM.

Securing MCP Monitoring Deployments

MCP servers often connect to sensitive systems, and their monitoring data may contain information useful to attackers. Securing your monitoring setup helps limit the risk of unauthorized access or data exposure.

Key security considerations include:

• Authentication: Use token-based authentication with short-lived credentials to ensure only trusted users or systems can access monitoring data.

• Encryption: Protect data in transit using TLS and encrypt stored logs using platform-level encryption or customer-managed keys.

• Access control: Implement role-based access control (RBAC) to limit who can view or change monitoring data.

This table compares security features across common monitoring tools:

Scaling Real-Time Monitoring for Complex MCP Environments

Monitoring a single MCP server is straightforward, but scaling across many servers introduces challenges related to data volume and system coordination.

High-volume environments generate many tools/call events. Without filtering or aggregation, this data can overwhelm logging systems and make dashboards slow.

Some effective scaling strategies include:

• Sampling: Instead of logging every tool call, sample a percentage of requests or aggregate metrics before sending them downstream.

• Distributed monitoring: Use multiple collectors that run alongside each MCP server instance and forward logs to a central system.

• Alert prioritization: Group alerts by tool category or response code to reduce noise, and tune thresholds based on tool importance.

At Stainless, our SDKs emit telemetry in a consistent format across languages and environments. This makes it easier to correlate logs from different services, especially when MCP servers are part of larger systems.

Advanced MCP Monitoring

MCP monitoring focuses on tracking how language models interact with external tools through MCP servers. It includes observing tool calls, measuring response times, analyzing logs for errors, and maintaining security controls.

A basic implementation checklist includes:

• Log all MCP requests and responses in a structured format

• Capture tool name, parameters, response time, and error messages

• Route logs to systems like Splunk, Azure Monitor, or Tinybird

• Set up dashboards to visualize request volume and latency

• Apply encryption and access control to monitoring infrastructure

A phased approach works best: start with local logging, add structured log forwarding to an observability backend, build queries and dashboards, configure access control, and then extend to multi-server environments.

Stainless generates SDKs that support MCP-compatible APIs with structured telemetry and consistent tool exposure. These SDKs allow teams to observe tool usage across environments using the same log format and metadata.

Get started with Stainless

Frequently Asked Questions About MCP Monitoring

How is MCP monitoring different from traditional API monitoring?

MCP monitoring tracks how AI models select and call tools during a session, while traditional API monitoring focuses on HTTP endpoints and client interactions. MCP uses JSON-RPC communication rather than REST, and monitors tool invocation patterns rather than fixed endpoint usage.

What security risks are specific to MCP servers?

MCP servers face unique security challenges including prompt injection (malicious input designed to force unintended tool calls), unauthorized tool access, data leakage during tool execution, and overbroad permissions that allow unintended system access.

How can I implement MCP monitoring in my existing infrastructure?

You can implement MCP monitoring by configuring your server to emit structured logs, forwarding those logs to an observability platform like Splunk or Azure Monitor, and creating dashboards to track metrics like tool usage, error rates, and response times.