# How to Enable Debug Logging for MCP Operations in the Dify MCP SSE Plugin

> Learn to enable debug logging for Dify MCP SSE plugin operations. This guide details using Python's logging module and setting log levels for effective troubleshooting.

- Repository: [Junjie.M/dify-plugin-tools-mcp_sse](https://github.com/junjiem/dify-plugin-tools-mcp_sse)
- Tags: how-to-guide
- Published: 2026-03-05

---

**The Dify MCP SSE plugin uses Python’s standard `logging` module with a custom Dify handler, and developers can enable debug logging by setting the logger level to `DEBUG` in [`utils/mcp_client.py`](https://github.com/junjiem/dify-plugin-tools-mcp_sse/blob/main/utils/mcp_client.py) or setting the `DIFY_LOG_LEVEL=DEBUG` environment variable.**

The `junjiem/dify-plugin-tools-mcp_sse` repository provides a Model Context Protocol (MCP) integration for Dify that enables tool discovery and execution over Server-Sent Events (SSE). Understanding how to enable **debug logging for MCP operations** is essential for troubleshooting tool calls, monitoring SSE connections, and diagnosing HTTP transport issues.

## Understanding the Logging Infrastructure

The plugin’s logging system is built on Python’s standard library `logging` module, augmented with a custom handler provided by the Dify core package.

### Core Logger Configuration in utils/mcp_client.py

In [`utils/mcp_client.py`](https://github.com/junjiem/dify-plugin-tools-mcp_sse/blob/main/utils/mcp_client.py), the MCP client initializes a module-level logger with explicit debug configuration:

```python
import logging
from dify_plugin.config.logger_format import plugin_logger_handler

logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
logger.addHandler(plugin_logger_handler)

```

This configuration ensures that all log levels—`debug`, `info`, `warning`, and `error`—are captured and routed through Dify’s unified logging formatter. The `plugin_logger_handler` is imported from `dify_plugin.config.logger_format` and provides consistent formatting across the Dify ecosystem.

### Logger Usage Across Tool Wrappers

The logging infrastructure is utilized across the plugin’s tool implementations. In [`tools/mcp_list_tools.py`](https://github.com/junjiem/dify-plugin-tools-mcp_sse/blob/main/tools/mcp_list_tools.py), errors during tool discovery are logged using the standard logger:

```python
import logging

logger = logging.getLogger(__name__)

# During error handling

logger.error(f"Failed to list tools: {str(e)}")

```

Similarly, [`tools/mcp_call_tool.py`](https://github.com/junjiem/dify-plugin-tools-mcp_sse/blob/main/tools/mcp_call_tool.py) employs the same pattern for logging execution failures, ensuring consistent error reporting across the MCP operation lifecycle.

## Methods to Enable Debug Logging for MCP Operations

Developers can activate debug logging through environment variables, programmatic configuration, or runtime inspection.

### Environment Variable Configuration

The Dify plugin framework respects the `DIFY_LOG_LEVEL` environment variable. Setting this to `DEBUG` ensures the custom handler emits debug records:

```bash

# In .env file or shell environment

export DIFY_LOG_LEVEL=DEBUG

```

When this variable is set before plugin initialization, all MCP operations—including tool discovery, SSE event handling, and HTTP request/response cycles—generate detailed debug output.

### Programmatic Logger Configuration

If the host application overrides the root logger level, developers can re-assert debug logging programmatically:

```python
import logging

# Force DEBUG level for the MCP client module

logging.getLogger('utils.mcp_client').setLevel(logging.DEBUG)

# Verify handler attachment

logger = logging.getLogger('utils.mcp_client')
print([h.__class__.__name__ for h in logger.handlers])

# Expected: includes the Dify custom handler

```

This approach is useful when integrating the plugin into larger applications where logging levels may be managed centrally.

### Adding Persistent File Logging

For debugging production issues, developers can supplement the default stdout handler with a file handler:

```python
import logging

logger = logging.getLogger('utils.mcp_client')

# Add file handler for persistent MCP operation logs

file_handler = logging.FileHandler('mcp_debug.log')
file_handler.setFormatter(logging.Formatter(
    '%(asctime)s %(levelname)s %(name)s %(message)s'
))
logger.addHandler(file_handler)

```

This configuration preserves debug logs across application restarts without interfering with Dify’s standard logging pipeline.

## What Gets Logged at Debug Level

When debug logging is enabled, the MCP client emits comprehensive diagnostic information covering the full operation lifecycle.

### Tool Discovery and Execution

- **Tool listing** (`tools/list`): Logs the complete list of available tools returned by the MCP server, including parameter schemas and descriptions.
- **Tool calling** (`tools/call`): Logs request payloads, execution parameters, and server responses, enabling verification of data serialization.

### SSE Transport and HTTP Details

- **Connection establishment**: Logs SSE endpoint URLs, headers, and connection attempts.
- **Event handling**: Logs received Server-Sent Events, parsing results, and any deserialization errors.
- **HTTP lifecycle**: Logs status codes, response headers, and bodies at `INFO` level, with additional wire-level details at `DEBUG` level.

## Summary

- The Dify MCP SSE plugin uses Python’s standard **`logging`** module with a custom **`plugin_logger_handler`** from the `dify_plugin` package.
- Debug logging is configured in **[`utils/mcp_client.py`](https://github.com/junjiem/dify-plugin-tools-mcp_sse/blob/main/utils/mcp_client.py)** by setting the logger level to **`logging.DEBUG`** and attaching the Dify handler.
- Developers can enable debug output by setting the **`DIFY_LOG_LEVEL=DEBUG`** environment variable or programmatically adjusting the logger level.
- At debug level, the plugin logs comprehensive details including **tool discovery**, **tool execution**, **SSE events**, and **HTTP request/response** cycles.

## Frequently Asked Questions

### How do I check if debug logging is actually enabled for MCP operations?

Inspect the logger configuration at runtime by retrieving the `utils.mcp_client` logger and checking its level and handlers. If `logger.level` equals `10` (the numeric value for `DEBUG`) and the handlers list includes the Dify custom handler, debug logging is active.

### Can I redirect MCP debug logs to a file instead of stdout?

Yes. While the default `plugin_logger_handler` writes to stdout, you can add a `logging.FileHandler` to the `utils.mcp_client` logger. This captures debug output to a persistent file without removing the default Dify handler, ensuring logs appear in both locations.

### Why am I not seeing debug logs even after setting DIFY_LOG_LEVEL=DEBUG?

If the environment variable is set but debug logs are absent, verify that the `dify_plugin` package is properly installed and that no other code is resetting the logger level after initialization. Also check that the logger name matches exactly (`utils.mcp_client` or the appropriate submodule name used in your import).

### Does enabling debug logging impact performance in production?

Debug logging can impact performance because it captures full HTTP request/response bodies and SSE event streams. In high-throughput production environments, consider using `INFO` level for normal operations and enabling `DEBUG` only during troubleshooting sessions, or use the file handler approach to offload I/O from the main execution path.