Troubleshooting MCP Retry Failures After Cancellation With LLM And Custom Servers

Understanding the Issue: MCP Server and LLM Errors

Hello, I'm encountering an issue with my custom MCP (Multi-Call Processing) server, which offers tools like list_schemas and use_schema. When the LLM (Language Model) interacts with the use_schema tool and makes an error, the system displays a message indicating a missing 'parameters' field. Specifically, I receive this message on the screen:

I apologize for the error. I missed the required 'parameters' field in the execute_script_mcp_myserv function call. Here's the corrected call:
[execute_script_mcp_myserv(script_path="example/N_rnd", parameters={"length": 5, "max": 100, "min": 1, "int": true})]

This message appears as the final answer, suggesting that the response is directly sent to the user after cancellation, preventing the LLM from correcting its error. The core question is: Is there a way to ensure the requested tool runs despite the initial error and cancellation? This involves understanding how to enable the LLM to retry or correct its function calls, particularly when interacting with custom MCP servers.

The current behavior interrupts the intended workflow, as the LLM doesn't get a chance to rectify its mistake and execute the tool correctly. For instance, if the LLM initially formulates an incorrect execute_script_mcp_myserv call, it recognizes the error and proposes a corrected version. However, this corrected version isn't executed, leaving the user with an unfulfilled request. This issue is critical because it impacts the reliability and efficiency of the entire system. The primary goal is to facilitate a mechanism where the LLM can learn from its errors and proceed with the corrected function call, thus ensuring the successful execution of the requested tool. This might involve modifying the error-handling process to allow for retries or adjustments, or implementing a feedback loop where the LLM receives information about the error and can act upon it.

Analyzing the Logs and Debugging

To further diagnose this problem, reviewing the logs provides valuable insights. The logs show the interaction between the LLM and the MCP server, including the messages exchanged and the timestamps. Specifically, the logs reveal that messages are being truncated, making it difficult to trace the complete sequence of events and understand the context in which errors occur. This truncation is evident in the following log snippets:

2025-07-06T07:44:06.455Z debug: [MCP][User: 686366d4837a0665571d82c7][myserv] Transport sending: {"method":"tools/call","params":{"name":"list_scripts","arguments":{}},"jsonrpc":"2... [truncated]
...
2025-07-06T07:44:08.679Z debug: [MCP][User: 686366d4837a0665571d82c7][myserv] Transport sending: {"method":"tools/call","params":{"name":"get_script_schema","arguments":{"script_pa... [truncated]
2025-07-06T07:44:16.450Z debug: [MCP][User: 686366d4837a0665571d82c7][myserv] Transport sending: {"jsonrpc":"2.0","method":"notifications/cancelled","params":{"requestId":5,"reason... [truncated]
2025-07-06T07:44:16.451Z debug: [MCP][User: 686366d4837a0665571d82c7][myserv] Transport sending: {"jsonrpc":"2.0","method":"notifications/cancelled","params":{"requestId":8,"reason... [truncated]

The truncated messages obscure the full details of the tools/call and notifications/cancelled methods, making it harder to pinpoint the exact cause of the cancellation and the nature of the error. To address this, it's essential to find a way to prevent log truncation and capture the complete messages. This would allow for a more thorough analysis of the interactions between the LLM and the MCP server. Furthermore, enabling LLM debug logging for custom endpoints is crucial. Debug logs can provide insights into the LLM's decision-making process, the parameters it's using, and any internal errors or warnings it might be encountering. This additional information is invaluable for troubleshooting and improving the LLM's performance. Therefore, resolving the log truncation issue and enabling comprehensive debug logging are key steps in addressing the broader problem of MCP retry failures.

Side Questions: Log Truncation and LLM Debugging

In addition to the main issue, I have two side questions that are crucial for effective debugging:

1. How can I prevent logs from being truncated? Truncated logs make it difficult to understand the full context of the interaction between the LLM and the MCP server. Full logs are essential for identifying the root cause of errors and ensuring the system functions as expected.
2. How can I enable LLM debug logging for custom endpoints? Debugging information from the LLM itself would be extremely valuable in understanding why it's making certain decisions and how it's interacting with the custom MCP server. This debug logging would help in diagnosing issues and optimizing the system's performance.

These side questions highlight the need for better diagnostic tools and logging capabilities. Ensuring that logs are complete and that LLM debugging is enabled will significantly enhance the ability to troubleshoot and resolve issues related to MCP retries and other interactions between the LLM and custom endpoints. The ability to view full logs and debug information from the LLM will provide a more comprehensive understanding of the system's behavior, ultimately leading to more effective solutions and a more robust system.

Potential Solutions and Strategies for MCP Retry

To address the core issue of MCP retry failures after cancellation, several strategies and solutions can be considered. The primary goal is to ensure that the LLM has the opportunity to correct errors and successfully execute the requested tool. This requires a multi-faceted approach that considers error handling, feedback mechanisms, and potential modifications to the LLM's interaction with the MCP server.

Implementing a Retry Mechanism

One potential solution is to implement a retry mechanism within the system. When the LLM makes an error, instead of immediately sending the corrected call to the user, the system should attempt to re-execute the tool with the corrected parameters. This retry mechanism could be configured with a maximum number of attempts to prevent infinite loops. The key here is to intercept the error, provide the LLM with feedback, and allow it to try again. This approach aligns with robust error-handling practices and can significantly improve the system's reliability. The retry mechanism could also incorporate a delay between attempts, giving the LLM time to process the feedback and adjust its approach. Furthermore, logging each retry attempt and its outcome would provide valuable data for analysis and future improvements.

Enhancing Error Feedback

Improving the error feedback provided to the LLM is crucial. The current system displays an error message and the corrected call, but it doesn't ensure that the corrected call is executed. The feedback mechanism should be enhanced to actively inform the LLM about the error in a structured format, allowing it to understand the specific issue and adjust its subsequent calls. This could involve providing a detailed error code or message that the LLM can parse and act upon. Additionally, the feedback should include contextual information, such as the original call, the erroneous parameters, and the expected format. This comprehensive feedback will enable the LLM to make more informed decisions and reduce the likelihood of repeating the same errors. The enhanced feedback mechanism should also be designed to be adaptive, learning from past errors and adjusting its strategies accordingly.

Modifying the Interaction Flow

The interaction flow between the LLM and the MCP server might need adjustments to better handle errors. Currently, the cancellation appears to prematurely terminate the process, preventing the LLM from utilizing its corrected call. Modifying the flow to allow the LLM to resubmit the corrected call after cancellation could resolve this issue. This could involve introducing an intermediate step where the LLM's proposed correction is validated before being sent to the user. If the validation is successful, the corrected call is executed; otherwise, further error handling or feedback mechanisms are triggered. This modified flow would ensure that the LLM has a chance to rectify its mistakes and complete the intended task. It would also align with the principles of iterative problem-solving, where errors are seen as opportunities for learning and improvement.

Debugging Custom Endpoints and Logs

Addressing the side questions regarding log truncation and LLM debugging is also essential. To prevent log truncation, the system's logging configuration needs to be adjusted to capture full messages. This may involve increasing the buffer size for log messages or implementing a more efficient logging mechanism that avoids truncation. For LLM debug logging, custom endpoints need to be configured to output detailed debug information. This could involve modifying the LLM's configuration or adding specific debug logging statements to the custom endpoint's code. The debug logs should include information about the LLM's internal state, the parameters it's using, and any errors or warnings it encounters. Comprehensive logging and debugging capabilities are crucial for understanding the system's behavior and identifying the root causes of issues. By addressing these side questions, the overall debugging process can be significantly improved, leading to more effective solutions for MCP retry failures.

Conclusion and Next Steps

The issue of MCP retry failures after cancellation is a significant challenge that requires a systematic approach to resolve. The current behavior, where the LLM's corrected call is not executed after an error, hinders the system's ability to perform its intended tasks. To address this, several potential solutions and strategies have been discussed, including implementing a retry mechanism, enhancing error feedback, and modifying the interaction flow between the LLM and the MCP server. Additionally, resolving the side questions related to log truncation and LLM debugging is crucial for effective troubleshooting and system optimization. The next steps involve implementing these solutions and strategies, conducting thorough testing, and continuously monitoring the system's performance. By addressing these issues, the system can be made more robust, reliable, and efficient, ensuring that the LLM can effectively interact with custom MCP servers and execute requested tools successfully.

Moving forward, a phased approach to implementing these solutions would be beneficial. Initially, focusing on enhancing the error feedback mechanism and implementing a basic retry mechanism could provide immediate improvements. Simultaneously, addressing the log truncation issue and enabling LLM debug logging would lay the foundation for more comprehensive debugging and analysis. Subsequently, the interaction flow between the LLM and the MCP server can be modified to ensure that the LLM has the opportunity to resubmit corrected calls. Regular monitoring and analysis of the system's performance will be essential to identify any remaining issues and make further optimizations. By taking a proactive and iterative approach, the system can be continuously improved, ensuring that it meets the evolving needs of its users and stakeholders.