Token Overload: MCP Prompts And AI Server Crashes
Hey guys, let's dive into a head-scratcher. We're looking at a situation where using MCP feature prompts in CherryHQ (version v1.6.7 on macOS) is causing a massive token submission, close to 8000 tokens, and ultimately crashing the AI server. This issue is a real pain, especially when non-MCP prompts are working just fine. The problem is critical, as it's blocking progress due to the server crashing from memory overload. I'll break down the issue, why it's happening, and what we can do to fix it. We need to figure out what's causing this token inflation and get things back on track.
First off, the core of the problem revolves around the number of tokens being submitted. When a user activates an MCP feature prompt, the system is somehow generating an excessive number of tokens. In the provided logs, the request includes a system message that's being sent with each prompt. The log shows a POST request to /v1/chat/completions, using a model like openai/gpt-oss-120b. The body of the request includes the messages array, which contains the system and user prompts. The system prompt contains the peculiar phrase: "In this environment you have access to a set of to...
This system message appears to be the culprit. The truncation in the logs suggests that it's a longer piece of text, and if this text is excessively long, it could easily balloon the number of tokens. The user prompt "尝试抓取https://ollama.com/library的数据，分析后生成excel数据，保持在~/temp下" (which translates to "Try to scrape data from https://ollama.com/library, analyze it, and generate excel data, save it under ~/temp") is relatively short. Therefore, the issue isn't the user's input, but rather the system's prompt used with the MCP feature. The fact that the server memory surges until it crashes indicates the resource-intensive nature of handling a large number of tokens. This can be caused by the sheer volume of text being processed, resulting in increased memory usage and subsequent server failure. I am sure that we need a more efficient way to process the MCP prompts or find a way to reduce the number of tokens being submitted.
Deep Dive into the Issue: Decoding the Token Inflation
Now, let's explore the specifics of why this token submission is so high. When using MCP feature prompts, the system is clearly struggling with efficiency, leading to the substantial token count. The problem could stem from several factors, including the length and complexity of the system prompt itself, which may be exceeding the system's token limits. If the prompt contains unnecessary information or redundant instructions, it will increase the number of tokens being processed without adding any real value. It's likely that a mechanism is adding a very long system message with the MCP feature prompts.
Another cause could be the way the system is handling the prompts. Incorrectly formatted or inefficiently processed prompts can lead to a significant increase in token count. For example, if the system is repeatedly sending the same context with each request, the number of tokens would steadily climb with each interaction. The integration between the MCP feature and the OpenAI model might also be the problem. If there's a mismatch or inefficiency in how the prompts are being sent or interpreted by the OpenAI model, this can lead to unexpected token inflation. Maybe we need a more efficient method of transferring the system prompts with the user input.
One thing to note is that the user's request involves scraping data and generating an Excel file. This task itself could contribute to the token usage, but the primary cause seems to be the system prompt. It would be valuable to examine the code where the MCP feature prompts are defined and processed. Pinpointing the length of the system prompt and identifying if it is being unnecessarily repeated is crucial. In essence, the goal is to pinpoint the exact section of code generating the high token count and optimize it. The objective is to identify and eliminate redundancies, streamline the formatting of the prompts, and make sure that we're only sending essential information. I believe that these techniques will help us to mitigate the token inflation and prevent server crashes.
Troubleshooting Steps and Solutions
Let's get practical and consider some troubleshooting steps and potential solutions to this token explosion problem. Here's a structured approach we can take to fix it, guys:
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Examine the System Prompt: The initial step is to thoroughly review the system prompt used with the MCP feature. Is it overly long? Does it contain any unnecessary details? We want to identify the exact content of the system prompt to understand its size and structure. In cases where the system prompt includes verbose instructions or context, consider shortening it by removing redundant information. The focus should be on delivering only essential instructions, since this will directly cut the token count. Also, make sure that the system prompt is not being sent repeatedly with each request. If so, a more efficient way to manage the context could be used.
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Code Review and Optimization: We need to scrutinize the code that generates the MCP prompts. Check how these prompts are constructed and sent to the API. Are there any loops or redundant operations contributing to extra tokens? You can optimize the prompt generation by using better coding practices that will help to save tokens. Using efficient data structures and algorithms will minimize the memory footprint and the token count. If there are any areas where the prompt can be built more efficiently, refactor the code to improve its performance. The aim is to make sure every part of the prompt is lean and necessary.
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Token Limit Management: Consider adding token limit controls to prevent excessive token submissions. You can set a maximum token limit to prevent requests that might overload the AI server. You should also introduce a warning system to notify the users when a prompt might exceed the allocated limit. This will help them adjust their prompts as needed, promoting efficient and responsible use of the MCP feature. You can also monitor the token usage of the different requests to identify any patterns of inflation and resolve them. The goal is to enforce limits and provide safeguards that will help prevent issues.
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API Configuration: Make sure you're using the right settings in your API requests. Review the parameters set to manage the temperature, max tokens, and other settings. You can make adjustments to these configurations to help manage the token usage. Make sure you understand the effects of your parameter settings on your overall token count and performance. To get the best results, you need to find the balance between model accuracy and token usage.
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Test Thoroughly: After implementing any changes, it's very important to test the MCP feature thoroughly. Try different prompts and scenarios to confirm that the token count has decreased and the AI server is stable. Monitor the server's resource usage, and keep testing until you are sure the problem has been resolved. The aim is to ensure the changes are successful and the MCP feature operates efficiently.
A Bit of Humor
I really like the joke "In this environment you have access to a set of to...
Conclusion: Solving the Token Overload
In conclusion, addressing the excessive token submission in MCP feature prompts is crucial to prevent the AI server from crashing. By optimizing prompts, reviewing code, and managing token limits, we can solve this problem. Careful code review and prompt refinement are key to making sure the MCP feature is useful and doesn't overload the system. I encourage everyone to follow the suggestions I have mentioned. I hope the solutions bring the necessary stability and efficiency.