Troubleshooting Gemini Models MCP Call Issues In CodeCompanion A Detailed Solution

July 10, 2025 by StackCamp Team 83 views

CodeCompanion Gemini Models MCP Call Not Working Issue and Solution

Introduction

This article addresses a specific issue encountered while using the Gemini models with CodeCompanion, a Neovim plugin. The problem arises when the Gemini model fails to call tools correctly, particularly within the Model Context Protocol (MCP) environment. This contrasts with the behavior observed when using Copilot models, which function as expected. This article will delve into the details of the problem, provide a step-by-step analysis, and offer a potential solution. We will explore the nuances of the issue and discuss the importance of understanding how language models interact with the MCP to ensure optimal performance. The goal is to equip users with the knowledge to diagnose and resolve similar problems, enhancing their experience with CodeCompanion and language model-assisted coding.

Problem Description

When using the Gemini adapter with CodeCompanion, the model encounters difficulties in calling tools within the MCP environment. Specifically, the Gemini models are unable to utilize tools as expected, unlike Copilot models, which function correctly. This issue manifests when attempting to access resources or use tools associated with MCP servers. For instance, when prompted to retrieve documentation using a specific MCP server, the Gemini model fails to recognize or utilize the available tools effectively. This problem disrupts the workflow and diminishes the effectiveness of using language models for code assistance. The inability of the Gemini model to access and use these tools hinders its ability to provide accurate and context-aware responses, which is crucial for tasks such as documentation retrieval and code generation. Understanding the root cause of this issue is essential for ensuring the seamless integration of language models within the Neovim environment.

Example Scenario

Consider a scenario where a user asks the Gemini model to retrieve documentation about Dapr using a specific MCP server named context7. The user's prompt includes the context <group>mcp</group> to indicate that the request pertains to MCP-related resources. The expected behavior is for the model to utilize the access_mcp_resource tool to fetch the requested documentation. However, the Gemini model responds by stating that it needs the specific URI for the Dapr documentation, despite the context7 MCP server having tools to search for such URLs. This indicates a failure to recognize and utilize the available tools within the MCP environment. Further, when the user explicitly mentions that context7 has a tool to search for the URL, the Gemini model asks for the name of the tool and its input requirements, even though this information should be available within the system prompt. This behavior suggests a disconnect between the Gemini model and the system prompt, which provides details about available tools and their usage. The discrepancy highlights the core issue of the Gemini model not effectively leveraging the MCP toolset, leading to a less efficient and user-friendly experience.

Debug Information

Debugging information reveals that all the necessary details about the tools are indeed being provided to the Gemini model. The debug view showcases the settings, messages, and context passed to the model. The messages array includes the system prompt, user prompts, and model responses. The system prompt explicitly outlines the capabilities of the MCP servers, including the available tools and resources. For example, the context7 MCP server has tools like resolve-library-id and get-library-docs, which are designed to help users find and retrieve documentation. The system prompt also provides detailed instructions on how to use these tools, including their input schemas and expected behavior. Despite this comprehensive information, the Gemini model fails to utilize these tools effectively, as demonstrated in the example scenario. The debug information confirms that the issue is not due to missing information but rather a failure of the Gemini model to properly process and act upon the provided context. This points towards a potential problem in how the model interprets or accesses the system prompt, leading to the observed inability to call the MCP tools correctly. Understanding this discrepancy is crucial for identifying the underlying cause and implementing an effective solution.

Root Cause Analysis

The root cause of the issue appears to be the Gemini model's inability to access or properly interpret the system prompt. The system prompt contains crucial information about the available MCP servers, their tools, and how to use them. Without this information, the Gemini model is essentially operating without the necessary context to effectively interact with the MCP environment. The fact that pasting the content of the system prompt as an additional user prompt resolves the issue further supports this hypothesis. When the system prompt is included in the user prompt, the Gemini model can then access and utilize the tool information, leading to the expected behavior. This suggests that the problem lies in how the Gemini model handles the system prompt separately from the user prompt. It's possible that the model's architecture or configuration does not prioritize or effectively integrate the system prompt context during the tool selection process. Another potential factor could be the way the prompt is formatted or the specific instructions within the system prompt that the Gemini model is not processing correctly. Identifying the specific mechanism behind this failure requires further investigation into the model's internal workings and its interaction with the CodeCompanion plugin. Understanding this root cause is essential for developing a robust and permanent solution to the problem.

Proposed Solution

Based on the analysis, a potential solution is to ensure that the system prompt information is consistently and effectively accessible to the Gemini model. One approach is to modify the CodeCompanion plugin to explicitly include the system prompt content within each user prompt. This can be achieved by prepending the system prompt to the user's query before sending it to the Gemini model. While this workaround is not ideal, as it increases the token usage and potentially impacts response time, it ensures that the model has the necessary context to function correctly. A more robust solution would involve investigating and addressing the underlying issue of why the Gemini model is not properly utilizing the system prompt. This might require adjustments to the model's configuration, architecture, or the way CodeCompanion interacts with the model's API. Another potential avenue is to explore alternative methods of providing context to the model, such as using metadata or structured data formats. Additionally, it would be beneficial to implement a mechanism for automatically detecting and reporting instances where the model fails to access or utilize the system prompt. This would allow for continuous monitoring and improvement of the model's performance within the MCP environment. By implementing these strategies, the Gemini model can be made to work more effectively with CodeCompanion, providing users with a seamless and productive coding experience.

Step-by-Step Guide to Implement the Workaround

To implement the workaround of including the system prompt content within each user prompt, follow these steps:

Identify the System Prompt: Locate the system prompt within the CodeCompanion plugin's configuration or code. This prompt typically contains information about the model's role, capabilities, and instructions for interacting with the MCP environment. In the debug information provided, the system prompt is clearly marked and includes details about the MCP servers, available tools, and their usage.
Modify the CodeCompanion Plugin: Edit the plugin's code to prepend the system prompt to each user query before sending it to the Gemini model. This will likely involve finding the function or method responsible for constructing the prompt and adding the system prompt content at the beginning.
Test the Solution: After making the changes, thoroughly test the solution by interacting with the Gemini model in various scenarios. Focus on cases where the model previously failed to utilize MCP tools correctly. Verify that the model now recognizes and uses the tools as expected.
Monitor Performance: Keep an eye on the model's performance, particularly response time and token usage. Since the workaround increases the length of the prompt, it may impact these metrics. If necessary, explore ways to optimize the prompt or the model's configuration to mitigate any performance issues.

Code Snippet Example (Conceptual)

local function construct_prompt(user_query, system_prompt) 
 local combined_prompt = system_prompt .. "\n" .. user_query
 return combined_prompt
end

-- Example usage
local user_query = "get docs about dapr using context7 mcp server"
local system_prompt = "You are an AI programming assistant... (system prompt content)"
local final_prompt = construct_prompt(user_query, system_prompt)

-- Send final_prompt to the Gemini model

Note: This code snippet is a conceptual example and may require adjustments based on the specific implementation of the CodeCompanion plugin.

Conclusion

The issue of Gemini models failing to call MCP tools within CodeCompanion highlights the importance of ensuring that language models have access to the necessary context. The root cause analysis points to a problem with how the Gemini model processes the system prompt, which contains crucial information about the MCP environment. While a workaround of including the system prompt in each user prompt can mitigate the issue, a more robust solution is needed to address the underlying problem. Further investigation into the model's architecture and its interaction with the CodeCompanion plugin is essential. By implementing appropriate solutions, we can enhance the performance of Gemini models within CodeCompanion, providing users with a more seamless and productive coding experience. This article serves as a starting point for understanding and resolving this specific issue, and the insights gained can be applied to similar problems encountered with other language models and plugins. Continuous monitoring and improvement are crucial for ensuring that language models function effectively within complex environments like the MCP, ultimately empowering developers to leverage these tools to their full potential.

This comprehensive guide not only addresses the technical aspects of the issue but also emphasizes the importance of user experience and continuous improvement in the realm of AI-assisted coding.