Gemini does not seems to know much about model context protocol

Understanding the Limitations of Gemini in Model Context Protocol Support

In recent experiences with Gemini, a coding assistance tool, I’ve observed some limitations regarding its understanding of specific protocols, particularly the Model Context Protocol (MCP). As a developer leveraging both the Gemini CLI API and the web-based Gemini Pro platform, I’ve found it to be an invaluable resource for various coding tasks. However, when attempting to employ Gemini to generate boilerplate code for REST services intended to function as an MCP server — especially for integration with Claude Desktop — I encountered unexpected hurdles.

The Challenge of Automating MCP Server Generation

My goal was straightforward: wrap a set of RESTful services into an MCP server. I provided Gemini with an OpenAPI specification for a small collection of fewer than ten services, expecting it to produce relevant code snippets or implementation scaffolding. Surprisingly, Gemini struggled with this task. It defaulted to treating the services as generic REST APIs rather than recognizing the specialized nature of the MCP.

This behavior highlights a broader issue: Gemini’s apparent limited familiarity with the Model Context Protocol’s nuances and specifications. Instead of generating specialized boilerplate or providing guidance aligned with MCP standards, it tended to encapsulate my services as run-of-the-mill REST endpoints, missing the protocol-specific context.

Workarounds and Deep Research

To bridge this gap, I had to manually supplement Gemini’s outputs. This involved consulting MCP-related documentation, exploring GitHub repositories with MCP examples, and conducting in-depth research to inform modifications and adjustments. Only through this effort did I manage to achieve a configuration that met my requirements.

Positive Aspects of Gemini Pro

Despite this specific shortcoming, I remain highly impressed with Gemini Pro 2.5. Its capabilities in troubleshooting legacy code across multiple programming languages are exemplary, making it a valuable asset in complex development environments. It excels in understanding and resolving issues that span different tech stacks, providing detailed guidance that often accelerates development cycles.

Reflections and Questions

My experience has raised questions about the scope of Gemini’s knowledge base, especially regarding emerging or less-common protocols like MCP. Is this limitation a lack of training data, or perhaps a bias ingrained in search algorithms that prioritizes more mainstream technologies?

Has anyone else encountered similar challenges with Gemini when working with protocol-specific frameworks like MCP? Insights or best practices for enhancing automation and support in such contexts would be highly appreciated.

Conclusion

While tools like Gemini are transforming development workflows, reliance on automated code generation remains contingent on the