Hands-On with MCP-Enabled Coding Assistants: Progress and Pain ...

Both tools demonstrated impressive capabilities. They both: - Successfully leveraged MCP to retrieve content from Jira and Figma. - Created reasonable implementation plans based on the issue description. - Analyzed static design images to correctly infer much of the page structure. However, we also encountered significant limitations: - Unfortunately, we had **no success with Continue**. We found the setup and configuration experience extremely frustrating, and were never able to get it fully operational. - Their **context windows are too small**to allow the tools to make broad inferences across all of the screens, and then to hold that information in memory while implementing the changes. When they tried to build components from their own summaries, they hallucinated details that didn’t match the original designs. … - While the tools got broad structure right, they often **struggled with specific details**not matching up with the mockups. Sometimes they’d correct these when prompted, other times they’d persistently misinterpret elements or seem unaware of the discrepancies. - Both tools **frequently invented images and icons**, even when they were able to fetch the mockups from Figma, requiring very explicit instructions and direct links to the exact Figma nodes to correct. - Installing Tailwind 4 (released in January 2025) proved challenging as it’s **too recent for the models’ training data**. Cursor particularly struggled, repeatedly “respectfully disagreeing” with correct installation instructions, even when provided with official documentation links. ## Surprising Wins The more impressive moment came when one of the tools correctly inferred the purpose of disclosure indicators in images of the resume form, and correctly implemented logic for expanding and collapsing each section without any prompting—showing an unexpected understanding of UI patterns.