Appwrite MCP vs Vercel MCP: I benchmarked both

An MCP server should be evaluated based on what an agent can complete, not merely the number of tools available. To assess the Appwrite and Vercel MCP servers, I instructed an agent to perform identical tasks - deploying identical applications, examining the builds, troubleshooting a deliberate failure, releasing a second version, and recovering from it.

The two servers excelled in different areas, resulting in a split: Appwrite excelled in the backend, providing rollback, deletion, and explicit write confirmation through MCP. Vercel, on the other hand, deployed more quickly and handled runtime failures more effectively.

Both servers ran over OAuth from the same Darwin 25.5 arm64 client, with the first call to each tool discarded to avoid cold start costs. The test encompassed two apps: a static site and a Next.js 15.2.8 SSR app. The agent's tasks included discovering the workspace, deploying both applications, inspecting logs and analytics, recovering from an intentional build failure, publishing version 2, attempting a rollback, and managing 50 requests at concurrency 10.

The stages were uniform for both platforms:

1. Orient workspace + IDs - Vercel ahead with a 4.6 score versus 3.4 for Appwrite.

2. Deploy same source bytes - Vercel led with a READY time of around 2.1 seconds compared to Appwrite's 27 seconds.

3. Break exit code 42 - Both servers identified their own failure.

4. Observe 3×200 · 1×400 · 1×500 - Vercel led with filtered runtime logs at 12.3 KB versus Appwrite's 115 KB.

5. Recover v2 + rollback - Appwrite excelled with a rollback time of 1.32 seconds, while Vercel did not expose this feature.

6. Burst 50 requests · c=10 - Both servers managed this task without errors.

Appwrite's scoring criteria included correctness and completeness at 30%, result compactness, discoverability, and call economy at 20%, median and p95 latency at 15%, and safety and lifecycle control at 15%. The combined scores for both servers were close enough that the difference between them signifies nothing, and the per-category numbers are not documented. The task scores from the read and observability phases are the most relevant.