HasMCP

HasMCP fills a clear gap: getting REST APIs talking to LLMs via the Model Context Protocol without hand-coding a server. If you maintain an OpenAPI spec, you can be up in minutes. The dual-engine interceptor (JMESPath for pruning, Goja JS for PII scrubbing) is the standout feature—it's rare to see token optimization and privacy enforcement baked into the same proxy layer. We'd reach for this when we need to prototype an agentic workflow over existing APIs quickly, especially if we're already in the Claude or Cursor ecosystem. The dashboard makes it easy to see how many calls are hitting each tool and how much token reduction is happening. The 250-call free tier is generous enough to evaluate seriously. Where it bites: the Wrapper Pattern, while clever for large APIs, adds complexity. If your API isn't documented with OpenAPI/Swagger, you're out of luck. And the pricing jumps from free to $24/mo (100k calls) to enterprise—no intermediate tier for mid-volume usage. Self-hosting isn't available on lower tiers either. Compared to alternatives like OpenAI's GPT Actions (which require manual schema writing and don't support MCP natively), or self-hosting MCP servers with something like FastMCP (which requires coding and infrastructure), HasMCP is much faster to start but locks you into their managed cloud. If you need total control or on-premise deployment, wait for the enterprise plan. Real-world caveat: the JMESPath and Goja interceptors are powerful but require learning their DSL or writing small JS snippets. The team has done a good job with the docs, but there's a small learning curve. Also, the CLI/API for automation isn't visible yet—everything is via dashboard. Bottom line: for teams that value time-to-prototype over infrastructure control, HasMCP is a strong