print(doubled); // [2, 4, 6, 8, 10]
NOTE: MCP did not solve these problems. It’s a client-server protocol for connecting data and tools, not an universal LLM API standard. The practical implication of this is that even if an agent knows how to discover and use tools, it still has to talk to the LLM, which means that the client software (e.g. Cursor, Copilot, Amp, etc) has to dynamically translate that tool’s definition into the proprietary OpenAI, Anthropic, or Gemini JSON payloads we discussed above.
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Additionally, the first IRQ triggered after SCRIRQON is written to will always be that which also has the “color pulse” mentioned above. In this way, the skilled developer can always know what field they’re on.