Building Resend CLI: Why Custom Tooling is the Secret to LLM Efficiency
How we built a specialized CLI for Resend to slash token consumption and give AI agents deterministic control over email workflows.
The Token Tax of Generic Skills
At Maximal Studio, our AI agents live in the terminal. But as we scaled our email automation workflows, we hit a wall: The Token Tax.
When an AI agent uses a generic "skill" to interact with an API (like Resend), it often involves loading massive documentation, multi-step fetch calls, and verbose error handling into the context window. This doesn't just cost money—it degrades the agent's performance by "cluttering" its memory with low-value boilerplate.
To fix this, we built resend-cli.
The Philosophy: Opinionated & Structured
The resend-cli isn't just a wrapper; it's a structured interface designed for machines. We focused on three core factors to save token spend:
1. Zero-Context Execution
Generic skills require the LLM to "understand" how to construct a request. With the Resend CLI, the agent only needs to know a single command structure.
Instead of a 500-token explanation of the Resend SDK, the agent just needs:
resend emails send --to user@example.com --subject "Hi" --html "<h1>Hello</h1>"
2. Pure JSON Output
LLMs are exceptionally good at parsing JSON, but they struggle when APIs return messy HTML or verbose text logs. Every one of the 29 commands in the Resend CLI returns a clean, predictable JSON object. This allows the agent to extract data.id or error.message with surgical precision, reducing the number of "reasoning steps" required to handle a response.
3. Native Authentication
By handling API keys through environment variables or local config files (~/.resend_api_key), we remove the need for the agent to manage sensitive tokens in the conversation history. This keeps the prompt clean and the workflow secure.
Why Custom Tooling Wins for AI
If you are running an AI Studio or an AI Development Agency, building custom tools is no longer optional. Generic agents are expensive and slow. Specialized tools make them lean and fast.
By offloading the "how" to a local CLI tool, we allow the LLM to focus entirely on the "what." This shift from generative interaction to deterministic execution is how we ship 10x faster.
Key Features
- Full API Surface: Emails, Domains, Audiences, Contacts, and Broadcasts.
- Agent-Ready: Designed to be piped into
jqor fed directly back into an LLM session. - Built with TypeScript: Fast, typed, and reliable.
Conclusion
The resend-cli was born from a need to optimize. By reducing the token footprint of our email workflows, we've made our agents more capable and significantly more cost-effective.
Check out the project on GitHub: Shubham-Rasal/resend-cli
Ready to optimize your agentic workflows? Let’s build.