Persistent Python sandbox for token-efficient codebase exploration in MCP clients
Your AI coding agent spends most of its token budget just reading your code — not reasoning about it. Every grep, file read, and glob result gets dumped into the conversation. On a large codebase, that's 25-35% of your context (and cost) burned on raw data the model never needed to see. RLM Tools gives your agent a persistent sandbox to explore code in. Data stays server-side. Only the…
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Your AI coding agent spends most of its token budget just reading your code — not reasoning about it. Every grep, file read, and glob result gets dumped into the conversation. On a large codebase, that's 25-35% of your context (and cost) burned on raw data the model never needed to see. RLM Tools gives your agent a persistent sandbox to explore code in. Data stays server-side. Only the conclusions come back. That's it. Your agent automatically uses the sandbox for exploration. No config, no…
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