Access MCP tools with progressive schema disclosure and on-demand hydration.
npx skills add https://github.com/Gitmaxd/droid-mode --skill droid-mode
Please help me install this Agent Skill. Skill: droid-mode Source: https://github.com/Gitmaxd/droid-mode/tree/main/templates/skills/droid-mode Command: npx skills add https://github.com/Gitmaxd/droid-mode --skill droid-mode
This Skill provides a way to access MCP (Model Communication Protocol) tools without loading their schemas into the LLM's context window, thereby saving tokens and improving efficiency.
Use the droid-mode skill to list available MCP servers by running the command dm servers.
High-intent search queries and answers about installing and using this skill.
To prevent MCP tool schemas from bloating your LLM context window, you can use progressive disclosure to load schemas on demand. This approach keeps tool definitions outside the context until needed, saving tokens and improving efficiency.
Managing multiple MCP servers with large toolsets is best handled through progressive schema discovery and on-demand hydration. This method fetches tool schemas only when required, preventing context overload while maintaining full access to complex toolsets.
Yes, you can execute multi-step MCP workflows outside the LLM context window using procedural JavaScript scripts. This allows complex multi-tool automation to run efficiently without injecting schema definitions into the model's context.
To reduce MCP connection latency for frequent tool calls, enable daemon mode. Daemon mode provides persistent MCP connections, significantly lowering latency and token overhead during continuous tool execution.
No, you do not need to load all MCP tool schemas before discovering servers. You can list available MCP servers using `dm servers` and incrementally discover their tools, fetching full type definitions only when explicitly needed.
Accessing MCP tools exhausts token limits quickly because loading comprehensive tool schemas directly into the LLM context window consumes massive overhead. Shifting to on-demand schema hydration prevents this context bloat and preserves tokens.
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