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Reduce LLM token usage by lazy-loading tools and routing repetitive subtasks.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "clair" yet — see the docs or source repo.
Help me integrate clair into my MCP agent so skills and tools load only when needed, and repetitive classification or extraction subtasks are routed to cheaper ML backends. Provide an integration plan, routing strategy, and sample configuration.
An integration plan to reduce token usage, including lazy-loading rules, subtask routing strategies, and sample configuration.
I have an AI workflow with summarization, tag classification, Q&A, and report generation. Using clair’s approach, decide which steps should go to the LLM and which should go to ML backends, and explain the reasons and expected savings.
A task decomposition and routing recommendation showing execution backends, decision criteria, and cost benefits for each step.
Our MCP toolchain costs too much to run. Based on clair’s model, analyze likely sources of high token usage and recommend optimizations through lazy loading, caching, and offloading repetitive tasks to ML backends.
A cost diagnosis and optimization checklist identifying problem sources and actionable savings measures.
Lets AI ask clarifying questions and collect structured human input.
Offload non-critical LLM tasks to your own model to save premium quota.
Cut AI API costs dramatically with token measurement, compression, caching, and pruning.
Discover, search, and read Claude Agent Skills guidance through MCP tools.
Turn HAR-captured web traffic into callable API tools for AI assistants.
Turn CLI tools or REST APIs into MCP servers for Claude.