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Compress prompts, tool outputs, and replies to reduce LLM token costs.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "llmtrim" MCP server from askskill: Run: claude mcp add 'io-github-fkiene-llmtrim' -- npx -y @llmtrim/cli
Developers can use it in MCP-based flows to compress prompts, tool outputs, and replies, reducing total token usage. This is especially useful for multi-step workflows with long context.
When a team forwards LLM requests through a proxy layer, it can compress content in transit. This helps lower usage costs and reduce overly long context.
It is an MCP server and proxy that compresses LLM prompts, tool outputs, and model replies to reduce token costs.
According to the description, it compresses prompts, tool output, and replies. That means it covers the main text exchanged before and after model calls.
The provided materials do not include installation steps or dependency requirements. See the source repository for setup details.
Cut AI API costs dramatically with token measurement, compression, caching, and pruning.
Compress long contexts and retrieve reusable summaries to reduce LLM token usage.
Compress MCP tool schemas to cut tokens while preserving semantics deterministically.
Aggregate multiple MCP servers into one endpoint for unified LLM access.
Proxy multiple MCP servers while reducing token usage with on-demand tool loading.
Use LiteLLM proxy tools for completions, embeddings, images, and admin tasks.