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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
Use llmtrim to compress this long prompt while preserving key information as much as possible and reducing tokens for a later LLM call: {{long prompt content}}A shorter prompt version suitable for later model requests to reduce cost.
Use llmtrim to compress this tool output, keeping key results and necessary context while reducing tokens sent to the LLM: {{tool output content}}A condensed version of the tool output for the next reasoning step.
Use llmtrim to compress this model reply, keeping the main conclusions and action items to lower the cost of future turns: {{model reply content}}A more compact reply suitable for passing along in the conversation chain.
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.
Compress and proxy MCP responses to reduce token usage for LLM tool calls.
Compress MCP tool schemas to cut tokens while preserving semantics deterministically.
Manage multiple MCP servers and load tool schemas on demand efficiently.
Route LLM completion requests to OpenAI-compatible providers through MCP tools.
Use LiteLLM proxy tools for completions, embeddings, images, and admin tasks.
Offload non-critical LLM tasks to your own model and save premium quota.