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Compress and proxy MCP responses to reduce token usage for LLM tool calls.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-compressor" yet — see the docs or source repo.
Connect this MCP service to mcp-compressor, intercept all tool responses, compress the content to keep only information necessary for downstream reasoning, and report the token difference before and after compression.
Compressed tool responses plus a summary of token savings statistics.
Wrap the existing MCP toolset behind mcp-compressor, do not expose the raw tools directly, and provide only two meta-tools to the model with an explanation of the mapping.
A proxy setup exposing only two meta-tools, with a mapping from original tools to the meta-tools.
Analyze which responses in this MCP-based AI workflow consume the most context window space, and design a compression pipeline with mcp-compressor that prioritizes lower cost while preserving output quality.
Recommendations for a compression pipeline, response types suitable for compression, and cost-versus-quality tradeoff notes.
Compress MCP tool schemas to cut tokens while preserving semantics deterministically.
Compress prompts, tool outputs, and replies to reduce LLM token costs.
Run a local-first MCP proxy with secure discovery and major token savings.
Organize flat MCP tools into subcommands to save tokens and improve discovery.
Manage multiple MCP servers and load tool schemas on demand efficiently.
Aggregate multiple MCP servers into one for search, parallel calls, and orchestration.