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Losslessly compress text for LLMs, saving 2x-8x tokens with exact reconstruction.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "densely" yet — see the docs or source repo.
Use densely to compress this long prompt, minimizing tokens while preserving exact reconstruction.
A compact compressed text suitable for a context window.
Compress this file content with densely and preserve exact slices for later expansion.
A compressed representation that can be expanded back to the original.
Use densely to expand the specified slice of this compressed content and verify sha256 consistency.
The original slice plus a checksum consistency result.
When prompts, notes, or documents are too long, developers can compress them before sending to the model to reduce token usage. It suits cases where exact restoration matters.
When a specific portion of compressed text is needed, you can expand an exact slice instead of reprocessing the whole text. The tool also verifies with sha256.
In an MCP-compatible client, you can integrate compression and expansion into your workflow as part of context management.
densely is a lossless context compression tool for LLMs. It reduces token usage and supports byte-exact reconstruction. It exposes MCP tools for compressing files/text and expanding exact slices.
It is not summarization; it is lossless compression. The goal is to minimize tokens while preserving exact reconstruction of the original text. The description also mentions sha256 verification.
The provided information only says it is an MCP tool with compression and expansion capabilities. No additional installation or runtime requirements are specified.
Compress content through MCP to reduce context size in AI workflows.
Compresses LLM conversation context while preserving meaning and reducing token usage.
Compress prompts, tool outputs, and replies to reduce LLM token costs.
Compress MCP tool schemas to cut tokens while preserving semantics deterministically.
Compress context and persist checkpoints to cut AI agent token usage.
Safely call CompText to create context packs and replay digests.