Compress numerical datasets into token-efficient semantic frames, achieving 95%+ token reduction. For developers building LLM applications that need to process large numerical datasets without exhausting context windows.
io.github.Anarkitty1/semantic-frame
Local install
STDIO
No auth required
How models use it and what it is built for.
Compress numerical datasets into token-efficient semantic frames, achieving 95%+ token reduction. For developers building LLM applications that need to process large numerical datasets without exhausting context windows.
Local install — runs as a subprocess.
Where to find authoritative docs and source for Anarkitty1 Semantic Frame.
Paste any of these into Agent Studio after connecting Anarkitty1 Semantic Frame.
Common questions about connecting and running Anarkitty1 Semantic Frame.
What kind of numerical data does semantic-frame compress best?
The server is optimized for token-efficient semantic compression of numerical datasets. It achieves 95%+ token reduction, making it ideal for time-series, sensor data, and large numerical tables that would otherwise consume excessive LLM context.
How do I install and run semantic-frame?
Install via `uvx semantic-frame@0.2.1`. The server uses stdio transport, so it integrates directly with MCP clients. Check the project repository for environment variable configuration and detailed setup instructions.
Will I lose data accuracy when compressing with semantic frames?
Semantic frames are designed for token efficiency while preserving the semantic meaning of numerical data. The exact accuracy trade-offs depend on your dataset and compression settings—review the output schema to understand what statistical information is retained.
Can semantic-frame handle real-time streaming data?
The registry metadata doesn't specify streaming capabilities. Check the project documentation or GitHub repository for details on whether semantic-frame supports incremental compression or real-time data ingestion.
What are alternatives to semantic-frame for reducing token usage?
Other approaches include summarization, sampling, or dimensionality reduction libraries. Semantic-frame is unique in targeting 95%+ token reduction specifically for numerical data while maintaining semantic fidelity—making it a specialized choice for LLM context optimization.
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