Semantic Frame
Shrink huge numerical datasets in agent context by semantic compression so you spend fewer tokens per analysis turn.
Overview
semantic-frame is a MCP server for the Build phase that semantically compresses numerical data so agents use far fewer tokens per dataset.
What is this MCP server?
- Semantic compression tuned for numerical/tabular data
- Advertised 95%+ token reduction for compatible payloads
- PyPI package semantic-frame 0.2.1 with stdio MCP transport
- Helps agents summarize series and matrices without raw dump paste
- Open-source server at Anarkitty1/semantic-frame
- Package version 0.2.1 on PyPI (identifier: semantic-frame)
- README claims 95%+ token reduction for numerical semantic compression
- Transport: stdio MCP
Community signal: 1 GitHub stars.
What problem does it solve?
Feeding raw numerical tables into agents wastes tokens and makes multi-step analysis unreliable before you ship data-aware features.
Who is it for?
Builders prototyping AI analytics, experiment trackers, or ops assistants where numeric time series dominate agent prompts.
Skip if: Pure text/code repos with no numeric payloads, or workflows that already query a warehouse via SQL MCP only.
What do I get? / Deliverables
After registration, agents can request compressed semantic frames instead of full numeric dumps, stretching context for deeper follow-up questions.
- Running semantic-frame MCP server via stdio
- Agent workflows that ingest compressed frames instead of full numeric blobs
Recommended MCP Servers
Journey fit
Semantic Frame fits Build when you are shaping how agents consume data—token budget is a build-time design constraint for analytics and ML-assisted features. Agent-tooling hosts MCP servers that change what fits in context windows, which is core to reliable agent workflows.
How it compares
Token compression MCP for numbers, not a full database connector or visualization skill.
Common Questions / FAQ
Who is io.github.Anarkitty1/semantic-frame for?
Solo builders running MCP agents on metrics-heavy tasks who need smaller, semantic numerical context without losing analytical intent.
When should I use io.github.Anarkitty1/semantic-frame?
While building agent tooling whenever raw arrays or logs would dominate the prompt and you want compressed numerical summaries first.
How do I add io.github.Anarkitty1/semantic-frame to my agent?
Install the semantic-frame PyPI package (0.2.1), configure stdio MCP transport in your client, and point the server entry at that package per host docs.