Semantic Sift
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# 🔍 Semantic-Sift **The Reasoning-First Middleware for High-Fidelity Agentic Workflows.** [](https://github.com/luismichio/semantic-sift/actions/workflows/ci.yml)…
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- Author
- luismichio
- Downloads
- 167
- Categories
- Developer Tools, AI, Other, Knowledge Base
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Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Semantic SiftCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
{
"mcpServers": {
"semantic-sift": {
"command": "semantic-sift",
"args": [],
"env": {
"SIFT_ALLOW_GLOBAL_READS": "false"
}
}
}
}
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"semantic sift": {
"semantic-sift": {
"command": "semantic-sift",
"args": [],
"env": {
"SIFT_ALLOW_GLOBAL_READS": "false"
}
}
}
}
}McpServers
{
"semantic-sift": {
"command": "semantic-sift",
"args": [],
"env": {
"SIFT_ALLOW_GLOBAL_READS": "false"
}
}
}🔍 Semantic-Sift
The Reasoning-First Middleware for High-Fidelity Agentic Workflows.
> "It saves tokens while preserving context - maximizing reasoning, minimizing hallucination."
Semantic-Sift is a local Model Context Protocol (MCP) server that acts as an intelligent "Sanitation Tier" between your raw data and your AI’s context window.
While modern LLMs have massive context windows, their reasoning accuracy often degrades as noise increases. Semantic-Sift solves this by distilling technical logs, long-form documents, and chat histories into high-density context using LLMLingua-2. It treats your context window as a precious resource—optimizing for Signal-to-Noise Ratio (SNR) so your models spend more time reasoning and less time navigating boilerplate.
🧠 Philosophy: The Studio of Two
Semantic-Sift is grounded in the Studio of Two philosophy: the belief that the future of engineering is a high-fidelity partnership between a human architect and a sovereign AI sidecar. By managing the friction of raw data ingestion, Sift allows this "Studio" to focus on building systems, not just applying patches. It acts as a cognitive filter that ensures both you and your agent are collaborating on the cleanest, most relevant representation of the technical truth.---
⚡ Quickstart (60 seconds)
```bash
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