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Semantic Frame

by Anarkitty1version 0.2.1i★ –iRuns on your computer
Registry nameio.github.Anarkitty1/semantic-frame

Install

Overview

Description from the maker (original English)

Token-efficient semantic compression for numerical data. 95%+ token reduction.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "semantic-frame": {
      "command": "uvx",
      "args": [
        "semantic-frame"
      ]
    }
  }
}

Run this in your terminal

claude mcp add semantic-frame -- uvx semantic-frame

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "semantic-frame": {
      "command": "uvx",
      "args": [
        "semantic-frame"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "semantic-frame": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "semantic-frame"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account Anarkitty1, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

  • More stars than 0% of the other 32497 servers in this category
  • Published to the registry more recently than 1% of the other 32497 servers in this category
  • Basic checks equal to or better than 100% of the other 32497 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Semantic Frame safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.2.1) reached the registry 11 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Zoo MCP

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Spec Driven Development

by formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

Runs on your computer★ 440MITUpdated 14 months ago
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Remote Desktop Commander

by desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Runs in the cloud★ 211Updated yesterday
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Zoo MCP

by KittyCAD

An MCP server that provides access to the Zoo API for various CAD operations and tools.

Runs on your computerUpdated today
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Semantic Frame

Token-efficient semantic compression for numerical data.

Semantic Frame converts raw numerical data (NumPy, Pandas, Polars) into natural language descriptions optimized for LLM consumption. Instead of sending thousands of data points to an AI agent, send a 50-word semantic summary.

The Problem

LLMs are terrible at arithmetic. When you send raw data like [100, 102, 99, 101, 500, 100, 98] to GPT-4 or Claude:

  • Token waste: 1000 data points = ~2000 tokens
  • Hallucination risk: LLMs guess trends instead of calculating them
  • Context overflow: Large datasets fill the context window

The Solution

Semantic Frame provides deterministic analysis using NumPy, then translates results into token-efficient narratives:

from semantic_frame import describe_series

import pandas as pd

data = pd.Series([100, 102, 99, 101, 500, 100, 98]) print(describe_series(data, context="Server Latency (ms)"))

Output:

The Server Latency (ms) data shows a flat/stationary pattern with stable

variability. 1 anomaly detected at index 4 (value: 500.00). Baseline: 100.00 (range: 98.00-500.00).

Result: 95%+ token reduction, zero hallucination risk.

Installation

pip install semantic-frame

Or with uv:

uv add semantic-frame

🤖 Claude Integration (MCP)

Semantic Frame is available on the official MCP Registry, enabling direct integration with Claude.

Claude Code CLI:

claude mcp add semantic-frame

Claude Desktop - Add to your claude_desktop_config.json:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{

"mcpServers": { "semantic-frame": { "command": "uvx", "args": ["--from", "semantic-frame[mcp]", "semantic-frame-mcp"] } } }

Once configured, Claude can use these tools:

  • describe_data - Analyze a single data series
  • describe_batch - Analyze multiple series at once
  • describe_json - Get structured JSON output

Quick Start

Analyze a Series
from semantic_frame import describe_series

import numpy as np

Works with NumPy arrays

data = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]) result = describe_series(data, context="Daily Sales") print(result)

"The Daily Sales data shows a rapidly rising pattern with moderate variability..."

Analyze a DataFrame
from semantic_frame import describe_dataframe

import pandas as pd

df = pd.DataFrame({ 'cpu': [40, 42, 41, 95, 40, 41], 'memory': [60, 61, 60, 60, 61, 60], })

results = describe_dataframe(df, context="Server Metrics") print(results['cpu'].narrative)

"The Server Metrics - cpu data shows a flat/stationary pattern..."

Get Structured Output
result = describe_series(data, output="full")

print(result.trend) # TrendState.RISING_SHARP print(result.volatility) # VolatilityState.MODERATE print(result.anomalies) # [AnomalyInfo(index=4, value=500.0, z_score=4.2)] print(result.compression_ratio) # 0.95

JSON Output for APIs
result = describe_series(data, output="json")

Returns dict ready for JSON serialization

Supported Data Types

  • NumPy: np.ndarray
  • Pandas: pd.Series, pd.DataFrame
  • Polars: pl.Series, pl.DataFrame
  • Python: list

Analysis Features

FeatureMethodOutput
TrendLinear regression slopeRISING_SHARP, RISING_STEADY, FLAT, FALLING_STEADY, FALLING_SHARP
VolatilityCoefficient of variationCOMPRESSED, STABLE, MODERATE, EXPANDING, EXTREME
AnomaliesZ-score / IQR adaptiveIndex, value, z-score for each outlier
SeasonalityAutocorrelationNONE, WEAK, MODERATE, STRONG
DistributionSkewness + KurtosisNORMAL, LEFT_SKEWED, RIGHT_SKEWED, BIMODAL, UNIFORM
Step ChangeBaseline shift detectionNONE, STEP_UP, STEP_DOWN
Data QualityMissing value %PRISTINE, GOOD, SPARSE, FRAGMENTED

LLM Integration

System Prompt Injection
from semantic_frame.interfaces import format_for_system_prompt

result = describe_series(data, output="full") prompt = format_for_system_prompt(result)

Returns formatted context block for system prompts

LangChain Tool Output
from semantic_frame.interfaces import format_for_langchain

output = format_for_langchain(result)

{"output": "narrative...", "metadata": {...}}

Multi-Column Agent Context
from semantic_frame.interfaces import create_agent_context

results = describe_dataframe(df) context = create_agent_context(results)

Combined narrative for all columns with attention flags

Framework Integrations

Anthropic Claude (Native Tool Use)
pip install semantic-frame[anthropic]
import anthropic

from semantic_frame.integrations.anthropic import get_anthropic_tool, handle_tool_call

client = anthropic.Anthropic() tool = get_anthropic_tool()

response = client.messages.create( model="claude-sonnet-4-20250514", max_tokens=1024, tools=[tool], messages=[{"role": "user", "content": "Analyze this sales data: [100, 120, 115, 500, 118]"}] )

Handle tool use in response

for block in response.content: if block.type == "tool_use" and block.name == "semantic_analysis": result = handle_tool_call(block.input) print(result)

LangChain
pip install semantic-frame[langchain]
from semantic_frame.integrations.langchain import get_semantic_tool

tool = get_semantic_tool()

Use as a LangChain BaseTool in your agent

CrewAI
pip install semantic-frame[crewai]
from semantic_frame.integrations.crewai import get_crewai_tool

tool = get_crewai_tool()

Use with CrewAI agents

MCP (Model Context Protocol)
pip install semantic-frame[mcp]

Run the MCP server:

mcp run semantic_frame.integrations.mcp:mcp

Exposes describe_data tool for MCP clients like:

  • ElizaOS: TypeScript-based agent framework
  • Claude Desktop: Anthropic's desktop app
  • Claude Code: Anthropic's CLI for Claude
  • Any MCP-compatible client
Claude Code

Add Semantic Frame as a native tool in Claude Code:

# Install MCP dependencies

pip install semantic-frame[mcp]

Add MCP server to Claude Code

claude mcp add semantic-frame -- uv run --project /path/to/semantic-frame mcp run /path/to/semantic-frame/semantic_frame/integrations/mcp.py

Restart Claude Code, then verify connection

claude mcp list

semantic-frame: ... - ✓ Connected

Once configured, ask Claude to analyze data and it will use the describe_data tool automatically.

Advanced Tool Use (Beta)

Semantic Frame supports Anthropic's Advanced Tool Use features for efficient tool orchestration in complex agent workflows.

Features
FeatureBenefitAPI
Input Examples+18% parameter accuracyIncluded by default
Tool Search1000+ tools without context bloatdefer_loading=True
Programmatic CallingBatch analysis via code executionallowed_callers=["code_execution"]
Quick Start (Advanced)
import anthropic

from semantic_frame.integrations.anthropic import get_advanced_tool, handle_tool_call

client = anthropic.Anthropic() tool = get_advanced_tool() # All advanced features enabled

response = client.beta.messages.create( betas=["advanced-tool-use-2025-11-20"], model="claude-sonnet-4-5-20250929", max_tokens=4096, tools=[ {"type": "tool_search_tool_regex_20251119", "name": "tool_search"}, {"type": "code_execution_20250825", "name": "code_execution"}, tool, ], messages=[{"role": "user", "content": "Analyze all columns in this dataset..."}] )

Configuration Options
from semantic_frame.integrations.anthropic import (

get_anthropic_tool, # Standard (includes examples) get_tool_for_discovery, # For Tool Search get_tool_for_batch_processing, # For code execution get_advanced_tool, # All features enabled )

MCP Batch Analysis
from semantic_frame.integrations.mcp import describe_batch

Analyze multiple series in one call

result = describe_batch( datasets='{"cpu": [45, 47, 95, 44], "memory": [60, 61, 60, 61]}', )

See docs/advanced-tool-use.md for complete documentation.

Use Cases

Crypto Trading
btc_prices = pd.Series(hourly_btc_prices)

insight = describe_series(btc_prices, context="BTC/USD Hourly")

"The BTC/USD Hourly data shows a rapidly rising pattern with extreme variability.

Step up detected at index 142. 2 anomalies detected at indices 89, 203."

DevOps Monitoring
cpu_data = pd.Series(cpu_readings)

insight = describe_series(cpu_data, context="CPU Usage %")

"The CPU Usage % data shows a flat/stationary pattern with stable variability

until index 850, where a critical anomaly was detected..."

Sales Analytics
sales = pd.Series(daily_sales)

insight = describe_series(sales, context="Daily Revenue")

"The Daily Revenue data shows a steadily rising pattern with weak cyclic pattern

detected. Baseline: $12,450 (range: $8,200-$18,900)."

IoT Sensor Data
temps = pl.Series("temperature", sensor_readings)

insight = describe_series(temps, context="Machine Temperature (C)")

"The Machine Temperature (C) data is expanding with extreme outliers.

3 anomalies detected at indices 142, 156, 161."

📈 Trading Module (v0.4.0)

Specialized semantic analysis for trading agents, portfolio managers, and financial applications.

Trading Tools
ToolDescription
describe_drawdownEquity curve drawdown analysis with severity
describe_trading_performanceWin rate, Sharpe, profit factor metrics
describe_rankingsMulti-agent/strategy comparison
describe_anomaliesEnhanced anomaly detection with PnL context
describe_windowsMulti-timeframe trend alignment
describe_regimeMarket regime detection (bull/bear/sideways)
describe_allocationPortfolio allocation suggestions ⚠️
Quick Examples
from semantic_frame.trading import (

describe_trading_performance, describe_drawdown, describe_regime, describe_allocation, )

Trading Performance

pnl = [100, -50, 75, -25, 150, -30, 80] result = describe_trading_performance(pnl, context="My Bot") print(result.narrative)

"My Bot shows good performance with 57.1% win rate. Profit factor: 2.53..."

Drawdown Analysis

equity = [10000, 10500, 10200, 9800, 9500, 10100] result = describe_drawdown(equity, context="Strategy") print(result.narrative)

"Strategy max drawdown: 9.5% (moderate). Currently recovering..."

Market Regime

returns = [0.01, 0.015, 0.02, -0.01, 0.025, 0.018] # Daily returns result = describe_regime(returns, context="BTC") print(result.narrative)

"BTC is in a strong bullish regime. Conditions favor trend-following..."

Portfolio Allocation (⚠️ Educational only, not financial advice)

assets = {"BTC": [40000, 42000, 44000], "ETH": [2500, 2650, 2800]} result = describe_allocation(assets, method="risk_parity") print(result.narrative) #

Only the beginning is shown; read the rest in the repo

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