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Semantic Frame

作者：Anarkitty1版本 0.2.1i★ –i在你的电脑上运行
注册名称io.github.Anarkitty1/semantic-frame

安装

概览

作者提供的说明（英文原文）

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

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 其他

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

  • 基础检查：1 项中通过 1 项
  • 在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
  • 注册中心没有列出需要填写的密钥。

你需要

  • PyPI已安装 Python 的 uv（用于 uvx 命令）

你用的是哪个应用？

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

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

在终端中运行这条命令

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

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

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

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

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

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 Anarkitty1 验证，该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行，没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行，改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“其他”中的其他服务器比较i

  • 星标数多于此分类中其他 32505 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 32505 个服务器的 1% 更新
  • 基础检查结果等于或优于此分类中其他 32505 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Semantic Frame 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（0.2.1）于11个月前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它？

同一分类中有：Spec Driven Development, Remote Desktop Commander, topo^alpin

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Spec Driven Development

作者：formulahendry

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

在你的电脑上运行★ 440MIT14个月前更新
安装

Remote Desktop Commander

作者：desktop-commander

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

在云端运行★ 211昨天更新
安装

topo^alpin

作者：topoalpin.app

Avalanche bulletins and mountain weather forecasts for any place in the Alps.

在云端运行今天更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

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) #

只显示了开头部分；其余内容请在仓库中阅读

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