Semantic Frame

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

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

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

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:

server.ts

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:

Code

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

Terminal

pip install semantic-frame

Or with uv:

bash

uv add semantic-frame

🤖 Claude Integration (MCP)

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

Claude Code CLI:

Terminal

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

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

server.ts

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

server.ts

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

python

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

python

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

Feature	Method	Output
Trend	Linear regression slope	RISING_SHARP, RISING_STEADY, FLAT, FALLING_STEADY, FALLING_SHARP
Volatility	Coefficient of variation	COMPRESSED, STABLE, MODERATE, EXPANDING, EXTREME
Anomalies	Z-score / IQR adaptive	Index, value, z-score for each outlier
Seasonality	Autocorrelation	NONE, WEAK, MODERATE, STRONG
Distribution	Skewness + Kurtosis	NORMAL, LEFT_SKEWED, RIGHT_SKEWED, BIMODAL, UNIFORM
Step Change	Baseline shift detection	NONE, STEP_UP, STEP_DOWN
Data Quality	Missing value %	PRISTINE, GOOD, SPARSE, FRAGMENTED

LLM Integration

System Prompt Injection

server.ts

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

server.ts

from semantic_frame.interfaces import format_for_langchain

output = format_for_langchain(result)
# {"output": "narrative...", "metadata": {...}}

Multi-Column Agent Context

server.ts

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)

Terminal

pip install semantic-frame[anthropic]

server.ts

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

Terminal

pip install semantic-frame[langchain]

server.ts

from semantic_frame.integrations.langchain import get_semantic_tool

tool = get_semantic_tool()
# Use as a LangChain BaseTool in your agent

CrewAI

Terminal

pip install semantic-frame[crewai]

server.ts

from semantic_frame.integrations.crewai import get_crewai_tool

tool = get_crewai_tool()
# Use with CrewAI agents

MCP (Model Context Protocol)

Terminal

pip install semantic-frame[mcp]

Run the MCP server:

bash

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:

bash

# 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

Feature	Benefit	API
Input Examples	+18% parameter accuracy	Included by default
Tool Search	1000+ tools without context bloat	`defer_loading=True`
Programmatic Calling	Batch analysis via code execution	`allowed_callers=["code_execution"]`

Quick Start (Advanced)

server.ts

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

server.ts

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

server.ts

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

python

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

python

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

python

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)."

Read the full README →View source on GitHub →

Related MCP Servers

View all in Finance & Fintech View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

9mo ago

Most recent push to the default branch.

Directory activity

2 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Semantic Frame

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "semantic-frame": { "command": "uvx", "args": ["semantic-frame"] } }

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Technical Specs & Signals

CategoryFinance & Fintech

5/6 checks healthy over the last 45d

35Quality signal: Fair · 35/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools15/30

Adoption & activity1/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 3d ago via OSV.dev · semantic-frame (PyPI)

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More in 💰 Finance & Fintech →Best MCP servers for Finance & Fintech →Alternatives to Semantic Frame →Install in Claude Desktop Install in Cursor Install in VS Code Setup guides for all 13 MCP clients

Semantic Frame

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

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

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

Semantic Frame

Token-efficient semantic compression for numerical data.

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:

server.ts

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:

Code

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

Terminal

pip install semantic-frame

Or with uv:

bash

uv add semantic-frame

🤖 Claude Integration (MCP)

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

Claude Code CLI:

Terminal

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

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

server.ts

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

server.ts

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

python

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

python

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

Feature	Method	Output
Trend	Linear regression slope	RISING_SHARP, RISING_STEADY, FLAT, FALLING_STEADY, FALLING_SHARP
Volatility	Coefficient of variation	COMPRESSED, STABLE, MODERATE, EXPANDING, EXTREME
Anomalies	Z-score / IQR adaptive	Index, value, z-score for each outlier
Seasonality	Autocorrelation	NONE, WEAK, MODERATE, STRONG
Distribution	Skewness + Kurtosis	NORMAL, LEFT_SKEWED, RIGHT_SKEWED, BIMODAL, UNIFORM
Step Change	Baseline shift detection	NONE, STEP_UP, STEP_DOWN
Data Quality	Missing value %	PRISTINE, GOOD, SPARSE, FRAGMENTED

LLM Integration

System Prompt Injection

server.ts

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

server.ts

from semantic_frame.interfaces import format_for_langchain

output = format_for_langchain(result)
# {"output": "narrative...", "metadata": {...}}

Multi-Column Agent Context

server.ts

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)

Terminal

pip install semantic-frame[anthropic]

server.ts

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

Terminal

pip install semantic-frame[langchain]

server.ts

from semantic_frame.integrations.langchain import get_semantic_tool

tool = get_semantic_tool()
# Use as a LangChain BaseTool in your agent

CrewAI

Terminal

pip install semantic-frame[crewai]

server.ts

from semantic_frame.integrations.crewai import get_crewai_tool

tool = get_crewai_tool()
# Use with CrewAI agents

MCP (Model Context Protocol)

Terminal

pip install semantic-frame[mcp]

Run the MCP server:

bash

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:

bash

# 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

Feature	Benefit	API
Input Examples	+18% parameter accuracy	Included by default
Tool Search	1000+ tools without context bloat	`defer_loading=True`
Programmatic Calling	Batch analysis via code execution	`allowed_callers=["code_execution"]`

Quick Start (Advanced)

server.ts

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

server.ts

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

server.ts

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

python

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

python

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

python

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)."

Read the full README →View source on GitHub →

Related MCP Servers

View all in Finance & Fintech View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

9mo ago

Most recent push to the default branch.

Directory activity

2 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Semantic Frame

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "semantic-frame": { "command": "uvx", "args": ["semantic-frame"] } }

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

Live Dynamic SVG Preview

Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/semantic-frame?style=directory)](https://allmcps.com/mcp/semantic-frame)

HTML Embed

<a href="https://allmcps.com/mcp/semantic-frame"><img src="https://allmcps.com/api/badge/semantic-frame?style=directory" alt="Semantic Frame on AllMCPs" /></a>

Technical Specs & Signals

CategoryFinance & Fintech

5/6 checks healthy over the last 45d

35Quality signal: Fair · 35/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools15/30

Adoption & activity1/15

Community engagement0/10

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 3d ago via OSV.dev · semantic-frame (PyPI)

★ Featured

Moxie Docs MCP

MCP & Agent Skills for Automated Documentation, and codebase conventions + context

Explore Server →

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.