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Pythagraph RED MCP Server

Pythagraph RED MCP Server

by pythagraph

pythAIs - Pythagraph RED MCP Server

Node.js server implementing Model Context Protocol (MCP) for Pythagraph RED API access.

Features

  • Fetch graph data from Pythagraph RED API

  • Detailed graph analysis with nodes, edges, and statistics

  • Formatted table outputs for easy visualization

  • Comprehensive graph summaries

  • Error handling and timeout management

Related MCP server: Gremlin MCP Server

API Integration

This server connects to the Pythagraph RED API at:

https://red.pythagraph.co.kr/api/red/graph/exportGraphInfo.do?graphId={graphId}

Tools

get_graph_data

Retrieve detailed graph data from Pythagraph RED API. Returns comprehensive information including nodes, edges, statistics, and metadata formatted as tables and descriptions.

Input:

  • graphId (string): The unique identifier for the graph to retrieve

Output:

  • Detailed tables showing graph statistics

  • Node type distributions

  • Edge type distributions

  • First 10 nodes with their properties

  • First 10 edges with their relationships

  • Metadata information

Example:

# MBTI(성질좋은 역순서,점유비율)

## 📊 기본 정보
| 항목 | 값 |
|------|-----|
| Graph ID | G81a6c348-4696-4f04-a164-6e306388ab92 |
| 단위 구분 | 비율 |
| 단위명 | 퍼센트(%) |
| 데이터 건수 | 16건 |

## 📈 데이터 테이블
| 시간 | MBTI유형 | 값 |
|------|----------|-----|
| 15 | ENFP | 12.6% |
| 08 | INFP | 13.4% |
| 04 | INFJ | 6.3% |

get_graph_summary

Get a concise summary of graph data from Pythagraph RED API. Provides overview statistics, node/edge type distributions, and key insights.

Input:

  • graphId (string): The unique identifier for the graph to get summary

  • includeDetails (boolean, optional): Include detailed node and edge information (default: false)

Output:

  • Quick overview with node and edge counts

  • Node and edge type listings

  • Graph density calculation

  • Optional detailed tables when includeDetails is true

Example:

# MBTI(성질좋은 역순서,점유비율) - 요약

📊 Graph ID: G81a6c348-4696-4f04-a164-6e306388ab92
📊 데이터 건수: 16건
📊 단위: 비율 (퍼센트(%))
📅 등록일: 2023-05-22 15:01

## 🔍 핵심 인사이트
🏆 최고: INFP (13.4%)
📉 최저: ENTJ (2.7%)
📊 총합: 100.0%

Usage with Claude Desktop

Add this to your claude_desktop_config.json:

NPX

{
  "mcpServers": {
    "pythAIs": {
      "command": "npx",
      "args": [
        "-y",
        "pythais-mcp-server"
      ]
    }
  }
}

Docker

{
  "mcpServers": {
    "pythAIs": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "mcp/pythais"
      ]
    }
  }
}

Development

Building

npm install
npm run build

Testing

npm test

Docker Build

docker build -t mcp/pythais.

API Response Format

The server expects the Pythagraph RED API to return JSON data in this format:

{
  "graphId": "G81a6c348-4696-4f04-a164-6e306388ab92",
  "graphNm": "MBTI(성질좋은 역순서,점유비율)",
  "graphDet": "<p>MBTI 성격별 인구비율 및 성격더러운 순서</p>",
  "unitDivNm": "비율",
  "unitNm": "퍼센트(%)",
  "link": "https://ddnews.co.kr/mbti-순위/",
  "dataSrc": "https://ddnews.co.kr/mbti-순위/",
  "dataOrg": "https://ddnews.co.kr/mbti-순위/",
  "regUser": "kimhoon1112@gmail.com",
  "regTime": "2023-05-22 15:01",
  "cols": ["시간", "MBTI유형", "값"],
  "cols2": ["T5", "M1", "VALUE"],
  "graphData": [
    ["15", "ENFP", "0.126"],
    ["08", "INFP", "0.134"],
    ["04", "INFJ", "0.063"]
  ],
  "regionList": [],
  "message": "OK"
}

Error Handling

The server includes comprehensive error handling for:

  • Invalid graph IDs

  • Network timeouts (30 second limit)

  • API response errors

  • Invalid JSON responses

  • Connection failures

Features

  • Automatic Table Formatting: Converts graph data into readable tables

  • Statistics Calculation: Computes graph density and type distributions

  • Memory Efficient: Only displays first 10 nodes/edges in detailed view

  • Flexible Output: Summary mode for quick insights, detailed mode for analysis

  • Robust Error Handling: Graceful handling of API failures

License

This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License.

Available Tools

2 tools
get_graph_dataB

Retrieve detailed graph data from Pythagraph RED API. Returns comprehensive information including nodes, edges, statistics, and metadata formatted as tables and descriptions. Perfect for analyzing graph structure and getting detailed insights.

NameRequiredDescriptionDefault
graphIdYesThe unique identifier for the graph to retrieve
get_graph_summaryA

Get a concise summary of graph data from Pythagraph RED API. Provides overview statistics, node/edge type distributions, and key insights without overwhelming detail. Use includeDetails=true for more comprehensive analysis.

NameRequiredDescriptionDefault
graphIdYesThe unique identifier for the graph to get summary
includeDetailsNoInclude detailed node and edge information

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updatesv1.0.0
    • First observedget_graph_data
    • First observedget_graph_summary

TDQS

B3.4/5.0

Scored across 2 tools

Disambiguation4/5

The two tools have distinct purposes: get_graph_data provides detailed, comprehensive graph data, while get_graph_summary offers a concise overview. However, the inclusion of an includeDetails parameter in get_graph_summary could cause some overlap or confusion, as it blurs the line between summary and detailed data retrieval.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern (get_graph_data and get_graph_summary), using snake_case and the same verb 'get'. This makes them predictable and easy to understand, with no deviations in naming style.

Tool Count2/5

With only 2 tools, the server feels thin for a graph analysis domain, as it lacks essential operations like creating, updating, or deleting graph data. This limited scope may hinder agents from performing full workflows, making it inappropriate for comprehensive graph management.

Completeness2/5

The server is severely incomplete for graph analysis, covering only retrieval operations (detailed and summary). There are significant gaps, such as no tools for creating, modifying, or deleting graphs, which are core to graph lifecycle management and will likely cause agent failures in broader tasks.

Maintenance

ActivityInactive
ResponsivenessOutdated data

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