一款模型上下文协议（MCP）服务器，将InfraNodus知识图谱和文本网络分析功能集成到大型语言模型（LLM）工作流和如Claude Desktop等AI助手中。

可在npm获取：https://www.npmjs.com/package/infranodus-mcp-server

概述

InfraNodus MCP 服务器使LLM工作流和AI助手能够利用先进的网络科学算法分析文本，生成知识图谱，检测内容缺口，并识别关键主题和概念。它利用图论和网络分析将非结构化文本转化为结构化洞见。

特色

你可以用它来

• 将你现有的InfraNodus知识图谱与你的大型语言模型工作流程和AI聊天连接起来
• 识别话语中的主要主题簇，同时不遗漏重要细微差别（比标准LLM工作流程更有效）
• 识别任何话语中的内容空白（对内容创作和研究有帮助）
• 从任意文本生成新的知识图谱，并用它们来补充你的大型语言模型（LLM）回答
• 利用知识图谱保存和检索实体及关系

可用工具

1. generate_knowledge_graph

• 将任何文本转换为可视化知识图谱

InfraNodus MCP Server

A Model Context Protocol (MCP) server that integrates InfraNodus knowledge graph and text network analysis capabilities into LLM workflows and AI assistants like Claude Desktop.

Available on npm: https://www.npmjs.com/package/infranodus-mcp-server

Overview

InfraNodus MCP Server enables LLM workflows and AI assistants to analyze text using advanced network science algorithms, generate knowledge graphs, detect content gaps, and identify key topics and concepts. It transforms unstructured text into structured insights using graph theory and network analysis.

Features

You Can Use It To

• Connect your existing InfraNodus knowledge graphs to your LLM workflows and AI chats
• Identify the main topical clusters in discourse without missing the important nuances (works better than standard LLM workflows)
• Identify the content gaps in any discourse (helpful for content creation and research)
• Generate new knowledge graphs from any text and use them to augment your LLM responses
• Save and retrieve entities and relations from memory using the knowledge graphs

Available Tools

1. generate_knowledge_graph

   • Convert any text into a visual knowledge graph
   • Extract topics, concepts, and their relationships
   • Identify structural patterns and clusters
   • Apply AI-powered topic naming
   • Perform entity detection for cleaner graphs

2. analyze_existing_graph_by_name

   • Retrieve and analyze existing graphs from your InfraNodus account
   • Access previously saved analyses
   • Export graph data with full statistics

3. analyze_text

   • Analyze a text, URL, or YouTube transcript
   • Extract and analyze a graph from text or URL; provide either text or url
   • Get topics, clusters, statements, graph structure, and AI summary as requested

4. generate_content_gaps

   • Detect missing connections in discourse
   • Identify underexplored topics
   • Generate research questions
   • Suggest content development opportunities

5. generate_topical_clusters

   • Generate topics and clusters of keywords from text using knowledge graph analysis
   • Make sure to beyond genetic insights and detect smaller topics
   • Use the topical clusters to establish topical authority for SEO
   • Returns AI-generated overviews of the topical clusters (topicalClusterSummaries), summarizing the discourse each cluster represents — useful for SEO-optimized content creation. Enabled by default; set generateTopicalSummaries: false to increase processing speed or if the summary request fails

6. generate_contextual_hint

   • Generate a topical overview of a text and provide insights for LLMs to generate better responses