Price Per TokenPrice Per Token
InfraNodus Knowledge Graphs & Text Analysis

InfraNodus Knowledge Graphs & Text Analysis

by infranodus

GitHub 61 502 uses Remote
0

About

InfraNodus is a text analysis and knowledge graph platform that transforms unstructured text into structured insights using network science algorithms and Graph RAG technology. It converts documents into visual knowledge graphs to reveal conceptual relationships, topical clusters, and structural patterns. Key features: - Generate knowledge graphs from any text to extract topics, concepts, and their relationships - Detect content gaps between topical clusters to identify underexplored areas and research opportunities - Generate research questions and innovative ideas that bridge identified content gaps - Create topical clusters and keyword groupings using network analysis for SEO and content strategy - Connect existing InfraNodus graphs to LLM workflows or create new ones to augment AI assistant responses - Generate contextual hints to provide LLMs with high-level text understanding and better prompt augmentation

README

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.

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. generate_content_gaps

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

    4. 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

    5. generate_contextual_hint

    - Generate a topical overview of a text and provide insights for LLMs to generate better responses - Use it to get a high-level understanding of a text - Use it to augment prompts in your LLM workflows and AI assistants

    6. generate_research_questions

    - Generate research questions that bridge content gaps - Use them as prompts in your LLM models and AI workflows - Use any AI model (included in InfraNodus API) - Content gaps are identified based on topical clustering

    7. generate_research_ideas

    - Generate innovative research ideas based on content gaps identified in the text - Get actionable ideas to improve the text and develop the discourse - Use any AI model (included in InfraNodus API) - Ideas are generated from gaps between topical clusters

    8. research_questions_from_graph

    - Generate research questions based on an existing InfraNodus graph - Use them as prompts in your LLM models - Use any AI model (included in InfraNodus API) - Content gaps are identified based on topical clustering

    9. generate_responses_from_graph

    - Generate responses based on an existing InfraNodus graph - Integrate them into your LLM workflows and AI assistants - Use any AI model (included in InfraNodus API) - Use any prompt

    10. develop_conceptual_bridges

    - Analyze text and develop latent ideas based on concepts that connect this text to a broader discourse - Discover hidden themes and patterns that link your text to wider contexts - Use any AI model (included in InfraNodus API) - Generate insights that help develop the discourse

    11. develop_latent_topics

    - Analyze text and extract underdeveloped topics with ideas on how to develop them - Identify topics that need more attention and elaboration - Use any AI model (included in InfraNodus API) - Get actionable suggestions for content expansion

    12. develop_text_tool

    - Comprehensive text analysis combining content gap ideas, latent topics, and conceptual bridges - Executes multiple analyses in sequence with progress tracking - Generates research ideas based on content gaps - Identifies latent topics and conceptual bridges to develop - Finds content gaps for deeper exploration

    13. create_knowledge_graph

    - Create a knowledge graph in InfraNodus from text and provide a link to it - Use it to create a knowledge graph in InfraNodus from text

    14. overlap_between_texts

    - Create knowledge graphs from two or more texts and find the overlap (similarities) between them - Use it to find similar topics and keywords across different texts

    15. difference_between_texts

    - Compare knowledge graphs from two or more texts and find what's not present in the first graph that's present in the others - Use it to find how one text can be enriched with the others

    16. **a

    Related MCP Servers

    AI Research Assistant

    AI Research Assistant

    hamid-vakilzadeh

    AI Research Assistant provides comprehensive access to millions of academic papers through the Semantic Scholar and arXiv databases. This MCP server enables AI coding assistants to perform intelligent literature searches, citation network analysis, and paper content extraction without requiring an API key. Key features include: - Advanced paper search with multi-filter support by year ranges, citation thresholds, field of study, and publication type - Title matching with confidence scoring for finding specific papers - Batch operations supporting up to 500 papers per request - Citation analysis and network exploration for understanding research relationships - Full-text PDF extraction from arXiv and Wiley open-access content (Wiley TDM token required for institutional access) - Rate limits of 100 requests per 5 minutes with options to request higher limits through Semantic Scholar

    Web & Search
    12 8
    Linkup

    Linkup

    LinkupPlatform

    Linkup is a real-time web search and content extraction service that enables AI assistants to search the web and retrieve information from trusted sources. It provides source-backed answers with citations, making it ideal for fact-checking, news gathering, and research tasks. Key features of Linkup: - Real-time web search using natural language queries to find current information, news, and data - Page fetching to extract and read content from any webpage URL - Search depth modes: Standard for direct-answer queries and Deep for complex research across multiple sources - Source-backed results with citations and context from relevant, trustworthy websites - JavaScript rendering support for accessing dynamic content on JavaScript-heavy pages

    Web & Search
    2 24
    Math-MCP

    Math-MCP

    EthanHenrickson

    Math-MCP is a computation server that enables Large Language Models (LLMs) to perform accurate numerical calculations through the Model Context Protocol. It provides precise mathematical operations via a simple API to overcome LLM limitations in arithmetic and statistical reasoning. Key features of Math-MCP: - Basic arithmetic operations: addition, subtraction, multiplication, division, modulo, and bulk summation - Statistical analysis functions: mean, median, mode, minimum, and maximum calculations - Rounding utilities: floor, ceiling, and nearest integer rounding - Trigonometric functions: sine, cosine, tangent, and their inverses with degrees and radians conversion support

    Developer Tools
    22 81