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. 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

6. 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

7. generate_research_questions

   • Generate research questions that bridge content gaps from text, URL, or an existing InfraNodus graph
   • 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

8. 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

9. optimize_text_structure

   • Analyze the level of bias and coherence in text using knowledge graph analysis
   • If the text is too biased, develop the represented topics to balance the discourse
   • If the text is focused or diversified, develop the content gaps to deepen the analysis
   • If the text is dispersed, focus the most common gap topics to improve coherence
   • Choose response type: response, idea, question, or transcend

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. merged_graph_from_texts

    • Build a graph of all the texts and URLs provided, providing topical clusters and gaps present in the merged graph generated from all the texts
    • Use it to combine multiple sources into one graph and see clusters and content gaps across the merged content

17. 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

18. analyze_google_search_results

    • Generate a graph with keywords and topics for Google search results for a certain query
    • Use it to understand the current informational supply (what people find)

19. analyze_related_search_queries

    • Generate a graph from the search queries suggested by Google for a certain query
    • Use it to understand the current informational demand (what people are looking for)

20. search_queries_vs_search_results

    • Generate a graph of keyword combinations and topics people tend to search for that do not readily appear in the search results for the same queries
    • Use it to understand what people search for but don't yet find

21. generate_seo_report

    • Analyze content for SEO optimization by comparing it with Google search results and search queries
    • Identify content gaps and opportunities for better search visibility
    • Get comprehensive analysis of what's in search results but not in your text
    • Discover what people search for but don't find in current results

22. memory_add_relations

    • Add relations to the InfraNodus memory from text
    • Automatically detect entities or use [[wikilinks]] syntax to mark them
    • Save memory to a specified graph name for future retrieval
    • Support automatic entity extraction or manual entity marking
    • Provide links to created memory graphs for easy access

23. memory_get_relations

    • Retrieve relations from InfraNodus memory for specific entities
    • Search for entity relations using [[wikilinks]] syntax
    • Query specific memory contexts or search across all memory graphs
    • Extract statements and relationships from stored knowledge graphs
    • Support both entity-specific searches and full context retrieval

24. retrieve_from_knowledge_base

    • Retrieve context from an existing InfraNodus knowledge graph using GraphRAG
    • Query your knowledge base with a natural language prompt to get relevant statements
    • Include graph summaries for quick overviews of the knowledge structure
    • Optionally retrieve the full graph, statements, or extended analysis
    • Ideal for augmenting LLM responses with domain-specific knowledge

25. search

    • Search through existing InfraNodus graphs
    • Also use it to search through the public graphs of a specific user
    • Compatible with ChatGPT Deep Research mode via Developer Mode > Connectors

26. fetch

    • Fetch a specific search result for a graph
    • Can be used in ChatGPT Deep Research mode via Developer Mode > Connectors

More capabilites coming soon!

Key Capabilities

• Topic Modeling: Automatic clustering and categorization of concepts
• Content Gap Detection: Find missing links between concept clusters
• Entity Recognition: Clean extraction of names, places, and organizations
• AI Enhancement: Optional AI-powered topic naming and analysis
• Structural Analysis: Identify influential nodes and community structures
• Network Structure Statistics: Modularity, centrality, betweenness, and other graph metrics
• Knowledge Graph Memory: Save and retrieve knowledge graph memories and analyze them to retrieve key nodes, clusters, and connectors

Knowledge Graph Memory Use Advice

InfraNodus represents any text as a network graph in order to identify the main clusters of ideas and gaps between them. This helps generate advanced insights based on the text's structure. The network is effectively a knowledge graph that can also be used to retrieve complex ontological relations between different entities and concepts. This process is automated in InfraNodus using the search and fetch tools along with the other tools that analyze the underlying network.

However, you can also easily use InfraNodus as a more traditional memory server to save and retrieve relations. We use [[wikilinks]] to highlight entities in your text to make your content and graphs compatible with markup syntax and PKM tools such as Obsidian. By default, InfraNodus will generate the name of the memory graph for you based on the context of the conversation. However, you can modify this default behavior by adding a system prompt or **proje

---