Infranodus Knowledge Graphs & Text Analysis
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
Available Tools
-
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
-
analyze_existing_graph_by_name
- Retrieve and analyze existing graphs from your InfraNodus account
- Access previously saved analyses
- Export graph data with full statistics
-
generate_content_gaps
- Detect missing connections in discourse
- Identify underexplored topics
- Generate research questions
- Suggest content development opportunities
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
generate_text_overview
- 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
-
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
-
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
-
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
-
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)
-
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)
-
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
-
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
-
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
-
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
• SEO Analysis: Optimize any text or discourse for search intent and enhanced topical authority
Server Config
{
"mcpServers": {
"infranodus": {
"command": "npx",
"args": [
"-y",
"infranodus-mcp-server"
],
"env": {
"INFRANODUS_API_KEY": "YOUR_INFRANODUS_API_KEY"
}
}
}
}