Best for
- Researchers needing knowledge graphs and network analysis
- Content creators and SEO professionals
- Developers looking to enhance LLM workflows with structured context
- Analysts seeking to extract structured insights from text
M
MCP Server infranodus
Official
Integrate InfraNodus knowledge graph and text network analysis into LLM workflows.
GitHub source repository ↗
56FMRS · C
InfraNodus MCP Server offers a rich set of text network analysis tools, seamlessly integrating knowledge graph capabilities into LLM workflows. Its powerful features, such as content gap detection and SEO analysis, make it particularly valuable for content creators and researchers. However, users should be mindful of API quotas and privacy considerations.
InfraNodus MCP Server is an official MCP server that 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.
Tools
generate_knowledge_graph
Convert any text into a visual knowledge graph, extract topics, concepts, and relationships, identify structural patterns and clusters, with AI-powered topic naming and entity detection.
analyze_existing_graph_by_name
Retrieve and analyze existing graphs from your InfraNodus account, access previously saved analyses, and export graph data with full statistics.
analyze_text
Analyze a text, URL, or YouTube transcript, extract and analyze a graph, returning topics, clusters, statements, graph structure, and AI summary.
generate_content_gaps
Detect missing connections in discourse, identify underexplored topics, generate research questions, and suggest content development opportunities.
generate_topical_clusters
Generate topics and clusters of keywords from text using knowledge graph analysis, with AI-generated cluster summaries useful for SEO topical authority.
generate_contextual_hint
Generate a topical overview of a text and provide insights for LLMs to generate better responses, useful for prompt augmentation.
generate_research_questions
Generate research questions that bridge content gaps from text, URL, or an existing graph.
generate_research_ideas
Generate innovative research ideas based on content gaps identified in the text, providing actionable suggestions.
optimize_text_structure
Analyze the level of bias and coherence in text using knowledge graph analysis, and suggest improvements to balance discourse or deepen analysis.
optimize_reasoning
Apply bias/coherence analysis to the model's own reasoning trace, steering it toward optimal diversity and coherence.
generate_responses_from_graph
Generate responses based on an existing InfraNodus graph, using any AI model and prompt.
develop_conceptual_bridges
Analyze text and develop latent ideas based on concepts connecting the text to broader discourse, discovering hidden themes.
develop_latent_topics
Extract underdeveloped topics with ideas on how to develop them, identifying areas needing more elaboration.
develop_text_tool
Comprehensive text analysis combining content gap ideas, latent topics, and conceptual bridges, with sequential analyses and progress tracking.
create_knowledge_graph
Create a knowledge graph in InfraNodus from text and provide a link to it.
generate_ontology_graph
Use AI to generate a reasoning ontology graph (entities and relations) from a topic, prompt, or text, optionally saving it.
analyze_llm_results
Ask an LLM to describe a topic and turn its response into a knowledge graph, revealing how the model frames it and its biases.
overlap_between_texts
Create knowledge graphs from two or more texts and find the overlap (similarities) between them.
merged_graph_from_texts
Build a graph of all provided texts and URLs, showing topical clusters and gaps across the merged content.
difference_between_texts
Compare knowledge graphs from two or more texts and find what's present in others but not in the first.
analyze_google_search_results
Generate a graph with keywords and topics for Google search results, understanding current informational supply.
analyze_youtube_results
Generate a graph from YouTube results (search, comments, channel, playlist, subtitles), understanding discourse topics and gaps.
analyze_related_search_queries
Generate a graph from Google suggested search queries, understanding current informational demand.
search_queries_vs_search_results
Generate a graph of keyword combinations people search for but that don't readily appear in results, finding demand-supply gaps.
generate_seo_report
Analyze content for SEO optimization by comparing with Google search results and queries, identifying content gaps and opportunities.
memory_add_relations
Add relations to InfraNodus memory from text, automatically detecting entities or using [[wikilinks]].
memory_get_relations
Retrieve relations from InfraNodus memory for specific entities, supporting entity-specific searches or full context retrieval.
retrieve_from_knowledge_base
Retrieve context from an existing InfraNodus knowledge graph using GraphRAG, with natural language queries.
search
Search through existing InfraNodus graphs, also public graphs of a user, compatible with ChatGPT Deep Research.
fetch
Fetch a specific search result for a graph, usable in ChatGPT Deep Research.
Setup
- Create an account on InfraNodus and get an API key (14-day free trial available). 2. For remote/web apps, use the server URL https://mcp.infranodus.com and authenticate via OAuth. 3. For local install, use npx command
npx -y infranodus-mcp-serverwith environment variable INFRANODUS_API_KEY. 4. Add configuration to a supported client (e.g., Claude Desktop or Cursor).
claude_desktop_config.json
{
"mcpServers": {
"infranodus": {
"command": "npx",
"args": [
"-y",
"infranodus-mcp-server"
],
"env": {
"INFRANODUS_API_KEY": "YOUR_INFRANODUS_API_KEY"
}
}
}
}
Fit and risk
Not for
- Users requiring fully offline usage without API key (limited free quota)
- Those seeking lightweight simple text summarization
- Users who don't need graph structures or network analysis
Required permissions
- Requires a valid InfraNodus API key
- Can read provided text or URL content
- Can create, retrieve, and modify knowledge graphs in the user's account
- May call external AI models (via InfraNodus API)
Risks and side effects
- Risk of API key misuse or leakage
- Dependence on external API availability and quota limits
- Paywall: API key needed after free quota
- Hosted server may store text data, privacy concerns
Troubleshooting
- Check that the server path or command in config is correct
- Verify the API key is valid and not expired
- Ensure Node.js is in system PATH
- Fully restart the client application
- For build errors, run `rm -rf node_modules package-lock.json && npm install && npm run build`
Use cases
Integrating existing knowledge graphs into AI conversations
Analyzing text structure to discover topical clusters and content gaps
Generating SEO reports and content optimization suggestions
Creating new knowledge graphs to augment LLM responses
Retrieving context from knowledge bases using GraphRAG
Supported clients
Claude DesktopFull support
CursorFull support