Infranodus Mcp Server Infranodus MCP server
{
"mcpServers": {
"smithery-infranodus-mcp-server-infranodus": {
"type": "http",
"url": "https://server.smithery.ai/@infranodus/mcp-server-infranodus/mcp"
}
}
}{
"mcpServers": {
"smithery-infranodus-mcp-server-infranodus": {
"type": "http",
"url": "https://server.smithery.ai/@infranodus/mcp-server-infranodus/mcp"
}
}
}{
"mcpServers": {
"smithery-infranodus-mcp-server-infranodus": {
"type": "http",
"url": "https://server.smithery.ai/@infranodus/mcp-server-infranodus/mcp"
}
}
}{
"servers": {
"smithery-infranodus-mcp-server-infranodus": {
"type": "http",
"url": "https://server.smithery.ai/@infranodus/mcp-server-infranodus/mcp"
}
}
}{
"mcpServers": {
"smithery-infranodus-mcp-server-infranodus": {
"type": "http",
"url": "https://server.smithery.ai/@infranodus/mcp-server-infranodus/mcp"
}
}
}file .mcp.json (in your project root)file claude_desktop_config.jsonfile ~/.cursor/mcp.jsonfile .vscode/mcp.jsonfile ~/.codeium/windsurf/mcp_config.jsonversion 1.0.0
What it does
An MCP server that converts text into knowledge graphs and analyzes them using network science algorithms. It detects topics, clusters, content gaps, and conceptual relationships, useful for researchers, content creators, and developers building AI workflows that need structured discourse analysis.
- Generate knowledge graphs from text or URLs to identify main topics and how they relate to each other.
- Detect gaps and missing connections in a text to find research opportunities or content development areas.
- Analyze existing InfraNodus graphs and use the results to improve LLM responses with topical context.
Tools this server exposes
| Tool | Does |
|---|---|
| generate_knowledge_graph | Convert text into a visual knowledge graph showing topics, concepts, and their relationships. |
| analyze_existing_graph_by_name | Retrieve and analyze previously saved graphs from an InfraNodus account. |
| analyze_text | Extract topics, clusters, and structure from text, URLs, or YouTube transcripts. |
| generate_content_gaps | Identify missing connections and underexplored topics in discourse. |
| generate_topical_clusters | Extract topics and keyword clusters with AI-generated summaries for topical authority. |
| generate_contextual_hint | Provide a high-level topical overview of text to augment LLM prompts. |
| generate_research_questions | Create research questions that address content gaps in text or graphs. |
| generate_research_ideas | Generate innovative research ideas based on identified gaps between topical clusters. |
| optimize_text_structure | Analyze bias and coherence in text and suggest ways to balance or deepen the discourse. |
| optimize_reasoning | Apply bias and coherence analysis to LLM reasoning traces to improve reasoning balance. |
| generate_responses_from_graph | Generate LLM responses using an existing InfraNodus graph as context. |
| develop_conceptual_bridges | Identify hidden themes and patterns connecting text to broader discourse. |
| develop_latent_topics | Extract underdeveloped topics and get suggestions for how to expand them. |
| develop_text_tool | Run comprehensive text analysis combining gaps, latent topics, and conceptual bridges. |
Before you enable it
An MCP server runs with whatever access you give it. Start with a development account rather than production, and check which tools the server exposes before you let an agent call them unattended.