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Infranodus MCP Server Infranodus

Maps text, URLs, transcripts, and search results into InfraNodus knowledge graphs for analysis, retrieval, and research.

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Install Infranodus MCP Server Infranodus

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "infranodus-mcp-server-infranodus": {
      "command": "npx",
      "args": [
        "-y",
        "infranodus-mcp-server"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Install not yet sandbox-tested— the server itself is confirmed live (we called its tools/list endpoint). The stdio install command is a separate, ongoing check.

Overview

infranodus-mcp-server-infranodus connects AI assistants to InfraNodus for knowledge-graph creation, text-network analysis, and graph-based retrieval. It can extract concepts, relations, topical clusters, structural gaps, research questions, and latent topics from text, URLs, YouTube material, search results, or saved graphs. Use it when an agent needs structured analysis of a discourse, persistent graph memory, or responses grounded in an existing InfraNodus graph. Claude Desktop is named as a supported AI assistant in the project documentation.

Use cases

  • Generate knowledge graphs from documents, URLs, or transcripts
  • Retrieve grounded context from saved InfraNodus graphs
  • Identify content gaps and formulate research questions
  • Compare multiple texts for shared or missing conceptual relations
  • Analyze codebase structure or produce a procedural project digest

Key features

  • Knowledge-graph generation from text and web-based sources
  • Topical cluster, concept, relation, and content-gap analysis
  • Persistent InfraNodus graph and memory operations
  • GraphRAG retrieval and response generation
  • Ontology generation for topics, codebases, and project digests
  • Text, search-result, and reasoning-structure analysis

Tools40

What Infranodus MCP Server Infranodus lets your agent do. Expand a tool for its parameters.

Verified live~36.2k tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked Today

Recent health check succeeded.

23/23 checks healthy over the last 91d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

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Scanned 6d ago via OSV.dev · infranodus-mcp-server (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars105Stargazers on the source repository.
npm downloads1.7kPackage downloads in the last 30 days.
Last commit1d agoMost recent push to the default branch.
Tools exposed40Callable tools this server registers over MCP.
Directory activity6 viewsConfig copies, upvotes, and views on AllMCPs.

How Infranodus MCP Server Infranodus works

What infranodus-mcp-server-infranodus does

InfraNodus MCP Server brings InfraNodus knowledge graphs and text-network analysis into MCP-compatible AI workflows. It turns unstructured material into concepts, entities, relations, clusters, and graph-level statistics that an assistant can inspect and use for further reasoning.

The server supports both one-off analysis and persistent knowledge work. Agents can generate a graph from text, save a graph to an InfraNodus account, retrieve information from an existing graph, or add and read relations in InfraNodus memory. Saved graphs can be listed, searched, analyzed, and used as context for generated responses.

How it works

Inputs can include plain text, URLs, YouTube transcripts or results, Google search results, related search queries, LLM-generated descriptions, and existing InfraNodus graphs. Analysis identifies main concepts and topical clusters, then uses their relationships to expose content gaps, underdeveloped areas, and possible connections between ideas.

Several tools accept an existing graph by name, allowing a workflow to build on previously stored material rather than reprocess the original source. Graphs can also be compared or combined: the server can find overlap between texts, identify relations present in one source but absent from another, and merge multiple sources into a graph with shared clusters and gaps.

The ontology workflow accepts exactly one of a topic prompt, a long text, or a source graph. It can frame an ontology around a software codebase or produce a procedural digest from an existing project graph. Ontologies may be saved as InfraNodus graphs or returned for one-off use, with chunking for longer inputs.

Tools and capabilities

  • Generate and save knowledge graphs from text, URLs, statements, or other supported sources.
  • Extract entities, concepts, relations, topical clusters, structural gaps, and graph statistics.
  • Create reasoning ontologies for topics, codebases, or procedural project digests.
  • List, search, fetch, inspect, and retrieve context from saved InfraNodus graphs.
  • Add relations to memory and query stored relations for a concept or entity.
  • Generate content gaps, research questions, research ideas, latent topics, and conceptual bridges.
  • Produce contextual hints, expert responses, and GraphRAG-style retrieval results from graph content.
  • Compare, merge, and analyze text collections, search results, YouTube results, and LLM responses.
  • Evaluate text structure or model reasoning for bias, focus, diversity, and coherence, then suggest directions for improvement.

Limitations and notes

Graph persistence depends on InfraNodus account graphs and memory. Tools that operate on saved material generally require a graph name or another identifier for the existing graph. The ontology tool has distinct source and mode parameters, including separate handling for codebase analysis and procedural digests. Some operations can return substantial graph and analytics data, so the available options to omit graph or analytics output are useful when keeping agent context smaller.

Read the full README View source on GitHub

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Frequently Asked Questions about Infranodus MCP Server Infranodus

infranodus-mcp-server-infranodus is an MCP server that connects AI assistants to InfraNodus knowledge graphs and text-network analysis. Its main tools generate and save graphs, analyze text or URLs, retrieve graph context, inspect memory relations, find topical clusters and gaps, and produce research questions, ideas, and responses.

Install the server from its published npm package, infranodus-mcp-server. The project also identifies Claude Desktop as an AI assistant integration for the server.

It can analyze text, URLs, YouTube transcripts or results, Google search results, related search queries, LLM responses, and existing InfraNodus graphs. The analysis can return concepts, relations, clusters, gaps, summaries, research questions, or research ideas.

Yes, it can save graphs in InfraNodus and return their names and links for later use. Saved graphs can be listed, searched, fetched, analyzed, combined with other sources, or used for knowledge-base retrieval.

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