InfraNodus MCP Server: AI Knowledge Graphs from Text with Network Analysis Insights

InfraNodus MCP Server is a standardized protocol that connects large language models (LLMs) like Claude, ChatGPT, and other AI systems directly to InfraNodus's knowledge graph analysis engine via its API. This means you can perform text analysis and extract insights from knowledge graphs using natural language commands directly in your AI chat interface, code editor, or automated workflows.

The advantage of using the MCP server to the API is that the MCP server has a collection of well-described tools that can be used for specific use cases without the need to write code or set up parameters for API connection. The model can also combine different tools at its own discretion based on the task at hand.

What Can the InfraNodus MCP Server Do?

InfraNodus MCP server has a collection of tools that can be called by the LLM models to perform specific tasks. Here is a high-level overview of what you can do with the InraNodus MCP server:

• 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 and ontologies from any text and use them to augment your LLM responses and reasoning logic

• Import data from external sources (e.g. Google results or search intent) and use them to augment your LLM responses

Available Tools in the InfraNodus MCP Server

The MCP server has a collection of tools that can be called by the LLM models to perform specific tasks. Here are the detailed tool descriptions, you can find more up-to-date data on the MCP Server npm package README page.

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. generate_content_gaps
Detect missing connections in discourse
Identify underexplored topics
Generate research questions
Suggest content development opportunities

4. 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

5. 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

6. generate_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

7. 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

8. 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

9. 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

10. generate_overlap_graph
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

11. generate_difference_graph
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

12. generate_google_search_graph
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)

13. generate_search_queries_graph
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)

14. generate_search_results_vs_queries_graph
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

15. 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

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

How to Deploy the InfraNodus MCP Server

There are two ways to deploy the InfraNodus MCP server: locally and remotely.

1. Locally: You can deploy the MCP server locally on your own server or on a cloud provider. You can find the instructions on the MCP Server npm package README page or below. This is suitable for local Claude desktop version or your local IDE such as Cursor, VSCode, or Windsurf AI.

2. Remotely: You can deploy the MCP server remotely on Smithery. You can find the instructions on the Smithery MCP Server page. This is a better option for faster one-click installation.

Quick Start & Configuration Examples

Here are step-by-step configuration examples for the most popular platforms. First, obtain your InfraNodus API key from your API control panel.

1. Claude Desktop Configuration

Install the MCP server via Smithery:

Go to the Smithery MCP Server page, choose "Claude" and click the install button.

At the beginning, you don't need to add the Infranodus API key, just install the server. You might need to log in Smithery though to generate their API key that gives you access via Smithery to the InfraNodus server. If you hit the API rate limit, just open a free Smithery account, log in, and in the Configuration tab for the InfraNodus server add the API key that you can obtain at InfraNodus API access page.

Restart Claude Desktop and you'll see InfraNodus tools available in your chat.

Smithery will add something like this to your Claude Desktop configuration file:

{
	"mcpServers": {
		"infranodus": {
			"command": "npx",
			"args": [
				"-y",
				"@smithery/cli@latest",
				"run",
				"@infranodus/mcp-server-infranodus",
				"--key",
				"YOUR_SMITHERY_API_KEY",
				"--profile",
				"YOUR_SMITHERY_PROFILE_NAME"
			]
		}
	}
								

The Claude Desktop configuration file is usually located at:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

2. Claude Web Configuration

The easiest way is to open the Connectors page, then click "Add Custom Connector" and add the following URL to the connector's URL field:

https://server.smithery.ai/@infranodus/mcp-server-infranodus/mcp

Give it a short name (e.g. "InfraNodus") and then click "Connect". You will be redirected to Smithery's authentication page. There you can provide additional settings, such as your InfraNodus API key (if you have an account). If you do not have an account, you can use it without the key, but when you hit the rate limit, you'll need to disconnect the server and connect it again and add the InfraNodus API key this time.

3. ChatGPT Web Configuration

ChatGPT doesn't have very good support for MCP servers, because you can only use it in the Developer Mode, and only in new converastions (you cannot activate it in existing conversations). So we recommend using it with Claude. But if you decide to use the InfraNodus MCP server anyway, here's how you can do it.

Open the Connectors page, then click "Advanced Settings" and activate the "Developer Mode". Then you'll have the "Create" button appearing at the top right corner next to "Enabled Connectors". Click it and the following URL to the connector's URL field:

https://server.smithery.ai/@infranodus/mcp-server-infranodus/mcp

Give it a short name (e.g. "InfraNodus") and then click "Connect". You will be redirected to Smithery's authentication page. There you can provide additional settings, such as your InfraNodus API key (if you have an account). If you do not have an account, you can use it without the key, but when you hit the rate limit, you'll need to disconnect the server and connect it again and add the InfraNodus API key this time.

4. Cursor IDE Configuration

Go to the <

Go to the InfraNodus MCP server page on Smithery and choose Cursor in "Add Your Own Client" > Auto menu.

Smithery will open Cursor and offer you to add a server.

Make sure you give it a short name (e.g. "InfraNodus") and then click "Connect".

Smithery will add the following setting to your MCP server configuration:

{
	"mcpServers": {
		"InfraNodus ": {
			"type": "http",
			"url": "https://server.smithery.ai/@infranodus/mcp-server-infranodus/mcp?api_key=YOUR_SMITHERY_API_KEY&profile=YOUR_SMITHERY_PROFILE_NAME",
			"headers": {}
		}
	}
								}

You can verify it in Settings → Cursor Settings → Tools & MCP and click "Add new MCP Server".

5. Local Installation (without Smithery)

You don't have to use Smithery to use the MCP server locally in your favorite IDE or Claude Desktop. This can be a more interesting setup if you want to add additional tools or modify the tool calling following our API documentation. Here's how you can install the InfraNodus MCP server locally:

Install the latest version of the MCP server from our GitHub repository using Terminal:

git clone https://github.com/yourusername/mcp-server-infranodus.git
cd mcp-server-infranodus
npm install
npm run build

								

Create the `.env` file and add your InfraNodus API key. You don't need to have the API key but you will then hit rate limits after a while.

INFRANODUS_API_KEY=your_api_key
								

Start and build the server using Terminal:

npm run inspect

Claude Desktop Configuration (macOS)

   
 open ~/Library/Application\ Support/Claude/claude_desktop_config.json
   

Add the InfraNodus server configuration:

   
   {
   	"mcpServers": {
   		"infranodus": {
   			"command": "node",
   			"args": ["/absolute/path/to/mcp-server-infranodus/dist/index.js"],
   			"env": {
   				"INFRANODUS_API_KEY": "your-api-key-here"
   			}
   		}
   	}
   }

4. Example Usage in Chat

Once configured, you can use natural language commands like:

• "Analyze this text and show me the main topical clusters"
• "What are the content gaps in this article?"
• "Generate research questions from this document"
• "Find my existing graphs where I talk about [topic]" - for logged in users
• "Compare these two texts and find overlapping concepts"
• "What do people search for about [topic] but don't find in results?"

Troubleshooting Common Issues

Issue: "Rate limit exceeded"
Solution: Free accounts have limited API calls. Upgrade to Advanced, Pro, or Premium for higher limits.

Issue: MCP server not appearing in Claude/Cursor
Solution: Restart the application completely after editing the config file.

Issue: "API key not found"
Solution: You can use the MCP server for free for the first few iterations. Then you'll need to log in InfraNodus and get your API key on the InfraNodus API Access page. Add this key to the Smithery Configuraiton page or to your `ENV` settings if you set up the server locally. You might need to disconnect, remove, and reconnect the server again for it to work.

Issue: Wrong tools are used
Solution: Ask your LLM expicitly to use the InfraNodus tool in the prompt and let us know about this issue.

Adding Tools to the InfraNodus MCP Server

If you install the InfraNodus MCP server locally, you can add more tools to it. The easiest way to do that is to read through our API documentation and create your own tools based on the already existing ones.

If you don't know how to code but would like to have a certain tool added, you can request it via our support portal, Discord server, or GitHub issues on the repository page.

Data Privacy

InfraNodus MCP server uses the `doNotSave` parameter from its API to avoid saving graphs in your user account. This way your data won't even be stored in our logs and there will be no trace of what you process on our servers. Most of the InfraNodus functionalities don't require the use of AI, however, when it is needed and you explicitly ask for it, we use GPT models from OpenAI, Claude, or Gemini's API (you choose the model). All of these companies claim that they don't use the data processed using their API for training purposes, so you get an additional degree of privacy for your data. This is suitable for the use in web-based applications, such as Claude Web, ChatGPT (via Developer Mode > Connectors), and cloud-based automation paltforms, such as N8N (via their MCP node).

Try the InfraNodus MCP Server