Install MCP Server to OpenClaw AI Agent

OpenClaw is a local autonomous AI agent that can run tools from MCP servers via MCPorter. Once MCPorter is installed and the InfraNodus MCP server is configured, OpenClaw can automatically call any InfraNodus tool based on your natural language prompts.

How It Works

OpenClaw does not need to have the MCP server added directly. Instead, it relies on mcporter to provide it a schema for each particular server / tool. When you give OpenClaw a prompt, it will:

1. Invoke MCPorter and read the schema for the InfraNodus MCP server
2. Understand which tool to use based on your prompt
3. Make the appropriate mcporter call request via the command line
4. Ingest the response and augment its answer with the data

How To Install

There are two ways to install the InfraNodus MCP server to OpenClaw.

1. You can add the `skill-cli-use.zip` skill file to OpenClaw from the InfraNodus skills repository and install it via the OpenClaw skills menu.

2. You can add the InfraNodus MCP server to OpenClaw via the deploy commands below.

To install your InfraNodus MCP server globally, run:

mcporter config --config ~/.mcporter/mcporter.json \
add infranodus --url https://mcp.infranodus.com \
--auth oauth \
--header "accept=application/json, text/event-stream"

To install your InfraNodus MCP server locally, run:

mcporter config add infranodus \
--url https://mcp.infranodus.com/ \
--transport http \
--auth oauth \
--header "accept=application/json, text/event-stream" \
--scope home

Viewing Available Tool Schemas

To see all available InfraNodus tools and their schemas that OpenClaw will use:

mcporter list infranodus --schema

This will list all the tools available in the MCP server as Typescript functions with schema definitions. This is sufficient for OpenClaw to understand how to use the tools, which parameters to provide, and what objectives each tool can help accomplish.

Example: Content Gap Analysis

For instance, the generate_content_gaps tool schema looks like this:

/**
 * Generate content gaps from text, URL, or an existing graph
 * using knowledge graph analysis.
 *
 * @param text? Text to retrieve content gaps from.
 * @param url? URL to fetch content from or YouTube video URL.
 * @param graphName? Name of an existing InfraNodus graph.
 */
function generate_content_gaps(
  text?: string,
  url?: string,
  graphName?: string
);

You can ask OpenClaw to run content gap analysis using InfraNodus with a prompt like:

find content gaps using infranodus in this text: "God said, 'You shall not eat of the fruit of the tree which is in the midst of the garden, neither shall you touch it, lest you die.'" But the serpent said to the woman, "You will not die. For God knows that when you eat of it your eyes will be opened, and you will be like God, knowing good and evil."

MCPorter will first invoke the MCP server and read the schema, then it will understand that it should use the generate_content_gaps tool and make the following request:

mcporter call infranodus.generate_content_gaps text="God said, 'You shall not eat of the fruit of the tree which is in the midst of the garden, neither shall…"

This request is calling the infranodus.generate_content_gaps tool via the command line available to OpenClaw.

The tool provides this result:

{
  "contentGaps": [
    "Gap 1: 2. Edenic Growth (tree midst fruit garden touch) -> 3. Moral Awareness (knowing good evil)",
    "Gap 2: 1. Divine Consumption (eat god eye open) -> 2. Edenic Growth (tree midst fruit garden touch)",
    "Gap 3: 1. Divine Consumption (eat god eye open) -> 3. Moral Awareness (knowing good evil)"
  ]
}

OpenClaw will then ingest this information and augment its response:

Here are the content gaps identified by InfraNodus via knowledge graph analysis:

Gap 1: Edenic Growth ↔ Moral Awareness
(tree, midst, fruit, garden, touch) → (knowing, good, evil)
The text doesn't connect the physical setting (the tree, the garden, touching
the fruit) with the moral outcome (knowing good and evil). What's the mechanism?
How does eating a physical fruit produce moral knowledge?

Querying Existing Graphs

You can also query content gaps directly from your existing InfraNodus graphs. For instance, to get the gaps in a graph called gato-politics-memory:

mcporter call infranodus.generate_content_gaps graphName="gato-politics-memory"

The response:

{
  "contentGaps": [
    "Gap 1: 3. Meaning Justification ([[relational_meaning]] ...) -> 6. Governance Accountability ([[transnational]] ...)",
    "Gap 2: 2. Protocol Coordination ([[protocols]] ...) -> 3. Meaning Justification ([[relational_meaning]] ...)",
    "Gap 3: 1. Harm Metrics ([[metrics]] [[harm]] ...) -> 3. Meaning Justification ([[relational_meaning]] ...)"
  ]
}

You can then reuse this in your LLM workflow.

To see the full list of tool definitions available for the InfraNodus MCP server, visit the MCP Tools page.

What's Next

• Terminal / Local CLI Setup (MCPorter)
• All Deployment Guides
• MCP Tools & Examples