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Atomic AGI

Connects AI agents to Atomic for SEO, AI visibility, reputation, content, keyword research, and technical or AX audits.

Install Atomic AGI

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

Streamable HTTP (Remote)
Server URL
https://app.atomicagi.com/api/mcp

Remote servers are added in the app, not the config file: Customize → Connectors → Add custom connector, then paste this URL.

Hosted endpointLive · 1d agoLive Remote EndpointOur automated health check connected to this remote MCP endpoint successfully.Last checked 1d ago · Nov 12, 2026, 04:30 UTC

Clients with native remote MCP support can connect directly to this URL.

Remote endpoint
https://app.atomicagi.com/api/mcp

Overview

Atomic AGI connects compatible AI agents to the Atomic SEO and AI search optimization platform through a remote MCP endpoint. It supports workflows across AI visibility tracking, brand reputation, content, technical audits, Agent Experience, and entity strategy, with an included keyword research skill. Use it when an agent needs to inspect Atomic project context, analyze available search data, or work within existing Atomic permissions. Authentication uses OAuth where supported or a personal Atomic MCP token configured privately in the client.

Use cases

  • Research keyword opportunities for an Atomic project
  • Review existing content before proposing topic clusters
  • Track AI search visibility and brand reputation
  • Audit technical SEO and Agent Experience
  • Connect an AI agent to authorized Atomic projects

Key features

  • Remote MCP connection to Atomic
  • AI visibility and SEO workflows
  • Brand reputation and entity strategy support
  • Technical and Agent Experience audits
  • Included keyword research skill
  • OAuth or personal-token authentication

Security & health

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

ActiveLast checked

Recent health check succeeded.

Adoption & maintenance

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

Last commit2d agoMost recent push to the default branch.
MaintenanceActively maintainedStatus declared by the project or listing owner.
Directory activity17 viewsConfig copies, upvotes, and views on AllMCPs.

How Atomic AGI works

What Atomic AGI MCP server does

Atomic AGI connects compatible AI agents with Atomic, an SEO and AI search optimization platform from Omnius. The platform covers strategy, AI visibility tracking, brand reputation, content workflows, entity strategy, technical audits, and Agent Experience (AX).

The repository packages the connection rather than implementing the Atomic application or MCP server. It includes public plugin metadata, a remote MCP connection definition, and a reusable atomic-keyword-research skill.

How it works

The integration uses Atomic’s remote MCP endpoint:

text
https://app.atomicagi.com/api/mcp

An MCP-compatible client connects to that endpoint and operates within the permissions of the Atomic account and projects being used. Clients with an OAuth connection flow can use that authentication method. Other clients can use a personal token created in Atomic’s MCP settings and pass it privately through the client configuration.

The included keyword research workflow is designed to inspect project context, review existing content and available search data, suggest topic clusters based on intent, separate measured evidence from hypotheses, avoid duplicate content, and verify authorized Grid changes.

Setup and configuration

An Atomic account and access to the relevant projects are required. Configure the remote endpoint in the chosen client, then complete OAuth or add a personal MCP token according to that client’s authentication model. Tokens should remain in local or private configuration and must not be committed to the repository.

For Cursor, the MCP configuration uses the endpoint and an authorization header:

config.json
{
  "mcpServers": {
    "atomic-agi": {
      "url": "https://app.atomicagi.com/api/mcp",
      "headers": {
        "Authorization": "Bearer atmcp_YOUR_TOKEN"
      }
    }
  }
}

The portable mcp.json describes the remote server; authentication is configured separately by the client. Plugin and skill installation behavior can vary between clients.

Tools and capabilities

  • Connect AI agents to Atomic projects over MCP.
  • Support SEO and AI search optimization workflows.
  • Track AI visibility and manage brand reputation workflows.
  • Work with content, entity strategy, technical audits, and AX reviews.
  • Research keyword opportunities using project context, existing content, and available search data.
  • Propose intent-based topic clusters while checking for duplicate content and distinguishing evidence from hypotheses.

Limitations and notes

The package is an integration layer, not the Atomic application or the MCP server implementation. Access depends on the Atomic account, project permissions, and the capabilities of the selected client. Keep credentials out of source control, and verify authorization before making Grid changes through a workflow.

Reviews

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Frequently Asked Questions about Atomic AGI

Atomic AGI is an MCP server that connects compatible AI agents to the Atomic SEO and AI search optimization platform. Its main capabilities cover AI visibility tracking, brand reputation, content and entity strategy, technical and Agent Experience audits, plus an included keyword research workflow for Atomic projects.

Configure the remote endpoint `https://app.atomicagi.com/api/mcp` in an MCP-compatible client, then authenticate with OAuth or a personal Atomic MCP token. The repository includes the remote connection definition and keyword research skill, while installation support varies by client.

Yes, Atomic AGI works with Cursor through a local MCP configuration containing the remote endpoint and an `Authorization: Bearer` token header. Create the personal token in Atomic’s MCP settings, replace the local placeholder, and keep the token out of committed files.

The keyword research skill reviews Atomic project context, existing content, and available search data before proposing intent-based topic clusters. It also helps distinguish measured evidence from hypotheses, avoid duplicate content, and verify authorized Grid changes.

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