What is the key difference between Neotoma and Iranti?

Neotoma belongs to the Knowledge & Memory category with cloud HTTP/SSE execution, while Iranti belongs to the Knowledge & Memory category with local stdio execution.

How do I install Neotoma or Iranti in Claude Desktop or Cursor?

Copy the mcpServers JSON configuration snippet provided on this page into your claude_desktop_config.json or ~/.cursor/mcp.json file and restart your AI client.

Are Neotoma and Iranti free or paid?

Neotoma operates under a Free / Open Source model (No auth required). Iranti operates under a Free / Open Source model (No auth required).

Which MCP server has more tools and community activity?

Neotoma lists 6 tools with 0 views and 27 GitHub stars. Iranti lists 5 tools with 0 views and 5 GitHub stars.

Can I use both Neotoma and Iranti together in the same MCP host?

Yes! MCP hosts like Claude Desktop, Cursor, Windsurf, Cline, and VS Code support configuring multiple servers under the mcpServers object simultaneously.

How do I install Neotoma or Iranti in Claude Desktop, Cursor, or Windsurf?

Both servers follow the standard Model Context Protocol configuration format. Simply copy the JSON block from the configuration section above and paste it into your AI client's mcpServers configuration object (for instance in claude_desktop_config.json or ~/.cursor/mcp.json ), then completely restart or refresh the application.

Are Neotoma and Iranti free to use, or do they require API keys?

Neotoma is listed under a Free / Open Source model with No auth required . Iranti operates under a Free / Open Source model with No auth required . If environment variables are required (such as API keys), be sure to define them under the env key in your MCP client's configuration file.

Can I run both Neotoma and Iranti at the same time in my AI client?

Yes! MCP clients support multi-server orchestration. You can include both markmhendrickson-neotoma and nfemmanuel-iranti as distinct keys inside the single mcpServers object in your configuration file. Your AI assistant will automatically select and call the appropriate tool from either server during chat sessions.

Which MCP server has higher directory engagement and quality ratings?

On AllMCPs, Neotoma has a Quality Score of 49 /100 ( Fair ) with 0 views, 0 installs, and 27 GitHub stars. Iranti holds a Quality Score of 40 /100 ( Fair ) with 0 views, 0 installs, and 5 GitHub stars.

Side-by-Side Model Context Protocol Comparison

Neotoma vs Iranti

In-depth architectural comparison of the Neotoma and Iranti MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.

At a Glance & Executive Verdict

Neotoma

Knowledge & Memory · Remote HTTP/SSE

Quality: 49/100 (Fair) | Auth: No auth required

Iranti

Knowledge & Memory · Local stdio

Quality: 40/100 (Fair) | Auth: No auth required

Verdict Summary: Choose Neotoma if you need specialized Knowledge & Memory tools running via a hosted cloud SSE transport. Choose Iranti if your workspace requires Knowledge & Memory integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.

Which MCP Server Should You Choose?

Choose Neotoma when:

You need dedicated capabilities in the Knowledge & Memory domain.
You prefer remote streaming HTTP/SSE transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
Primary tools included: Immutable, append-only observation storage with content-addressed sources, Deterministic entity resolution and snapshot computation, Schema-first extraction with incremental schema evolution.

Explore Neotoma Details

Choose Iranti when:

You need dedicated capabilities in the Knowledge & Memory domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
Primary tools included: Entity/key/value triple storage, Hybrid semantic search capability, Task checkpoint management.

Feature & Specification Comparison

Specification	Neotoma markmhendrickson Knowledge & Memory	Iranti nfemmanuel Knowledge & Memory
Summary	Deterministic state layer for AI agents. Stores versioned entities (contacts, tasks, transactions, decisions) with immutable observations, full provenance, and schema-first extraction. Local-first SQLite, cross-client memory across Claude, Cursor, ChatGPT, and OpenClaw. Website	Persistent shared memory for AI coding agents. Stores facts as entity/key/value triples with hybrid semantic search, task checkpoints, and conflict resolution — shared across Claude Code, Codex CLI, and GitHub Copilot.
Category & Scope

Tools & Capabilities Breakdown

Neotoma Tools (6)

Immutable, append-only observation storage with content-addressed sources

Deterministic entity resolution and snapshot computation

Schema-first extraction with incremental schema evolution

Typed relationships and timeline event queries

Multi-agent access control with hardware-attested authentication

Peer instance federation and configurable conflict resolution

Iranti Tools (5)

Ready-to-Paste Client Configurations

Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).

Neotoma Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "markmhendrickson-neotoma": {
      "url": "https://neotoma.io)**"
    }
  }
}

Iranti Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "nfemmanuel-iranti": {
      "command": "npx",
      "args": [
        "-y",
        "nfemmanuel-iranti"
      ]
    }
  }
}

Frequently Asked Questions

Neotoma is categorized under Knowledge & Memory and uses a remote streaming HTTP/SSE transport. In contrast, Iranti belongs to Knowledge & Memory using local stdio subprocess. Select Neotoma when you need capabilities focused on knowledge & memory and Iranti when you require tools for knowledge & memory.

More alternatives to Neotoma More alternatives to Iranti Knowledge & Memory category hub