Networkx MCP Server vs Dingo

In-depth architectural comparison of the Networkx MCP Server and Dingo 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.

Which MCP Server Should You Choose?

Choose Networkx MCP Server when:

• You need dedicated capabilities in the Data Science Tools domain.
• You prefer local stdio subprocess transport architecture.
• Your security boundary fits: No auth required (Free / Open Source).
• Primary tools included: NetworkX graph creation and editing, Citation analysis through CrossRef, Centrality, PageRank, and community algorithms.

Explore Networkx MCP Server Details

Choose Dingo when:

• You need dedicated capabilities in the Data Science Tools domain.
• You prefer local stdio subprocess transport architecture.
• Your security boundary fits: No auth required (Free / Open Source).
• Primary tools included: Rule-based data quality evaluation, LLM-based quality assessment, Rules and prompts discovery.

Feature & Specification Comparison

Specification	Networkx MCP Server Bright-L01 Data Science Tools	Dingo DataEval Data Science Tools
Summary	The first NetworkX integration for Model Context Protocol, enabling graph analysis and visualization directly in AI conversations. Supports 13 operations including centrality algorithms, community detection, PageRank, and graph visualization.	MCP server for the Dingo: a comprehensive data quality evaluation tool. Server Enables interaction with Dingo's rule-based and LLM-based evaluation capabilities and rules&prompts listing.
Category & Scope

Tools & Capabilities Breakdown

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).

{
  "mcpServers": {
    "bright-l01-networkx-mcp-server": {
      "command": "uvx",
      "args": [
        "networkx-mcp-server"
      ]
    }
  }
}

{
  "mcpServers": {
    "dataeval-dingo": {
      "command": "uvx",
      "args": [
        "dingo-python"
      ]
    }
  }
}

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