Dingo vs Pixlint

A side-by-side look at the Dingo and Pixlint MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.

Dingo logoDingoData Science Tools · Local subprocess (stdio)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.60GoodAuth: no auth requiredPixlint logoPixlintData Science Tools · Local subprocess (stdio)Lint, curate & prepare computer-vision datasets from your AI assistant — MCP server, 67 tools37FairAuth: not declared

Verdict: choose Dingo for data science tools work as a local process; choose Pixlint for data science tools as a local process. You can also run both side by side in one mcpServers block.

Which MCP server should you choose?

Dingo logoChoose Dingo when

  • You need dedicated capabilities in the Data Science Tools domain.
  • You prefer a local stdio subprocess transport.
  • Its auth model fits you: No auth required.
Explore Dingo

Pixlint logoChoose Pixlint when

  • You need dedicated capabilities in the Data Science Tools domain.
  • You prefer a local stdio subprocess transport.
  • You have the keys it needs: CV_DATA_DIR.
Explore Pixlint

Feature & specification comparison

Rows where the two servers differ are marked.

SpecDingo logoDingoPixlint logoPixlint
CategoryData Science ToolsData Science Tools
Quality signal60/100 · Good37/100 · Fair
Install commandConfirmedConfirmed
TransportLocal subprocess (stdio)Local subprocess (stdio)
AuthNo auth requiredNot declared
PricingFreeNot declared
Required env varsNone listedCV_DATA_DIR
Verified ownerCommunity listingCommunity listing
GitHub stars7590
Directory activity 5 views 0 installs 0 upvotes 0 views 0 installs 0 upvotes
ToolsNot listedNot listed

Tools & capabilities

Dingo tools

No tool names declared yet. Check the project README from the listing page.

Pixlint tools

No tool names declared yet. Check the project README from the listing page.

Ready-to-paste client configs

Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.

Dingo

mcpServers (Claude Desktop / Cursor)
{
  "mcpServers": {
    "dataeval-dingo": {
      "command": "uvx",
      "args": [
        "dingo-python"
      ]
    }
  }
}

Pixlint

mcpServers (Claude Desktop / Cursor)
{
  "mcpServers": {
    "pixlint": {
      "command": "uvx",
      "args": [
        "pixlint"
      ],
      "env": {
        "CV_DATA_DIR": "YOUR_CV_DATA_DIR_HERE"
      }
    }
  }
}

Frequently asked questions

Dingo is categorized under Data Science Tools and uses a local stdio subprocess. Pixlint belongs to Data Science Tools and uses a local stdio subprocess. Pick Dingo for data science tools work and Pixlint for data science tools.

Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.

Dingo is listed as free. Auth: no auth required. Pixlint has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.

Yes. MCP clients support multiple servers: add dataeval-dingo and pixlint as separate keys in the same mcpServers object, and your assistant will call tools from either one.

On AllMCPs, Dingo has a quality signal of 60/100 (Good) with 5 views, 0 installs and 759 GitHub stars. Pixlint has 37/100 (Fair) with 0 views, 0 installs and 0 GitHub stars.