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@@ -175,6 +175,7 @@ Official integrations are maintained by companies building production ready MCP
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**[Databricks](https://docs.databricks.com/aws/en/generative-ai/mcp/)** - Connect to data, AI tools & agents, and the rest of the Databricks platform using turnkey managed MCP servers. Or, host your own custom MCP servers within the Databricks security and data governance boundary.
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**[DataHub](https://github.com/acryldata/mcp-server-datahub)** - Search your data assets, traverse data lineage, write SQL queries, and more using [DataHub](https://datahub.com/) metadata.
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**[Daytona](https://github.com/daytonaio/daytona/tree/main/apps/cli/mcp)** - Fast and secure execution of your AI generated code with [Daytona](https://daytona.io) sandboxes
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**[Datawrapper](https://github.com/palewire/datawrapper-mcp)** -A Model Context Protocol (MCP) server for creating [Datawrapper](https://datawrapper.de) charts using AI assistants.
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**[Debugg.AI](https://github.com/debugg-ai/debugg-ai-mcp)** - Zero-Config, Fully AI-Managed End-to-End Testing for any code gen platform via [Debugg.AI](https://debugg.ai) remote browsing test agents.
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**[DeepL](https://github.com/DeepLcom/deepl-mcp-server)** - Translate or rewrite text with [DeepL](https://deepl.com)'s very own AI models using [the DeepL API](https://developers.deepl.com/docs)
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**[Defang](https://github.com/DefangLabs/defang/blob/main/src/pkg/mcp/README.md)** - Deploy your project to the cloud seamlessly with the [Defang](https://www.defang.io) platform without leaving your integrated development environment