Lightdash
This lightdash-mcp-server, developed by Ryo Okubo, provides integration between Lightdash and AI systems using the Model Context Protocol. Built with TypeScript and leveraging the @modelcontextprotocol/sdk, it offers a standardized interface for accessing Lightdash data and analytics capabilities. The server uses environment variables for configuration and includes robust error handling. By abstracting Lightdash functionality into MCP tools, this implementation facilitates use cases such as automated reporting, data exploration, and analytics-driven decision making. It is designed for easy deployment in containerized environments, making it valuable for organizations looking to enhance their Lightdash workflows with AI-powered insights.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-19.
Scan details: Circle-IR · 2026-09-19 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- devops-ci
- Source
- PulseMCP
- Repository
- github.com/syucream/lightdash-mcp-server
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve Lightdash from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.