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Found 2,558 Skills
Storybook MCP server integration for component-aware AI development. Covers 6 tools across 3 toolsets (dev, docs, testing): component discovery via list-all-documentation/get-documentation, story previews via preview-stories, and automated testing via run-story-tests. Use when generating components that should reuse existing Storybook components, running component tests via MCP, or previewing stories in chat.
Use Dune MCP through UXC for blockchain table discovery, SQL query creation/execution, execution result retrieval, and visualization with help-first schema inspection, explicit auth binding, and guarded credit-consuming operations.
Use the LI.FI MCP server through UXC for cross-chain route discovery, bridge/DEX availability checks, token and chain lookup, gas/balance/allowance checks, quote generation, and transfer status tracking. Use when tasks involve planning or monitoring cross-chain swaps and bridges without signing or broadcasting transactions.
Use The Graph Subgraph MCP through UXC via native SSE with a fixed linked command for subgraph discovery, schema retrieval, deployment selection, and GraphQL query execution with help-first inspection and explicit auth handling.
Connect AI coding agents (Claude Code, Cursor, VS Code, OpenAI Codex) to Grafana Cloud via the Model Context Protocol (MCP) server. Use when the user asks to connect Claude Code to Grafana, set up MCP for Grafana, use Grafana tools in Cursor, query Grafana from an AI agent, configure the Grafana MCP server, or make AI agents interact with Grafana Cloud APIs. Triggers on phrases like "MCP server", "connect Claude Code to Grafana", "Grafana MCP", "AI agent Grafana", "Claude Grafana tools", "Cursor Grafana", or "agent observability".
Expert guidance for Chrome DevTools MCP server - browser automation, debugging, and performance analysis for AI agents
Expert in using next-devtools-mcp for Next.js development with AI coding agents
MCP Server Construction Methodology — Systematically build production-grade MCP tools to enable AI assistants to connect to external capabilities
Model Context Protocol (MCP) server implementation patterns with LangChain4j. Use when building MCP servers to extend AI capabilities with custom tools, resources, and prompt templates.
Model Context Protocol (MCP) server implementation patterns with Spring AI. Use when building MCP servers to extend AI capabilities with custom tools, resources, and prompt templates using Spring's official AI framework.
Use when a user wants to add MCPCat analytics to their Python MCP server, install the mcpcat Python package, or integrate mcpcat.track() into an existing Python MCP server codebase.
Use Orchata MCP tools to search, browse, and manage knowledge bases programmatically. For MCP-connected environments only.