Skills for “mcp”

Starting point for exploring how a PostHog MCP server's tools are used — routes a broad question to the typed tool that answers it. Use when the user asks "how is my MCP doing?", "what should I look at?", "explore my…

Install, configure, and use the Grafana MCP server (mcp-grafana) effectively. Covers setup via Docker or binary, environment variables, CLI flags, choosing tools, RBAC, and best practices for context window management.…

This skill should be used when the user wants to build an "MCP app", add "interactive UI" or "widgets" to an MCP server, "render components in chat", build "MCP UI resources", make a tool that shows a "form", "picker",…

Investigate individual PostHog MCP sessions — the sequence of tool calls a single agent made in one run, what it was trying to do, and where it went wrong. Use when the user asks "what did this MCP session do?", "show…

Mandatory Step 0 pre-flight check for regional Google Cloud MCP servers (e.g. Dataproc) before using MCP tools or falling back to gcloud/bq CLI. Use whenever interacting with Dataproc clusters, Spark batches, jobs,…

Connect clients to Box's hosted MCP server and operate Box through its tools. Use for Box MCP setup, authentication, missing tools, or Box tasks using an MCP connection.

Decide when Zoom MCP is the right fit and produce a safe setup plan for Claude. Use when planning AI workflows over Zoom data, deciding between MCP and REST, or defining a hybrid MCP architecture.

Explore PostHog MCP intent clusters — agent goals grouped by semantic similarity, with each cluster's tool distribution and error rates, plus the tool-centric pivot (capture rate per intent, discovery rate against the…

Build a starting-point taxonomy for an MCP tool — what users were trying to accomplish before they reached the tool — and publish it as a PostHog notebook. Reconstructs each session's goal from its opening tool calls,…

Use when working through Postman MCP tools to inspect or change workspace resources, choose a targeted operation, or handle MCP limitations. Supports hosted workflows when the CLI is unavailable and distinguishes tool…

Connect to and use the hosted Preset MCP gateway, choose between the aggregate gateway and a direct workspace Superset MCP connection, and run the list_workspaces, search_workspace_tools, call_tool flow. Use only for…

Configure, authenticate, troubleshoot, or use the SumUp MCP server at mcp.sumup.com. Use for MCP client setup, missing tools, OAuth connection failures, merchant account queries, and SumUp documentation resources.

Investigate the quality of PostHog MCP tool calls — error rates, latency, reach, and which tools are failing or slow. Use when the user asks "which MCP tool has the highest error rate?", "what's the slowest tool?",…

Design a Zoom MCP workflow for Claude. Use when deciding whether Zoom MCP fits a task, when planning tool-based AI workflows, or when separating MCP responsibilities from REST API responsibilities.

Publish the user's own gbrain over MCP so other devices, desktop apps and cloud agents can reach it. `gbrain mcp expose` installs and signs in Tailscale, publishes the running `gbrain serve --http` on the tailnet…

Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP…

Working with the redis-docs MCP server (search / fetch / ask at redis.io/mcp) — how its index is shaped and how to read what it returns. Use when those tools are available and you are about to look something up in the…

Reference for the Pinecone MCP server tools. Documents all available tools - list-indexes, describe-index, describe-index-stats, create-index-for-model, upsert-records, search-records, cascading-search, and…

Guidance for Zoom's MCP server catalog. Use after routing to an MCP workflow when planning or troubleshooting tool-based access to Meetings, Canvas, Chat, Tasks, Revenue Accelerator, Whiteboard, recordings, meeting…

Use Superset MCP tools for dataset inspection, semantic-layer querying, and virtual dataset creation. Use only for MCP tool workflows; do not use for direct API work.