| name | mcp-integrations |
| description | Use whenever the task involves an external integration connected through Executor — Notion, Context7, Supabase, Vercel, or any other MCP surfaced by mcp__executor__execute. Ensures these are always accessed through Executor instead of a direct MCP server, the public API, or the CLI. |
All external integrations go through Executor (mcp__executor__execute), never a direct MCP server, the public API, or a CLI shortcut. Call skills({ name: "execute" }) inside Executor first if you're unsure how to write the sandboxed code.
Discover, don't memorize. Connections and tool names change over time. Use tools.search({ query: "...", namespace: "<integration>_mcp" }) (or the bare integration name, e.g. "vercel") to find the exact tool path and argument shape. Any MCP added to Executor shows up here automatically — this skill does not keep an inventory of them.
Accounts
Several integrations have more than one connected account/organization, distinguished by a .user.<name> suffix on the namespace (e.g. notion_mcp.user.felipegiraldo, notion_mcp.user.centrodeprototipado). Which ones are split changes over time, so check with tools.search rather than assuming a given integration has only one.
felipegiraldo is the default; centrodeprototipado is only for that project.- Use the account the user tells you to use — it's the source of truth.
- If the user didn't specify and it isn't obvious, ask before writing (reading from the wrong account is harmless; creating/editing, migrations, deploys, or branches are not).
Context7 (context7_mcp)
Two-step flow, worth remembering because it isn't obvious from the tool list:
resolve_library_id({ libraryName, query }) — both required; query is the user's full question, improves relevance ranking.query_docs({ libraryId, query }) — use the library ID from step 1 (prefer exact name match, higher benchmark score, and version-specific IDs when the user names a version).
Use it when the user asks about libraries, frameworks, or API references, or needs current code examples instead of relying on training data.
