/mcp-scripting
Write mcpScript JavaScript for discovering, inspecting, and calling MCP tools.
$ npx -y skills add nicobailon/pi-mcp-adapter --skill mcp-scripting --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/mcp-scripting
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The summary Claude sees to decide when to auto-load this skill.
Write mcpScript JavaScript for discovering, inspecting, and calling MCP tools.
SKILL.md
mcp-scripting.SKILL.mdname: mcp-scripting
description: Write mcpScript JavaScript for discovering, inspecting, and calling MCP tools.
MCP scripting
For multi-call MCP work, write ordinary JavaScript with loops, filtering, chaining, fan-out, or other logic between calls. Run that source with `mcpScript`; it is the primary MCP orchestration surface. For a single MCP search, describe, status check, auth action, or tool call, use `mcp` instead.
Write the source naturally, then pass it as `mcpScript`'s `code` argument:
const { items } = await tools.search({ query: "search issues", server: "github" });
const candidate = items[0];
if (!candidate) return { error: "No matching tool" };
const details = await tools.describe({ path: candidate.path });
if (details.error) return details;
const result = await tools.call(details.path, { query: "is:open label:bug" });
if (!result.ok) return result;
emit({ tool: details.path, completed: true });
return result.data;Workflow
1. Find candidate tools with `await tools.search({ query, server?, limit?, offset? })`. 2. Inspect the exact returned path with `await tools.describe({ path })`. 3. Call it with `tools.call(path, args)`.
Calls resolve to `{ ok: true, data }` or `{ ok: false, error }`; handle failed calls instead of expecting them to stop the script. `emit(value)` adds user-visible output before the final `return` value. `console` output is captured too.
`tools` is a non-enumerable proxy: `Object.keys(tools)` throws. Always use `tools.search` for discovery. When a known flat path is a valid identifier, direct calls such as `tools.github_search_issues(args)` are supported; use bracket syntax for hyphenated names: `tools["server_tool-name"](args)`. `search`, `call`, `describe`, and promise/serialization names (`then`, `catch`, `finally`, `toJSON`, `toString`, `valueOf`) are reserved on the proxy; if a flat path collides with one, call it via `tools.call("exact-path", args)`.
`tools.search` and `tools.describe` are asynchronous and must be awaited. The default script timeout is 30 seconds; the worker is terminated at the deadline, including for infinite loops. Every invocation still uses normal lazy connection, authentication, output guarding, and approval gates. Result details contain a concise `calls` trace with every search, describe, and call operation; each entry includes its query or path, outcome, and duration.
Use plain JavaScript loops and Promise utilities for composition. Fluent helpers such as `tools.find(...).one()`, `tools.parallel(...)`, and `tools.retry(...)` are not provided.
Read more
name: mcp-scripting description: Write mcpScript JavaScript for discovering, inspecting, and calling MCP tools.
MCP scripting
For multi-call MCP work, write ordinary JavaScript with loops, filtering, chaining, fan-out, or other logic between calls. Run that source with `mcpScript`; it is the primary MCP orchestration surface. For a single MCP search, describe, status check, auth action, or tool call, use `mcp` instead.
Write the source naturally, then pass it as `mcpScript`'s `code` argument:
const { items } = await tools.search({ query: "search issues", server: "github" });
const candidate = items[0];
if (!candidate) return { error: "No matching tool" };
const details = await tools.describe({ path: candidate.path });
if (details.error) return details;
const result = await tools.call(details.path, { query: "is:open label:bug" });
if (!result.ok) return result;
emit({ tool: details.path, completed: true });
return result.data;Workflow
1. Find candidate tools with `await tools.search({ query, server?, limit?, offset? })`. 2. Inspect the exact returned path with `await tools.describe({ path })`. 3. Call it with `tools.call(path, args)`.
Calls resolve to `{ ok: true, data }` or `{ ok: false, error }`; handle failed calls instead of expecting them to stop the script. `emit(value)` adds user-visible output before the final `return` value. `console` output is captured too.
`tools` is a non-enumerable proxy: `Object.keys(tools)` throws. Always use `tools.search` for discovery. When a known flat path is a valid identifier, direct calls such as `tools.github_search_issues(args)` are supported; use bracket syntax for hyphenated names: `tools["server_tool-name"](args)`. `search`, `call`, `describe`, and promise/serialization names (`then`, `catch`, `finally`, `toJSON`, `toString`, `valueOf`) are reserved on the proxy; if a flat path collides with one, call it via `tools.call("exact-path", args)`.
`tools.search` and `tools.describe` are asynchronous and must be awaited. The default script timeout is 30 seconds; the worker is terminated at the deadline, including for infinite loops. Every invocation still uses normal lazy connection, authentication, output guarding, and approval gates. Result details contain a concise `calls` trace with every search, describe, and call operation; each entry includes its query or path, outcome, and duration.
Use plain JavaScript loops and Promise utilities for composition. Fluent helpers such as `tools.find(...).one()`, `tools.parallel(...)`, and `tools.retry(...)` are not provided.
Use MCP servers with Pi without burning your context window.
Repo: nicobailon/pi-mcp-adapter

