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Ask GitHub Copilot (new session)

copilot_ask

Get coding assistance by submitting prompts to GitHub Copilot in a fresh session, with optional model and workspace settings.

Instructions

Ask the GitHub Copilot CLI (copilot -p) a question or task in a NEW session.

Uses your existing Copilot login (OS credential store, or a COPILOT_GITHUB_TOKEN/GH_TOKEN/GITHUB_TOKEN env var — see copilot_status). Returns the agent's final message, read straight from stdout (the CLI's -s silent mode; no scraping). Copilot is a capable agentic coder — good for real code/repo work; point workspace at a project dir for context-aware answers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoOptional model override (`--model`, e.g. "gpt-5.3-codex"); omit to use your account's default. An unavailable model errors immediately.
watchNoIf true, open a live "watch" view streaming copilot's steps from its `--output-format json` event stream. Same final text is returned. Best-effort. Default false.
promptYesQuestion or instruction for Copilot.
sandboxNoPermission policy (maps to copilot's tool/path flags): "read-only" (default — best-effort: denies the local write/shell tools; NOT an OS sandbox, so unlike codex it is not a hard boundary), "workspace-write" (may edit files, confined to the workspace), or "danger-full-access" (--allow-all — avoid).read-only
timeout_sNoMax seconds to wait for copilot to complete. Default 180. (Copilot's reasoning models can be slow; raise this if needed.)
workspaceNoWorking root for the session (`-C`). Defaults to the server cwd.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations (readOnlyHint=false, openWorldHint=true, idempotentHint=false) are complemented by description detailing stdout retrieval, silent mode, and sandbox limitations. No contradictions; adds valuable behavioral context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise, front-loaded with purpose. Every sentence adds value, no fluff. Well-structured with clear flow.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers authentication, usage, parameters, behavior, and links to siblings. Output schema exists so return value explanation not needed. Complete for a 6-parameter tool with annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% so baseline is 3. Description adds meaning: model override error behavior, watch live stream explanation, sandbox permission policy detail, timeout reasoning for slow models, and workspace default. Adds significant value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description uses specific verb 'ask' and identifies the resource 'GitHub Copilot CLI' in a new session. It clearly states the tool's function and distinguishes from siblings like copilot_continue and copilot_status.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides context on authentication, return value, and recommended use for real code/repo work. Implicitly differentiates from sibling tools (e.g., copilot_status for checking status). Lacks explicit 'when not to use' guidance but still very clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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