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fvegiard

copilot-cloud-agent-mcp

by fvegiard

create_task

Start a Copilot cloud-agent task on a repository. Provide a natural-language prompt to automate research, edits, and pull request creation.

Instructions

Start a new Copilot cloud-agent task on a repository.

POST /agents/repos/{owner}/{repo}/tasks

Args: owner: Repo owner (user or org login). repo: Repo name. prompt: Natural-language instruction for the agent. base_ref: Branch the agent should base work on (defaults to repo default). head_ref: Branch the agent should push work to (optional). model: Optional model override. Known values: claude-sonnet-4.6, claude-opus-4.6, gpt-5.2-codex, gpt-5.3-codex, gpt-5.4, claude-sonnet-4.5, claude-opus-4.5. Omit for auto-selection. create_pull_request: If true, the agent opens a PR when done.

Returns: Task object including id, state, html_url, artifacts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repoYes
modelNo
ownerYes
promptYes
base_refNo
head_refNo
create_pull_requestNo
Behavior3/5

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

Annotations are absent, so the description carries full burden. It explains parameters and returns a Task object, but it does not disclose behavioral traits such as async nature (the task likely runs asynchronously), rate limits, auth requirements, or potential costs. Some transparency is present via parameter explanations, but broader behavioral context is missing.

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

Conciseness4/5

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

The description is well-structured with a concise opening sentence followed by a clear parameter list resembling a docstring. While slightly verbose, each sentence earns its place. It efficiently communicates purpose and parameter details without redundancy.

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

Completeness3/5

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

The description covers all 7 parameters and returns a Task object, but lacks context about async behavior (e.g., that the task may be long-running and requires polling via get_task or wait_for_task). No mention of error states, limitations, or usage scenarios beyond the basic action. Given no output schema, the return info helps but completeness is limited.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate. It fully explains each parameter's purpose, including owner, repo, prompt, base_ref, head_ref, model (with known values listed), and create_pull_request. This adds significant meaning beyond the raw schema structure.

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?

The description clearly states 'Start a new Copilot cloud-agent task on a repository,' identifying the specific verb (start), resource (Copilot cloud-agent task), and scope (on a repository). It also includes the HTTP endpoint and distinguishes from sibling tools like get_task and list_tasks by implying creation vs retrieval.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, when to use list_tasks or wait_for_task instead, or any context for choosing this tool over others. The description only states what it does, not when or why.

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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