Skip to main content
Glama
Sign In
gemini-pro-mcp

gemini-pro-mcp

by Muzaqil555
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

jules_start

Start an asynchronous Jules coding session in a GitHub repository, letting the agent work on a task and optionally open a pull request; confirm the task before launch.

Instructions

Start a Jules session: Google's coding agent works on a task in one of the user's GitHub repositories, in the cloud and asynchronously, within the Google AI Pro plan's Jules limits (no extra cost). It can push a branch and open a pull request, so confirm the task with the user first.

    Then poll with jules_status, answer questions with jules_message, and
    approve the plan with jules_approve_plan if you asked for approval. For a
    change in a local folder use gemini_code instead.

    Args:
        prompt: The task with acceptance criteria, e.g. "Add input validation
            to the /signup endpoint and tests for it".
        repo: Repository from jules_sources, e.g. "github/owner/repo"; omit for
            a session without a repository.
        branch: Branch Jules starts from (default "main").
        title: Optional session title; Jules makes one up otherwise.
        create_pr: True lets Jules open a pull request automatically when done.
        require_plan_approval: True makes Jules wait for jules_approve_plan
            before it changes any code.

    Returns:
        The session id and its web link (https://jules.google.com/session/<id>),
        or an error such as an unknown repository.

Input Schema

NameRequiredDescriptionDefault
repoNo
titleNo
branchNomain
promptYes
create_prNo
require_plan_approvalNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.9/5.0
Behavior5/5

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

Annotations only declare readOnly=false, openWorld=true, destructive=false. The description adds substantive context beyond that: cloud/asynchronous execution, the fact that Jules can push a branch and open a pull request (a real side effect worth warning about), that it operates under the Google AI Pro plan's Jules limits at no extra cost, and that require_plan_approval gates code changes. It also notes error behavior for unknown repositories.

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?

Front-loaded with the purpose and the safety warning, then workflow, then alternatives, then parameters — a sensible ordering. It is somewhat longer than strictly necessary, and the Returns block partially restates what the output schema already provides, though the session-link format is a small extra. Minor redundancy, not bloat.

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?

For a six-parameter, open-world, non-destructive-but-side-effectful tool, nothing an agent needs is missing: cost/quota context, side-effect warning, approval gating mechanism, sibling routing, parameter sourcing, and error cases. Since an output schema exists, the extra return-value sentence is harmless surplus rather than a gap.

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 carry the full burden, and it does: every one of the six parameters is explained with meaning, a concrete example ('Add input validation to the /signup endpoint...'), the source for valid values ('Repository from jules_sources, e.g. github/owner/repo'), stated defaults, and the null/omit case for repo.

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?

States a specific verb and resource ('Start a Jules session') and immediately explains what the underlying agent does: Google's coding agent working on a task in one of the user's GitHub repos, in the cloud asynchronously. It also differentiates from the sibling gemini_code for local-folder changes, so the agent can route correctly without opening either schema.

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

Usage Guidelines5/5

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

Explicitly names the alternative ('For a change in a local folder use gemini_code instead'), prescribes the follow-up workflow (poll with jules_status, answer questions with jules_message, approve with jules_approve_plan), and flags the prerequisite of confirming the task with the user before starting. When-to-use, when-not-to-use, and sequencing are all covered.

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

Deploy Server

Other Tools