jules-mcp
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- -licenseAqualityNot gradedmaintenanceEnables automation of Google Jules AI coding assistant through task creation, code review automation, repository management, and AI-powered development workflows. Supports multiple session modes including cloud deployment with persistent authentication.13-
- AlicenseBqualityNot gradedmaintenanceEnables orchestration of multiple Jules AI workers for tasks like code generation, bug fixing, and review using the Google Jules API. It features git integration, a shared memory system, and real-time activity monitoring for complex, multi-agent development workflows.317 npm4-
- FlicenseAqualityDmaintenanceEnables LLM applications to interact with Google's Jules AI coding assistant to manage repositories, coding sessions, and pull requests. It allows users to programmatically create tasks, approve plans, and communicate with the assistant during active coding sessions.9-
- AlicenseNot gradedqualityAmaintenanceMCP server for batch-dispatching coding tasks to Google Jules in parallel, with YAML task definitions, plan approval, and optional LLM-based task planning. Integrates with Claude Code and OpenAI Codex CLI as a tool.32 npm4MIT
- AlicenseBqualityCmaintenanceExposes Google Jules AI capabilities for automated coding tasks, including session management, code reviews, and unified diff handling. It enables users to create sessions, approve plans, and synchronize AI-generated code changes with GitHub repositories.2611 npmMIT
- FlicenseBqualityBmaintenanceEnables Codex to interact with Google Jules through MCP, allowing discovery of connected GitHub repositories, starting coding sessions, monitoring activity, sending follow-up instructions, approving plans, and retrieving results and pull-request links.8-
TDQS
Scored across 31 tools
Most tools map to distinct resources or lifecycle stages, and the dispatch family is differentiated by concurrency and wait semantics. A few pairs, such as check_events vs wait_for_task and verify_patch vs apply_patch, could cause confusion, but the descriptions are generally clear enough to guide selection.
All tools share the jules_ snake_case prefix and mostly follow a verb_object pattern, with predictable list/get and create/dispatch groupings. Exceptions like recipe_dispatch, batch_dispatch, pool_status, and auto_nudge_all break the strict verb-first convention but remain readable.
At 31 tools, the surface feels heavy and exceeds the typical well-scoped range. Several highly granular tools such as get_activity, get_media_artifacts, and inspect_bash_logs could plausibly be consolidated without losing core functionality, though the broad Jules lifecycle domain does justify some of the count.
The tool set covers the full session lifecycle from dispatch through waiting, monitoring, patching, PR management, archival, and deletion. Notable gaps include no explicit cancel/abort running session and no source creation or connection management, but these are mostly peripheral to the core automation workflow.