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cursor-cloud-agents-mcp

by TygartMedia
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Python
Remote

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    • cursor-cloud-agents-mcp

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      Enables AI assistants and other MCP clients to launch and steer Cursor Cloud Agents on repositories or as repo-less research tasks, including model selection, mid-run follow-ups, cancellation, result retrieval, and run listing. Also exposes authentication checks and per-agent or per-run token usage and cost reporting.
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    • cursor-mcp

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      Enables delegating coding tasks to Cursor Cloud Agents from Claude Code, Codex, or Grok, with scoped repository access, progress monitoring, follow-ups, and result reporting.
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    • cursor-agents-mcp

      6e6p1k
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      Enables an orchestrator like Claude Code to hand coding work off to Cursor SDK agents, run as detached background jobs that survive the session and can be listed, inspected, steered, resumed, or stopped. Agents are asynchronous and reusable across follow-up turns, letting expensive frontier models delegate cheaply without blocking or paying to read the results.
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    • Cloud Agent MCP Server

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    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A4/5.0

    Scored across 9 tools

    Disambiguation4/5

    Tools map to distinct lifecycle actions (launch, poll status, wait for result, follow up, cancel, list, discover models, verify identity, check usage). The only real overlap is cursor_status vs cursor_result, but their descriptions clearly distinguish granular polling from a blocking final-answer wrapper.

    Naming Consistency4/5

    All tools share a consistent cursor_ prefix and snake_case formatting, making the set easy to scan. The minor deviation is mixing verb-style names (launch, followup, cancel, list) with noun-style names (status, result, models, usage, whoami), but the convention remains predictable.

    Tool Count5/5

    Nine tools is well-scoped for managing cloud agents, with each tool earning its place across the launch-monitor-followup-cancel lifecycle plus supporting discovery (models, identity, usage). There are no redundant or filler tools.

    Completeness4/5

    The surface covers the core lifecycle: launch, poll status, wait for result, follow up, cancel, and list agents, plus model discovery and usage checks. Missing are explicit agent archival/deletion and a dedicated get-agent-details tool, though these are minor gaps for most workflows.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources