Skip to main content
Glama
asanchezleache

ipython-kernel-mcp

by asanchezleache
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Related Servers

Alternatives to ipython-kernel-mcp

No user-submitted related servers found.

    Related Servers

    • Jupiter Controller MCP

      nufelroknidev
      A
      license
      Not graded
      quality
      C
      maintenance
      Connects Claude Code to local Jupyter kernels for code execution, kernel management, and notebook execution, with security gating and output capture.
      PolyForm Noncommercial 1.0.0

    • kernel-mcp

      Luckylobster8868
      F
      license
      Not graded
      quality
      B
      maintenance
      Provides a persistent Jupyter kernel for executing code, inspecting variables and dataframes, and checking SQL query plans, enabling agents to work with stateful Python sessions.
      -

    • loommux

      MichengLiang
      A
      license
      A
      quality
      A
      maintenance
      Enables interactive Python execution with a persistent IPython kernel through MCP, retaining namespace state and providing structured output logs for agent and tool integrations.
      8
      Apache 2.0

    • notebook-agent-mcp

      KJH-Sun
      F
      license
      Not graded
      quality
      D
      maintenance
      Enables AI agents to execute Jupyter notebook cells with persistent kernel state, output persistence, and structured JSON control surface.
      2
      -

    • ipython-mcp

      husams
      A
      license
      B
      quality
      B
      maintenance
      Provides a persistent, stateful IPython execution environment for MCP clients, allowing agents to run Python code, define functions and classes, and maintain state across calls. It includes tools for execution, inspection, dynamic tool registration, and robust timeout/recovery handling.
      11
      MIT
    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

    A4.1/5.0

    Scored across 4 tools

    Disambiguation5/5

    Each tool performs a clearly distinct function: connecting, executing, checking status, and interrupting. There is no meaningful overlap or ambiguity between tool purposes.

    Naming Consistency4/5

    Most tools follow a verb-first naming pattern (connect_to_kernel, execute_code, interrupt_kernel), but kernel_status is noun-first and breaks the pattern slightly. Overall the naming remains predictable and readable.

    Tool Count5/5

    Four tools is a well-scoped count for a focused IPython kernel integration. Each tool fills a necessary role without unnecessary bloat.

    Completeness4/5

    The core lifecycle of connect, execute, check status, and interrupt is covered. Missing disconnect or restart operations are minor gaps that agents can work around, but the main workflows are supported.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources