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jlab-mcp

jlab-mcp

by kdkyum
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

start_new_session

Launch a new JupyterLab session on GPU compute nodes by submitting a SLURM job, starting a kernel, and creating a notebook for high-performance computation experiments.

Instructions

Start a new session: submit SLURM job, start kernel, create notebook.

Args: experiment_name: Name for the experiment/notebook.

Returns: Dict with session_id, notebook_path, job_id, hostname.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "experiment_name": "<experiment_name>"
    },
    "name": "start_new_session"
  }
}

Input Schema

NameRequiredDescriptionDefault
experiment_nameYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the actions (submit job, start kernel, create notebook) but omits details like required permissions, potential side effects (e.g., resource allocation), or error handling. It adds some context but falls short of fully describing behavioral traits for a tool that initiates processes.

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

Conciseness5/5

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

The description is front-loaded with the core purpose in the first sentence, followed by structured sections for Args and Returns. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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

Completeness4/5

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

Given the tool's complexity (initiates multiple operations) and no annotations, the description does well by outlining actions, parameters, and return values. The presence of an output schema reduces the need to detail returns, but it could benefit from more behavioral context, such as execution time or failure modes, to be fully complete.

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

Parameters4/5

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

The input schema has 0% description coverage, so the description must compensate. It provides the parameter 'experiment_name' with a clear meaning ('Name for the experiment/notebook'), adding essential semantics beyond the schema. However, it doesn't specify constraints like length or allowed characters, leaving some gaps.

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 the specific action ('Start a new session') and enumerates the three concrete operations it performs: 'submit SLURM job, start kernel, create notebook.' It distinguishes itself from sibling tools like 'shutdown_session' or 'start_session_continue_notebook' by emphasizing it's for initiating a new session from scratch.

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

Usage Guidelines4/5

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

The description implies usage context by specifying it's for starting a 'new session,' which differentiates it from siblings like 'start_session_continue_notebook' or 'start_session_resume_notebook' that handle existing sessions. However, it lacks explicit guidance on when not to use it or detailed prerequisites, such as whether prior setup is needed.

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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