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list_experiments

View all CloudLab experiments to monitor and manage research projects, enabling control of nodes, access to logs, and experiment lifecycle oversight.

Instructions

List all your CloudLab experiments

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Handler for the list_experiments tool. Fetches the list of experiments from the CloudLab API endpoint '/experiments' and returns the result as formatted JSON text.
    case "list_experiments": {
      const result = await cloudlabRequest("/experiments");
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify(result, null, 2),
          },
        ],
      };
    }
  • src/index.ts:100-108 (registration)
    Registration of the list_experiments tool in the ListTools response, including name, description, and input schema (no parameters required).
    {
      name: "list_experiments",
      description: "List all your CloudLab experiments",
      inputSchema: {
        type: "object",
        properties: {},
        required: [],
      },
    },
  • Input schema for list_experiments tool, which requires no parameters.
    inputSchema: {
      type: "object",
      properties: {},
      required: [],
    },
Behavior2/5

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 states the tool lists experiments but doesn't describe output format, pagination, permissions needed, or other behavioral traits like rate limits or side effects.

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 a single, efficient sentence with no wasted words, clearly front-loading the purpose. It's appropriately sized for a simple tool.

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

Completeness3/5

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

For a simple list tool with no parameters and no output schema, the description is minimally adequate. However, without annotations or output details, it lacks completeness in explaining what the list includes or how results are structured.

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 tool has 0 parameters, and the schema description coverage is 100%, so no parameter information is needed. The description doesn't add param semantics, but this is acceptable given the lack of parameters, aligning with the baseline for 0 params.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('List') and resource ('CloudLab experiments'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'get_experiment' which might retrieve a specific experiment, so it doesn't reach the highest score.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives like 'get_experiment' for retrieving a single experiment. The description implies usage for listing all experiments but lacks explicit context or exclusions.

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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