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@arizeai/phoenix-mcp

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by Arize-ai

get-dataset-experiments

Retrieve all experiments conducted on a specific dataset to analyze testing history and metadata.

Instructions

List experiments run on a dataset.

Example usage: Show me all experiments run on dataset RGF0YXNldDox

Expected return: Array of experiment objects with metadata. Example: [ { "id": "experimentid1234", "dataset_id": "datasetid1234", "dataset_version_id": "datasetversionid1234", "repetitions": 1, "metadata": {}, "project_name": "Experiment-abc123", "created_at": "YYYY-MM-DDTHH:mm:ssZ", "updated_at": "YYYY-MM-DDTHH:mm:ssZ" } ]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetIdYes

Implementation Reference

  • The core handler function for the "get-dataset-experiments" tool. It fetches experiments for the given datasetId via the PhoenixClient API and returns a text content block with the JSON-stringified response data.
    async ({ datasetId }) => {
      const response = await client.GET(
        "/v1/datasets/{dataset_id}/experiments",
        {
          params: {
            path: { dataset_id: datasetId },
          },
        }
      );
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify(response.data, null, 2),
          },
        ],
      };
    }
  • Zod input schema for the tool, defining 'datasetId' as a required string parameter.
    {
      datasetId: z.string(),
    },
  • Registration of the "get-dataset-experiments" MCP tool using McpServer.tool(), including the tool name, description reference, input schema, and inline handler function.
    server.tool(
      "get-dataset-experiments",
      GET_DATASET_EXPERIMENTS_DESCRIPTION,
      {
        datasetId: z.string(),
      },
      async ({ datasetId }) => {
        const response = await client.GET(
          "/v1/datasets/{dataset_id}/experiments",
          {
            params: {
              path: { dataset_id: datasetId },
            },
          }
        );
        return {
          content: [
            {
              type: "text",
              text: JSON.stringify(response.data, null, 2),
            },
          ],
        };
      }
    );
  • Description constant for the "get-dataset-experiments" tool, providing usage examples and expected return format.
    const GET_DATASET_EXPERIMENTS_DESCRIPTION = `List experiments run on a dataset.
    
    Example usage:
      Show me all experiments run on dataset RGF0YXNldDox
    
    Expected return:
      Array of experiment objects with metadata.
      Example: [
        {
          "id": "experimentid1234",
          "dataset_id": "datasetid1234",
          "dataset_version_id": "datasetversionid1234",
          "repetitions": 1,
          "metadata": {},
          "project_name": "Experiment-abc123",
          "created_at": "YYYY-MM-DDTHH:mm:ssZ",
          "updated_at": "YYYY-MM-DDTHH:mm:ssZ"
        }
      ]`;
  • Invocation of initializeDatasetTools function during MCP server setup, which registers the dataset tools including "get-dataset-experiments".
    initializeDatasetTools({ client, server });
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses the return format (array of experiment objects) and provides a detailed example structure, which is valuable behavioral information. However, it doesn't mention pagination, rate limits, authentication requirements, error conditions, or whether this is a read-only operation (though 'List' implies reading).

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 well-structured and appropriately sized. It starts with a clear purpose statement, provides a practical usage example, and then details the expected return format with a comprehensive example. Every sentence earns its place, and the information is front-loaded with the core functionality stated first.

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 no annotations, 0% schema description coverage, and no output schema, the description does a good job of explaining what the tool does and what it returns. The detailed example return format compensates for the lack of output schema. However, for a tool with siblings like 'list-experiments-for-dataset', more differentiation would improve completeness.

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?

With 0% schema description coverage for the single parameter 'datasetId', the description must compensate. While it doesn't explicitly describe the parameter in the text, the example usage 'Show me all experiments run on dataset RGF0YXNldDox' demonstrates the parameter's purpose and format (a string identifier). This provides meaningful context beyond the bare schema, though it could be more explicit about parameter requirements.

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 tool's purpose with 'List experiments run on a dataset' - a specific verb ('List') and resource ('experiments run on a dataset'). It distinguishes from some siblings like 'get-experiment-by-id' (specific vs. all) and 'list-experiments-for-dataset' (similar but not identical naming). However, it doesn't explicitly differentiate from 'list-experiments-for-dataset' which appears to be a direct sibling with potentially overlapping functionality.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to use this versus 'list-experiments-for-dataset' (a clear sibling), 'get-experiment-by-id' (for specific experiments), or 'list-datasets' (to first identify datasets). The example shows basic usage but offers no contextual decision-making help.

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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