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@arizeai/phoenix-mcp

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by Arize-ai

list-datasets

Retrieve available datasets containing inputs, outputs, and metadata for use as experiment inputs in the Arize Phoenix platform.

Instructions

Get a list of all datasets.

Datasets are collections of 'dataset examples' that each example includes an input, (expected) output, and optional metadata. They are primarily used as inputs for experiments.

Example usage: Show me all available datasets

Expected return: Array of dataset objects with metadata. Example: [ { "id": "RGF0YXNldDox", "name": "my-dataset", "description": "A dataset for testing", "metadata": {}, "created_at": "2024-03-20T12:00:00Z", "updated_at": "2024-03-20T12:00:00Z" } ]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo

Implementation Reference

  • Handler function that fetches the list of datasets from the PhoenixClient API using GET /v1/datasets and returns the JSON-stringified data.
    async ({ limit }) => {
      const response = await client.GET("/v1/datasets", {
        params: {
          query: { limit },
        },
      });
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify(response.data?.data, null, 2),
          },
        ],
      };
    }
  • Zod input schema defining the optional 'limit' parameter (1-100, default 100).
    {
      limit: z.number().min(1).max(100).default(100),
    },
  • Tool registration call using McpServer.tool() with name 'list-datasets', description, input schema, and handler function.
    server.tool(
      "list-datasets",
      LIST_DATASETS_DESCRIPTION,
      {
        limit: z.number().min(1).max(100).default(100),
      },
      async ({ limit }) => {
        const response = await client.GET("/v1/datasets", {
          params: {
            query: { limit },
          },
        });
        return {
          content: [
            {
              type: "text",
              text: JSON.stringify(response.data?.data, null, 2),
            },
          ],
        };
      }
    );
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It states the tool returns an array of dataset objects with metadata and includes an example, which helps understand the output format. However, it does not cover important aspects like whether this is a read-only operation, potential rate limits, pagination behavior (only mentions a 'limit' parameter in the schema without description), or error handling, leaving gaps in behavioral context.

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

Conciseness4/5

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

The description is well-structured and appropriately sized. It starts with the core purpose, adds explanatory context about datasets, includes an example usage, and details the expected return with a clear example. Most sentences earn their place, though the example return could be slightly condensed without losing clarity.

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?

Given no annotations, no output schema, and low schema description coverage (0%), the description provides some completeness by explaining datasets and the return format. However, it misses key details like parameter semantics, behavioral traits (e.g., read-only nature, pagination), and usage guidelines relative to siblings, making it only partially adequate for a tool with one parameter and no structured support.

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

Parameters2/5

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

The input schema has one parameter ('limit') with 0% schema description coverage, meaning the schema does not explain its purpose. The description does not mention any parameters, failing to compensate for this gap. It should have explained what 'limit' does (e.g., maximum number of datasets to return) to add value beyond the schema.

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: 'Get a list of all datasets.' It specifies the verb ('Get') and resource ('datasets'), and explains what datasets are ('collections of dataset examples...'). However, it does not explicitly differentiate from sibling tools like 'list-projects' or 'list-experiments-for-dataset,' which keeps it from a score of 5.

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

Usage Guidelines3/5

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

The description implies usage through the example 'Show me all available datasets,' suggesting it's for retrieving all datasets. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get-dataset-experiments' or 'list-experiments-for-dataset,' and does not mention any exclusions or prerequisites, such as authentication needs.

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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