Skip to main content
Glama
Arize-ai

@arizeai/phoenix-mcp

Official
by Arize-ai

list-projects

Retrieve all available projects to organize traces, spans, and observability data for applications or experiments.

Instructions

Get a list of all projects.

Projects are containers for organizing traces, spans, and other observability data. Each project has a unique name and can contain traces from different applications or experiments.

Example usage: Show me all available projects

Expected return: Array of project objects with metadata. Example: [ { "id": "UHJvamVjdDox", "name": "default", "description": "Default project for traces" }, { "id": "UHJvamVjdDoy", "name": "my-experiment", "description": "Project for my ML experiment" } ]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
cursorNo
includeExperimentProjectsNo

Implementation Reference

  • Handler function that fetches projects from the Phoenix API using GET /v1/projects and returns the data as a formatted JSON string in the MCP response format.
    async ({ limit = 100, cursor, includeExperimentProjects = false }) => {
      const response = await client.GET("/v1/projects", {
        params: {
          query: {
            limit,
            cursor,
            include_experiment_projects: includeExperimentProjects,
          },
        },
      });
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify(response.data?.data, null, 2),
          },
        ],
      };
    }
  • Zod input schema for the list-projects tool parameters: limit (1-100), cursor, includeExperimentProjects.
    {
      limit: z.number().min(1).max(100).default(100).optional(),
      cursor: z.string().optional(),
      includeExperimentProjects: z.boolean().default(false).optional(),
    },
  • Registers the list-projects tool on the MCP server with description, input schema, and handler function.
    server.tool(
      "list-projects",
      LIST_PROJECTS_DESCRIPTION,
      {
        limit: z.number().min(1).max(100).default(100).optional(),
        cursor: z.string().optional(),
        includeExperimentProjects: z.boolean().default(false).optional(),
      },
      async ({ limit = 100, cursor, includeExperimentProjects = false }) => {
        const response = await client.GET("/v1/projects", {
          params: {
            query: {
              limit,
              cursor,
              include_experiment_projects: includeExperimentProjects,
            },
          },
        });
        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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return format with an example, which is helpful, but doesn't mention pagination behavior (despite cursor and limit parameters), rate limits, authentication requirements, or whether this is a read-only operation. The description adds some value but leaves significant behavioral aspects undocumented.

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 appropriately sized and well-structured with clear sections: purpose statement, context about projects, example usage, and expected return format. Each sentence adds value, though the project context paragraph could be slightly more concise.

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 list tool with 3 parameters, 0% schema coverage, no annotations, and no output schema, the description provides good information about the return format but completely neglects parameter documentation. The combination of detailed return format example with zero parameter information creates an uneven level of completeness.

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?

With 0% schema description coverage for 3 parameters, the description provides no information about the parameters. It doesn't mention 'limit', 'cursor', or 'includeExperimentProjects' at all, leaving their purpose and usage completely undocumented despite the schema defining them.

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 a specific verb ('Get') and resource ('list of all projects'), and provides context about what projects are. However, it doesn't explicitly differentiate this tool from potential sibling list tools (like list-datasets, list-experiments-for-dataset) beyond the resource type.

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. While it includes an example usage ('Show me all available projects'), this is a usage example rather than contextual guidance about when this tool is appropriate versus other listing tools on the server.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Arize-ai/phoenix'

If you have feedback or need assistance with the MCP directory API, please join our Discord server