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Arize-ai

@arizeai/phoenix-mcp

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by Arize-ai

list-projects

Retrieve a list of all projects to organize and access traces, spans, and observability data.

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
include_experiment_projectsNo
Behavior3/5

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

No annotations provided, so description carries full burden. It describes the return format with an example, indicating a read operation. However, it does not mention authentication, rate limits, or other side effects beyond the example.

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 concise and front-loaded with the main purpose. The example is somewhat lengthy but provides useful context. Overall, it is well-structured with minimal waste.

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

Completeness2/5

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

Given 3 undocumented parameters and no output schema, the description lacks completeness. It does not cover pagination (cursor), the effect of include_experiment_projects, or any other behavioral details beyond the basic listing.

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?

Schema description coverage is 0%, and the description provides no explanation of the three parameters (limit, cursor, include_experiment_projects). The example only shows output structure, not parameter behavior.

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 'Get a list of all projects.' It uses a specific verb and resource, and distinguishes from sibling 'get-project' which retrieves a single project.

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 for listing all projects but does not explicitly state when to use it vs alternatives like 'get-project' or provide any exclusions or conditions.

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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