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Arize-ai

@arizeai/phoenix-mcp

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by Arize-ai

list-projects

Retrieve all projects, each with a unique name and metadata, for organizing traces 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 are provided, so the description must carry the burden. It explains that projects are containers for observability data and gives an example return format. However, it does not disclose behaviors like pagination, rate limits, or whether the list is exhaustive over time.

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 relatively compact but includes an example and explanation of projects, which may be slightly verbose. The structure is clear with a header, explanation, example, and expected return, but could be trimmed.

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 the tool's simplicity and the lack of output schema, the description provides an example return object but omits parameter guidance. It is minimally complete for a straightforward list tool but could be more thorough.

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

Parameters1/5

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

The schema has three parameters (limit, cursor, include_experiment_projects) with 0% coverage in the description. The description does not mention any parameters or their meaning, so it adds no 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 'Get a list of all projects' and explains what projects are, making the purpose evident. However, it does not explicitly differentiate from sibling tools like 'get-project' or other list tools, which would merit a higher 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?

The description provides an example usage ('Show me all available projects') but no guidance on when to use this tool versus alternatives like 'get-project' for a single project, or when not to use it. No context about prerequisites or limitations.

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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