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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 across 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
include_experiment_projectsNo
Behavior2/5

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

No annotations provided, so the description carries full burden. It does not disclose that the tool is read-only, does not mention pagination behavior, rate limits, or effects. The return example is given but without explanation of how parameters affect behavior.

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?

Description is short and front-loaded with the main action. Includes an example return which is helpful but slightly verbose. Overall efficient with no filler.

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 parameters with zero schema descriptions and no output schema, the description fails to provide complete context. Lacks documentation on how to paginate, what cursor means, and how include_experiment_projects affects results. The example output is partial.

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%, so the description must explain parameters. It does not mention limit, cursor, or include_experiment_projects at all. The parameter names are somewhat self-explanatory but missing details like cursor format or pagination mechanism.

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?

Clearly states it returns a list of all projects, but could be more precise about pagination given the limit and cursor parameters. The description distinguishes it from sibling tools like get-project implicitly.

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?

No guidance on when to use this tool versus alternatives (e.g., get-project for a single project). Only provides an example usage 'Show me all available projects' but no explicit when-not-to-use or sibling tool comparisons.

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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