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@arizeai/phoenix-mcp

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by Arize-ai

upsert-prompt

Create or update AI prompt templates with model settings, enabling versioned prompt management for LLM applications.

Instructions

Create or update a prompt with its template and configuration. Creates a new prompt and its initial version with specified model settings.

Example usage: Create a new prompt named 'email_generator' with a template for generating emails

Expected return: A confirmation message of successful prompt creation

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
templateYes
model_nameNogpt-4
descriptionNo
temperatureNo
model_providerNoOPENAI
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions 'create or update' but does not disclose update behavior (e.g., whether it overwrites or creates a new version), side effects, permissions, or limits. The description is vague on the mutation aspects.

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

Conciseness3/5

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

The description is front-loaded with purpose, then provides example usage and expected return. It is relatively concise but the example could be shortened. Every sentence adds some value, but the structure is adequate.

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 6 parameters, no output schema, and no annotations, the description is incomplete. It does not explain the return value in detail, error handling, or update semantics. The example only covers creation, leaving significant gaps for a complex upsert operation.

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 coverage is 0%, but the description only indirectly mentions 'template', 'configuration', and 'model settings'. It does not explain individual parameters like model_name, temperature, model_provider, or description. The example only uses name and template, leaving other parameters undocumented.

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 'Create or update a prompt with its template and configuration' using a specific verb and resource. It distinguishes from sibling tools like get-prompt-by-identifier and list-prompt-versions, which are read-only. However, the example only shows creation, slightly missing the update aspect.

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 explicit guidance on when to use this tool vs alternatives. It implies usage for creating/updating prompts but does not mention when to avoid it or when to use sibling tools like get-prompt-by-identifier for retrieval. The description lacks context for decision-making.

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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