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freeplayai

freeplay-mcp

Official
by freeplayai
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Python
Remote

optimize_prompt

Analyzes prompt template versions against labeled examples and customer feedback to generate improved versions, with optional automated testing.

Instructions

Optimize a prompt template version using AI-powered analysis and automated experimentation. This is a write operation that creates a new prompt template version. Always confirm with the user before calling this tool — describe which prompt template will be optimized, the dataset being used, and that this may incur LLM costs.

Analyzes the prompt template version against scored examples from production logs and generates an improved version with guidance from the user on where to focus. The optimization considers human evaluation labels, customer feedback, and best practices based on the flags provided.

This is a long-running operation that may take several minutes. When run_test_after_optimization is True (the default), the job will also run baseline and optimized test runs and create a comparison, which incurs additional LLM costs.

Args: project_id: The Freeplay project ID prompt_template_version_id: The prompt template version ID to optimize dataset_id: The dataset ID containing examples to analyze user_instructions: Optional specific instructions for the optimization (e.g., "focus on reducing hallucinations") use_best_practices: Whether to apply general prompt engineering best practices (default: True) use_labels: Whether to use human evaluation labels from the dataset in analysis (default: True) use_customer_feedback: Whether to incorporate customer feedback data (default: True) run_test_after_optimization: Whether to run a comparison test after optimization (default: True)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_idYes
project_idYes
use_labelsNo
user_instructionsNo
use_best_practicesNo
use_customer_feedbackNo
prompt_template_version_idYes
run_test_after_optimizationNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.7/5.0
Behavior5/5

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

With no annotations, the description fully discloses behavior: it's a write operation, long-running, creates a new version, incurs LLM costs, and explains the effect of each boolean flag. No contradiction.

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?

Well-structured with key warnings upfront and bullet-like parameter list. Slightly verbose but every sentence adds value; could be tightened slightly without losing clarity.

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

Completeness5/5

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

Given it has an output schema, return values are covered elsewhere. The description fully explains all parameters, behavioral nuances, prerequisites (user confirmation), and cost/performance implications for a complex 8-parameter tool.

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

Parameters5/5

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

Schema coverage is 0%, so the description must compensate. It lists all 8 parameters with clear explanations, e.g., 'user_instructions: Optional specific instructions for the optimization (e.g., focus on reducing hallucinations)', adding significant meaning beyond the schema.

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 the tool's purpose: 'Optimize a prompt template version' and specifies it is a write operation that creates a new version. It distinguishes from siblings like 'create_prompt_version' by focusing on AI-powered optimization.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly instructs to confirm with the user before calling, mentioning cost implications and what to relay. However, no direct exclusions or comparisons to alternatives are provided.

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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