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hzy9981

hzy9981

by hzy9981
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

run_data_driven_optimize

Starts an automated prompt optimization job on Vertex AI using a dataset and configurable metrics. Provide a config file in GCS and choose between VAPO or Gemini Nano methods.

Instructions

Starts a data-driven prompt optimization job on Vertex AI.

This method uses a dataset and configurable metrics. The config_gcs_path must point to a JSON file in Google Cloud Storage.

Args: config_gcs_path: The Google Cloud Storage URI (e.g., "gs://your-bucket/config.json") to a JSON file containing the Prompt Optimizer configuration. This is required. service_account: The service account email to run the job. This is required. prompt_optimizer_method: The method for prompt optimization. Either 'VAPO' or 'OPTIMIZATION_TARGET_GEMINI_NANO'. wait_for_completion: If True, the tool will block until the Vertex AI CustomJob completes. Defaults to False.

Returns: A string indicating the status and details of the optimization job, including a link to the Vertex AI console.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "config_gcs_path": "<config_gcs_path>",
      "service_account": "<service_account>",
      "prompt_optimizer_method": "<prompt_optimizer_method>"
    },
    "name": "run_data_driven_optimize"
  }
}

Input Schema

NameRequiredDescriptionDefault
config_gcs_pathYes
service_accountYes
wait_for_completionNo
prompt_optimizer_methodYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool starts a job and can block if wait_for_completion is True. However, it does not mention destructive behavior (e.g., does it modify existing data?), authentication needs, rate limits, or side effects. The return value is described as a string with status and a console link, but no details on error states.

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 well-structured with an introductory sentence, separate 'Args' and 'Returns' sections, and concise bullet-style explanations. It is not overly verbose, though the parameter descriptions could be slightly tightened (e.g., 'This is required' is redundant). The introductory sentence is front-loaded.

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 has 4 parameters, no annotations, and an output schema (content not provided), the description covers the inputs and return string adequately. However, it lacks information on error handling, prerequisites (e.g., having the config JSON file properly formatted), or consequences of failures. For a tool that kicks off a long-running job, more context would be beneficial.

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 description coverage is 0%, so the description must fully compensate. It does so by providing clear, detailed explanations for each parameter: config_gcs_path is a GCS URI to a JSON file, service_account is an email, wait_for_completion defaults to False, and prompt_optimizer_method accepts specific enum values. This adds significant meaning beyond the raw 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 it starts a data-driven prompt optimization job on Vertex AI using a dataset and configurable metrics. It names the specific resource (Vertex AI CustomJob). However, it does not explicitly differentiate from sibling tools like 'run_few_shot_optimization' or 'analyze_data_driven_optimize_results'.

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 is provided on when to use this tool vs alternatives. It does not mention prerequisites, such as the need to have a config file prepared or what types of optimization problems are suited for this tool. Sibling tools exist but are not referenced for comparison.

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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