vertex-mcp-server

by app.run.us-central1.vertex-mcp-server-1069561025565

write_data_driven_optimize_config

Constructs a JSON configuration for Data-Driven Optimize and uploads it to GCS.

    This tool generates a JSON configuration file based on the provided
    parameters and uploads it to the specified Google Cloud Storage URI. This
    configuration file is required to run data-driven prompt optimization.

    Args:
      gcs_config_uri: The GCS URI where the generated VAPO config JSON file will
        be saved (e.g., 'gs://my-bucket/vapo/config.json').
      prompt_optimizer_method: The method for prompt optimization. Either
        'VAPO' or 'OPTIMIZATION_TARGET_GEMINI_NANO'.
      target_model_endpoint_url: The custom endpoint URL for the target model.
        Required for Gemini Nano target.
      base_config: Optional. A dictionary representing the base configuration.
      modifications: Optional. A dictionary representing the modifications to
        apply to the base config.
      base_config_path: Optional. Path to a base config file. If provided and
        `base_config` is None, this config will be loaded.

    Returns:
      A string containing a success message and details about the uploaded
      configuration file, including a link to the Vertex AI console.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`base_config`	No
`modifications`	No
`gcs_config_uri`	Yes
`base_config_path`	No
`prompt_optimizer_method`	Yes
`target_model_endpoint_url`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A3.9/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden. It explains the main behavior (config generation and upload) and return value, but omits potential side effects, error conditions, or prerequisites (e.g., GCS permissions, config validation).

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 a docstring format, front-loading the main purpose. While somewhat lengthy, it is organized and each sentence adds value. Could be slightly more concise, but overall effective.

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

Completeness4/5

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

Given the tool has 6 parameters (2 required) and an output schema, the description explains the return value and parameter roles. It does not address edge cases like conflicting base_config and base_config_path, but overall provides sufficient context for an agent to use the tool.

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

Parameters4/5

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

Schema description coverage is 0%, but the description compensates by providing clear explanations for each parameter (e.g., gcs_config_uri: 'The GCS URI where the generated VAPO config JSON file will be saved'). This adds significant meaning beyond the schema property names.

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 constructs a JSON configuration for Data-Driven Optimize and uploads it to GCS. It uses a specific verb-resource pair ('Constructs a JSON configuration... uploads it to GCS') and distinguishes itself from siblings by focusing on configuration generation rather than execution.

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

Usage Guidelines3/5

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

The description implies usage ('required to run data-driven prompt optimization') but does not explicitly state when to use this tool versus alternatives like run_data_driven_optimize or run_few_shot_optimization. No exclusions or alternatives are mentioned.

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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TDQS

B3.1/5.0

Disambiguation4/5

Tools are generally distinct, with prompt CRUD operations clearly separated from optimization workflows. However, 'analyze_data_driven_optimize_results' and 'generate_html_report' could be confused as both relate to results analysis, and 'call_dashscope_mcp' is an outlier with a different purpose.

Naming Consistency5/5

All tools follow a consistent snake_case verb_noun pattern (e.g., create_prompt, run_data_driven_optimize, write_data_driven_optimize_config). The naming is predictable and clear, with no mixed conventions.

Tool Count5/5

11 tools is appropriate for a Vertex AI prompt optimization and management server. It covers prompt CRUD, optimization jobs, config generation, analysis, and reporting without being excessive or insufficient.

Completeness4/5

The tool surface covers prompt lifecycle (CRUD) and key optimization workflows (data-driven, few-shot, config, analysis, report). Minor gaps exist, such as no dedicated tool to fetch optimization job status separately (though wait_for_completion is an option), and 'call_dashscope_mcp' feels extraneous.

Resources

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