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Rockfish MCP Server

Rockfish MCP Server

Official
by Rockfish-Data
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Python
Remote

obtain_train_config

Generate a training configuration for synthetic data models, returning a cached train_config_id for start_training_workflow. Supports TabGAN and TimeGAN.

Instructions

Generate training configuration for synthetic data models. Returns a cached train_config_id for use in start_training_workflow. Supports TabGAN (rf_tab_gan) and TimeGAN (rf_time_gan).

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": {
      "dataset_id": "<dataset_id>",
      "model_type": "<model_type>"
    },
    "name": "obtain_train_config"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idYesDataset ID to generate config for
model_typeYesModel type: 'rf_tab_gan' for tabular data or 'rf_time_gan' for time-series data

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full behavioral burden. It does disclose that the config is cached and returned as an ID, which hints at repeat-call behavior, but it never states side effects, whether regenerating overwrites an existing config, cost/latency, or permission requirements for a generation operation.

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

Conciseness5/5

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

Three short sentences, front-loaded with purpose before the return value and supported types. No filler, no restatement of the tool name; every sentence carries information an agent needs.

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?

For a two-required-parameter tool with no output schema, the description usefully substitutes for return documentation by naming the train_config_id and its consumer. It is nearly complete; only error/prerequisite behavior and regeneration semantics are unaddressed.

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

Parameters3/5

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

Schema description coverage is 100% and model_type carries an enum with per-value guidance, so the schema already documents both parameters fully. The description only restates the model type identifiers, adding no syntax, constraints, or edge cases beyond what structured fields provide — baseline 3.

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?

Specific verb+resource ('Generate training configuration for synthetic data models') with the return contract stated ('cached train_config_id') and the downstream consumer named ('start_training_workflow'). Supported model types are enumerated, so the agent knows exactly what this tool produces and where it fits relative to the workflow siblings.

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?

The description clearly positions this as the step that feeds start_training_workflow, which tells the agent when to reach for it. It stops short of explicit exclusions (e.g., when to use update_train_config instead of regenerating) or prerequisites about the dataset_id needing to exist, so it is clear context without routing rules.

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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