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Rockfish MCP Server

Rockfish MCP Server

Official
by Rockfish-Data
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

start_training_workflow

Initiates a training workflow with a cached config, auto-detecting the model type. Use after obtaining the train_config_id.

Instructions

Build and start a training workflow using a cached config. Automatically detects model type from train_config structure. Use this after obtaining train_config_id from obtain_train_config.

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>",
      "train_config_id": "<train_config_id>"
    },
    "name": "start_training_workflow"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idYesDataset ID to train on
train_config_idYesConfig ID from obtain_train_config (retrieves cached config)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the burden and does disclose two traits: it performs a mutation (build and start) and auto-detects model type from train_config structure. It omits permissions/auth needs, whether the workflow runs asynchronously, failure behavior, and reversibility, so it is only partially transparent.

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?

Three sentences, front-loaded with the core action and scope, with no wasted wording. The auto-detection and prerequisite notes each add information rather than padding.

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?

For a mutation tool with no annotations and no output schema, the description should explain what the agent gets back (e.g., a workflow ID) and whether execution is asynchronous. It covers the input-side prerequisite but leaves the outcome and lifecycle underspecified.

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 coverage is 100%, so both parameters are already documented in the schema, establishing a baseline of 3. The description adds modest value by clarifying that train_config is a cached config whose structure drives model-type detection, but no format or syntax detail beyond the 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?

States a specific compound verb (build and start) and resource (training workflow), which clearly separates it from the sibling start_generation_workflow and the obtain_* tools. It does not explicitly name and contrast the closest sibling, but the verb+resource is unambiguous.

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 gives the prerequisite sequence: 'Use this after obtaining train_config_id from obtain_train_config', naming the dependency tool. However, it offers no exclusions or conditions for when NOT to use it versus starting a generation workflow.

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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