Skip to main content
Glama
Sign In
Rockfish MCP Server

Rockfish MCP Server

Official
by Rockfish-Data
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

update_train_config

Update model hyperparameters and encoder field classifications such as categorical, continuous, or ignore in a Rockfish training config by config ID.

Instructions

Update training configuration. Modify model hyperparameters (epochs, batch_size, etc.) and/or encoder field classifications (categorical, continuous, ignore). See https://docs.rockfish.ai/model-train.html for config structure.

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": {
      "train_config_id": "<train_config_id>",
      "updates": "<updates>"
    },
    "name": "update_train_config"
  }
}

Input Schema

NameRequiredDescriptionDefault
updatesYesUpdates to apply: model_config for hyperparameters, encoder_config for field types
train_config_idYesConfig ID from obtain_train_config

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
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. 'Update' implies mutation, but the description never states whether it requires specific permissions, whether updates merge with or replace existing config, whether changes are reversible, or whether a running training workflow is affected. For a mutation tool with zero annotation coverage this is a real gap.

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 short sentences, front-loaded with the core action and following with the modifiable areas and a doc link. Little waste, though the URL is a pointer rather than content an agent can act on directly.

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 rich nested schema coverage and no output schema, the description covers the essentials of what is being modified and points to external docs. It is nonetheless thin on behavioral context (permissions, merge semantics, effect on active jobs) for a mutation tool with no annotations.

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%, so the nested model_config/encoder_config structure and train_config_id are already documented inline, meeting the baseline. The description's examples (epochs, batch_size, categorical/continuous/ignore) largely restate the schema rather than adding format or constraint detail.

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 clear verb+resource (update training configuration) and enumerates the two modifiable areas: model hyperparameters and encoder field classifications. An agent can distinguish it from obtain_train_config. However, it does not explicitly name the sibling relationship or contrast with start_training_workflow.

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?

The description gives no when-to-use, when-not-to-use, or alternative guidance. The only hint of workflow context ('Config ID from obtain_train_config') lives in the schema, not the description, so the agent gets no routing signal from the prose itself.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools