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Rockfish MCP Server

Rockfish MCP Server

Official
by Rockfish-Data
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

update_dataset

Modify an existing dataset's name or description by supplying its ID. This keeps Rockfish AI dataset metadata accurate for synthetic data and ML workflows.

Instructions

Update a dataset

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": {
      "id": "<id>"
    },
    "name": "update_dataset"
  }
}

Input Schema

NameRequiredDescriptionDefault
idYesDataset ID
nameNoDataset name
descriptionNoDataset description

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2/5.0
Behavior1/5

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

With no annotations present, the description carries the full behavioral burden and discloses nothing. It does not say whether this is a partial or full replacement update, what happens to omitted fields, whether permissions are required, or whether the change is reversible.

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

Conciseness2/5

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

The single sentence is short but that is under-specification rather than conciseness. There is no wasted wording, yet the sentence does not earn its place by conveying any information an agent could act on.

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

Completeness2/5

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

The three parameters are simple and fully documented in the schema, so basic invocation is feasible, but critical semantics for a mutation tool (patch vs replace behavior, effect of omitted fields) are absent from both the description and the annotations, leaving the definition thin.

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% (id, name, description all documented in the schema), so the baseline is 3 even though the description contributes no parameter meaning. It does not clarify, for example, that omitting name or description leaves those fields unchanged.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Update a dataset' merely restates the tool name in sentence form, adding no scope, no list of updatable fields, and no distinction from siblings like update_project, update_workflow, or update_database. An agent learns nothing beyond what the name already conveys.

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?

There is no guidance on when to use this tool versus siblings such as create_dataset or delete_dataset. No prerequisites, no mention of whether it is safe to call repeatedly, and no stated alternatives.

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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