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Rockfish MCP Server

Rockfish MCP Server

Official
by Rockfish-Data
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_dataset_query_bookmarks

Retrieve the query bookmarks for a dataset to review saved queries and support dataset analysis.

Instructions

Retrieve the query bookmarks for 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": {
      "dataset_id": "<dataset_id>"
    },
    "name": "get_dataset_query_bookmarks"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idYesDataset ID

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It only implies a read operation via 'Retrieve' but omits permissions, rate limits, return format, pagination, or whether bookmarks are user-specific, providing minimal transparency beyond the obvious.

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?

The description is a single sentence, front-loaded with the verb and with no wasted words. It is appropriately sized for a simple retrieval tool.

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 simple read tool with no annotations and no output schema, the description is minimally adequate but lacks context about what bookmarks are returned (e.g., format, owner scope). With no output schema to explain return values, the description should ideally provide more detail to be fully complete.

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 single dataset_id parameter is fully documented in the schema. The description adds no meaning beyond 'for a dataset,' so the baseline of 3 applies when the schema does the heavy lifting.

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?

The description states a specific verb ('Retrieve') and resource ('query bookmarks') with scope ('for a dataset'), making the tool's function immediately clear. It does not differentiate from sibling tools like get_dataset or execute_sql_query, but the resource is distinct enough that an agent can identify it without ambiguity.

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?

No guidance is provided on when to use this tool versus alternatives, prerequisites, or context. The description only states what it retrieves, leaving the agent to infer usage without any when-not conditions or routing hints.

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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