mcp-server-worldquantbrain

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Get a dataset

get_dataset

Read-onlyIdempotent

Retrieve one WorldQuant BRAIN dataset by dataset_id to view its complete description and understand the data available for alpha research.

Instructions

Return one dataset with its full description, for example dataset_id 'pv1'.

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"
  }
}

Input Schema

Name	Required	Description	Default
`dataset_id`	Yes

Output Schema

Name	Required	Description	Default
`id`	Yes
`name`	No
`delay`	No
`region`	No
`category`	No
`coverage`	No
`universe`	No
`user_count`	No
`alpha_count`	No
`description`	No
`field_count`	No
`subcategory`	No
`value_score`	No
`pyramid_multiplier`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 11, 2026

TDQS

B3.4/5.0

Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint, so the safety profile is fully covered by structured data. The description adds that the response includes the full description (not just metadata fields) and shows an example identifier format. It does not mention auth requirements, error behavior for unknown ids, or freshness/caching of dataset metadata.

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?

A single front-loaded sentence with the core action first and the example value trailing. No filler or redundant restatement of the tool name. Slightly terse given it is the only prose available to the agent.

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?

An output schema exists, so return values need not be explained, and with one required parameter the surface area is small. Still, for an openWorld read tool the description omits id discovery guidance and failure behavior, which are the main things an agent could get wrong.

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 0% – the dataset_id parameter is an undescribed bare string. The description partially compensates by giving a concrete example value ('pv1'), which conveys the expected format. However it says nothing about how to obtain valid ids (e.g., via list_datasets) or whether the id is case-sensitive.

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 verb and resource: "Return one dataset with its full description." The singular framing implicitly distinguishes it from list_datasets, and the example id 'pv1' anchors what a dataset identifier looks like. It stops short of explicitly naming the sibling list tool or the datafield tool, so differentiation is only implied.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied rather than stated: fetch the full record for a dataset you already have an id for. There is no explicit guidance on when to use this instead of list_datasets to discover ids, or get_datafield for field-level detail, and no note about what happens with an invalid or unknown dataset_id.

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