mcp-server-worldquantbrain

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Get self-correlation

get_alpha_self_correlation

Read-onlyIdempotent

Return an alpha's highest correlation with your already submitted alphas, capped at 0.7, to check whether a new signal duplicates your existing work.

Instructions

Return the alpha's highest correlation with the user's submitted alphas (limit 0.7).

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": {
      "alpha_id": "<alpha_id>"
    },
    "name": "get_alpha_self_correlation"
  }
}

Input Schema

Name	Required	Description	Default
`alpha_id`	Yes	BRAIN alpha id, for example 'qMPjAxnO'.
`wait_seconds`	No

Output Schema

Name	Required	Description	Default
`top`	No
`kind`	Yes
`count`	No
`passes`	No
`pending`	No
`alpha_id`	Yes
`next_step`	No
`threshold`	Yes
`max_correlation`	No
`min_correlation`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 11, 2026

TDQS

B3.2/5.0

Behavior3/5

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

Annotations already declare readOnly, idempotent, non-destructive, so the safety profile is covered. The description adds the '0.7' bound, which is real behavioral context, but it is ambiguous — it is unclear whether 0.7 is a cap applied to reported values or a threshold that filters results — and it says nothing about the potentially long-running wait_seconds behavior.

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 no filler. It is appropriately sized for a simple getter, though the parenthetical '(limit 0.7)' is cryptic enough that its brevity costs some clarity.

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. However, for a tool with an undocumented wait parameter and an unexplained numeric bound, the description leaves the agent guessing about invocation timing and result semantics.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 50%: alpha_id is documented in the schema, but wait_seconds has no description anywhere. The description does not compensate at all — it never mentions the wait/polling parameter, its 300s default, or its 900s ceiling, which materially affects how an agent should call this tool.

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+resource: it returns the alpha's highest correlation against the user's own submitted alphas. That is distinguishable from get_alpha_prod_correlation, but the definition never names that sibling, so an agent must infer the distinction from the resource wording alone.

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 only implied: an agent can guess this is for checking self-correlation before submission, but there is no explicit when-to-use statement, no exclusion, and no pointer to the prod-correlation or check_alpha_submission alternatives.

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