mcp-server-worldquantbrain

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Get alpha PnL

get_alpha_pnl

Read-onlyIdempotent

Summarize an alpha's profit and loss by year, optionally including recent daily cumulative values for performance review.

Instructions

Summarize an alpha's PnL by year, with optional recent daily cumulative values.

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_pnl"
  }
}

Input Schema

Name	Required	Description
`last_n`	No	Only the last N days.
`alpha_id`	Yes	BRAIN alpha id, for example 'qMPjAxnO'.
`include_daily`	No	Add daily rows, at most 500.

Output Schema

Name	Required	Description	Default
`daily`	No
`yearly`	No
`alpha_id`	Yes
`end_date`	No
`total_pnl`	No
`truncated`	No
`start_date`	No
`total_rows`	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 readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint, so the safety profile is fully covered. The description adds the useful framing that returns are aggregated by year and that daily values are cumulative, but says nothing about latency, whether data must come from an existing simulation, or gaps when an alpha has no PnL history — which is meaningful for an openWorld read.

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?

One tightly written sentence with the purpose and both output modes front-loaded; no filler or repetition. It is arguably over-terse given the absence of usage guidance, but nothing in it is wasted.

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-value shape need not be explained, and all three parameters are fully documented in the schema. Still, for a data-retrieval tool sitting among many alpha siblings, the definition leaves the agent without usage context or any note about data availability assumptions.

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 coverage is 100%, so the schema already documents alpha_id format, last_n bounds, and include_daily's 500-row cap; baseline is 3. The description adds only a small amount beyond the schema by characterizing the daily rows as 'cumulative' values, but it does not explain how last_n interacts with include_daily or whether the year summary is restricted by last_n.

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 names a specific verb (summarize) and resource (an alpha's PnL) and scopes the output as by-year with optional recent daily cumulative values. This is clearly distinct from siblings like get_alpha or check_alpha_submission, though it never explicitly names an alternative to differentiate against.

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 when-to-use statement, no exclusion, and no named alternative (e.g., when to prefer this over get_alpha for performance review or get_simulation_status for raw results). The agent must infer the trigger condition purely from the tool name and one-sentence purpose.

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