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Look up employer volatility and drawdown history

myrsu_get_employer

Look up annual volatility (σ), historical peak-to-trough drawdowns, and the recommended max concentration cap for 40+ tech employer presets (NVDA, TSLA, MSFT, GOOGL, META, AAPL, AMZN, plus SaaS / cloud / semis / consumer / fintech / mobility). Use this when a user mentions their employer but you don't yet have their wealth numbers — gives quick context. Accepts ticker or name (case-insensitive). If outside the preset list, ask the user for a volatility estimate and use myrsu_analyze_risk directly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ticker_or_nameYesStock ticker or company name. Case-insensitive. Examples: 'NVDA', 'NVIDIA', 'tesla', 'Meta Platforms'.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool accepts ticker or name case-insensitively and focuses on presets. While it doesn't explicitly state non-destructiveness, 'look up' implies read-only, and the description is otherwise clear.

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 two concise sentences plus a conditional fallback. It is front-loaded with purpose, then usage, then alternative. No unnecessary words.

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

Completeness5/5

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

For a simple tool with one parameter and no output schema, the description covers what it does, when to use it, what it returns (volatility, drawdowns, cap), and how to handle cases outside the preset list.

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% and the schema already describes the parameter well. The description adds examples and reinforces case-insensitivity, but does not provide substantial new meaning beyond what the schema offers.

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

Purpose5/5

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

The description clearly states it looks up volatility, drawdowns, and concentration cap for 40+ tech employer presets, naming specific tickers and categories. It distinguishes from the sibling tool myrsu_analyze_risk by specifying when to use the alternative.

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

Usage Guidelines5/5

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

Explicitly says 'Use this when a user mentions their employer but you don't yet have their wealth numbers' and provides a clear alternative: 'If outside the preset list, ask the user for a volatility estimate and use myrsu_analyze_risk directly.'

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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