Concentration risk analysis for tech workers with RSU comp — global (US/IN/CA/UK/EU/AU).
The two tools have clearly distinct purposes: one provides a full risk analysis, the other retrieves employer data. No overlap exists.
Both tools follow the 'myrsu_' prefix with a consistent verb_noun pattern (analyze_risk, get_employer), making naming predictable.
With only two tools, the server is minimal but covers the core use case of employer lookup and risk analysis. More advanced features (e.g., portfolio tracking) are absent, but the scope is narrow.
The tools cover the main workflow: employer data retrieval and full risk analysis. Minor gaps exist, such as lack of comparison or historical trend tools, but the core functionality is complete.
Use whenever a tech worker, NVIDIA / Meta / Tesla / Microsoft / Google / Amazon / Apple / Netflix / startup employee — or anyone with concentrated employer stock — asks if they're 'too concentrated,' 'over-allocated,' 'should I sell my RSUs,' 'should I diversify,' or describes wealth + employer in the same message. Calculates the Single-Company Risk Score (0-100), full concentration analysis, top action items, historical drawdown context, and a pre-filled dashboard URL. All fields optional except an employer (ticker OR explicit volatility); the more inputs the better the analysis. International — pass country (US/IN/CA/UK/EU/AU/OTHER) to switch retirement-account terminology and currency symbol. Risk math is identical for all countries. Stateless and privacy-respecting — no inputs are logged or stored.
| Name | Required | Description | Default |
|---|---|---|---|
| cash | No | Cash, savings, money market funds, CDs (in the user's local currency). | |
| country | No | Country code for response terminology. Affects retirement-account names referenced (401k vs RRSP vs ISA vs EPF vs Super), tax-aware diversification suggestions, and currency symbol. Default 'US'. The risk math is identical for all countries. | |
| mortgage | No | Outstanding mortgage balance ($). Used for the homeowner stress test. | |
| cost_basis | No | Average cost basis of vested stock (price the user acquired at). | |
| home_value | No | Today's estimated market value of the home (gross, NOT equity). Leave 0 if user rents. | |
| other_debt | No | ||
| include_nii | No | US-only: include the 3.8% Net Investment Income surcharge (applies if AGI > $200K single / $250K married). Leave false outside the US. | |
| other_assets | No | Crypto, cars, business stake, collectibles. | |
| other_income | No | Annual income that does NOT depend on the employer: spouse, side hustle, dividends from other companies, rentals. | |
| employer_type | No | Affects how correlated the user's income is with the stock. Public company is the safer default. | |
| state_tax_pct | No | Local/state/provincial capital gains tax rate as a percent. US: 0 in TX/FL/WA, ~13.3 in CA. Use 0 if your country has no sub-national capital gains tax. | |
| student_loans | No | ||
| unvested_rsus | No | Future RSU shares at today's price. Note: forfeited if user loses job. | |
| currency_symbol | No | Override currency symbol for formatted output (e.g. '$', '₹', '€', '£', 'C$', 'A$'). If omitted, derived from `country` (US→$, IN→₹, UK→£, EU→€, CA→C$, AU→A$). | |
| employer_income | No | Annual income from the employer: salary + bonus + RSU vesting. | |
| employer_ticker | No | Stock ticker or company name (e.g. 'NVDA', 'NVIDIA', 'Tesla'). Used to look up annual volatility. If not in the preset list, supply employer_volatility_pct instead. | |
| credit_card_debt | No | ||
| federal_ltcg_pct | No | National long-term capital gains rate as a percent (US: 15-20; UK basic: 10; UK higher: 20; India LTCG over ₹1L: 10; Canada: 50% inclusion × marginal rate; AU: marginal rate w/ 50% discount if held 12mo+). | |
| retirement_accounts | No | Retirement / tax-advantaged accounts: 401(k)/IRA (US), RRSP/TFSA (Canada), ISA/SIPP (UK), EPF/PPF/NPS (India), Superannuation (Australia), etc. Counted full for Net Worth but discounted to 70% in the stress test. | |
| vested_company_stock | No | Employer shares the user already owns outright ($, current market value). | |
| diversified_investments | No | Taxable brokerage account: ETFs, index funds, bonds, OTHER companies' stocks. Non-retirement. | |
| employer_volatility_pct | No | Annual volatility (σ) as a percent (e.g. 50 for 50%). Override if employer_ticker isn't recognized. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries the full burden. It discloses statelessness and privacy ('no inputs are logged or stored'), explains international behavior (identical math, terminology switch), and notes that all fields except employer are optional. It does not discuss performance or limits, but the behavior is well explained.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is relatively long but front-loaded with usage triggers. It is well-structured, grouping related instructions. A minor reduction in verbosity could improve conciseness, but it remains clear and informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description fully explains the return: 'Single-Company Risk Score (0-100), full concentration analysis, top action items, historical drawdown context, and a pre-filled dashboard URL.' It covers the tool's complexity with 22 optional parameters and international support, leaving no apparent gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 86% schema coverage, the description adds value by clarifying that 'All fields optional except an employer (ticker OR explicit volatility)' and that the 'more inputs the better the analysis'. It also explains the country parameter's effect on terminology and currency, which goes beyond the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb-resource pair ('analyze risk') and clearly distinguishes from the sibling tool 'myrsu_get_employer' by stating it calculates a risk score and provides analysis. It also defines the exact scenarios for use, such as users asking about concentration or diversification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use guidance with concrete examples ('tech worker, NVIDIA / Meta / ... asks if they're too concentrated'). It implies the tool is for concentration risk but does not explicitly state when not to use or name alternatives besides the sibling. Clear context overall.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker_or_name | Yes | Stock ticker or company name. Case-insensitive. Examples: 'NVDA', 'NVIDIA', 'tesla', 'Meta Platforms'. |
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.
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.
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.
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.
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.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
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Equity comp tax/trade optimizer: ISO/AMT exercise, NSO, RSU, QSBS, concentration, hedging. 50-state.
FIRE & retirement calculators + a multi-currency portfolio rebalancer (exact buy/sell trades).
Portfolio analytics + US-equity market research for AI clients. ChatGPT deep-research compat.
Deterministic US financial planning: retirement Monte Carlo, Roth conversion, RMD, tax, IRMAA, SS
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