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gapup-mcp

by io.gapup.mcp

monte_carlo_portfolio

Read-only

Pure-compute Monte Carlo portfolio simulation using Geometric Brownian Motion (GBM). Models a multi-asset portfolio across time with contributions, withdrawals, and annual rebalancing. Returns full probability distribution of terminal wealth, percentile paths, drawdown stats, and Sharpe ratio. Modes: simulate (full Monte Carlo) | glide_path (lifecycle 110-age target-date allocation) | stress_test (4 historical crises: 2008 GFC / 2000 dotcom / 1970s stagflation / 2020 COVID). No external data needed — all computed from asset assumptions. Ticker defaults built-in: SPY/VOO/VTI 7%/15%, QQQ 9%/20%, TLT/BND 3%/6%, GLD 5%/18%, BTC 30%/70%. ICP: asset managers, family offices, retail wealth advisors, robo-advisor agents, retirement planners. 10k simulations × 30 years runs in <3s on V8 JIT.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYessimulate = full Monte Carlo GBM | glide_path = lifecycle target-date allocation | stress_test = 4 historical crisis scenarios
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
assetsYesPortfolio assets. Weights must sum to 1.0 (auto-normalized if not).
simulationsNoNumber of Monte Carlo simulations (1000-100000). Default 10000.
horizon_yearsYesInvestment horizon in years (1-50).
target_value_eurNoTarget terminal portfolio value in EUR. Used to compute probability_target_achieved.
confidence_intervalsNoPercentiles to compute in the output distribution. Default [5, 25, 50, 75, 95].
initial_investment_eurYesInitial capital in EUR (e.g. 100000 for €100k).
withdrawals_annual_eurNoAnnual withdrawal amount in EUR for decumulation phase (e.g. 50000 for €50k/yr).
contributions_annual_eurNoAnnual contribution in EUR (e.g. 12000 for €1000/month).

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint and openWorldHint, which the description reinforces by stating 'no external data needed.' It adds behavioral details like the computational speed (<3s for 10k simulations × 30 years) and the use of default ticker assumptions, going beyond annotations without contradiction.

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?

The description is a single paragraph but packs significant detail without redundancy. It could benefit from more structured formatting (e.g., bullet points for modes), but it remains concise and front-loads the core purpose.

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

Completeness4/5

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

Given the tool's complexity (10 parameters, 3 modes, no output schema), the description covers the core functionality, performance, target users, and default values. It does not detail the exact output structure but mentions key outputs (distribution, percentile paths, drawdown stats, Sharpe ratio). More detail on error conditions or exact output format would improve completeness, but it is largely adequate.

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

Parameters4/5

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

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining default ticker assumptions (SPY 7%/15%, etc.) and the meaning of each mode (simulate, glide_path, stress_test with specific crisis scenarios), which are not fully detailed in the schema.

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 precisely states it is a Monte Carlo portfolio simulation using GBM, with clear modes and output types. It distinguishes itself from sibling financial tools by specifying its unique functionality and target users.

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

Usage Guidelines4/5

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

The description identifies the ideal customer profile (asset managers, family offices, etc.) and enumerates three distinct modes (simulate, glide_path, stress_test), providing context for when to use each. However, it does not explicitly state when not to use this tool or contrast with alternative tools.

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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TDQS

C2.5/5.0
Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

Resources