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gapup-mcp

by io.github.getgapup

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

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, and the description reinforces this with 'pure-compute' and 'no external data needed.' It adds significant behavioral transparency by detailing performance (<3s for 10k×30y), the output (distribution, paths, drawdown, Sharpe), and built-in assumptions (ticker defaults), which go beyond the annotations.

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 front-loaded with the core purpose, then logically covers modes, defaults, ICP, and performance. Most sentences earn their place, but the 'ICP' line is arguably filler and could be omitted without losing tool guidance. Still, it remains well-structured and not overly verbose given the tool's complexity.

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?

With no output schema, the description compensates by clearly stating what is returned (full probability distribution, percentile paths, drawdown stats, Sharpe ratio). It also covers mode specifics, performance, and defaults, making it self-contained for an agent to invoke correctly across the 10-parameter, 4-required tool.

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 coverage is 100% for the 10 parameters, so the baseline is 3. The description adds meaningful context by explaining the mode values (glide_path→110-age allocation, stress_test→4 crises) and enumerating default ticker return/volatility percentages, which are not present in the schema. This enriches parameter understanding beyond the basic property descriptions.

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 specifies a clear action (Monte Carlo portfolio simulation) using a precise method (GBM) and distinct resources (multi-asset portfolio, terminal wealth distribution). It also differentiates itself through modes (simulate, glide_path, stress_test) and the 'pure-compute' nature, distinguishing it from sibling tools like financial_model_3statement.

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?

Provides clear context on intended users (asset managers, family offices, etc.) and mode-specific scenarios (glide_path for target-date, stress_test for historical crises). However, it does not explicitly mention when not to use the tool or name alternative tools, so there is no direct exclusions.

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

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.

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