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mcp-knowledge

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

A4.4/5.0
Behavior4/5

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

Annotations already indicate read-only and open-world. Description adds performance metric (<3s) and confirms it's pure-compute with no side effects, consistent with annotations. No 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?

Packed with relevant information, well-structured from purpose to details. Could be slightly shorter but every sentence adds value. Good front-loading.

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?

Covers inputs, outputs, modes, performance, default assumptions, and intended users. No output schema, but output types are described. Sufficient for an agent to use correctly.

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 has 100% coverage with descriptions. Tool description adds value by explaining default ticker returns/volatilities and the meaning of modes, enhancing parameter understanding.

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?

Clearly states it's a Monte Carlo portfolio simulation using GBM, lists outputs (distribution, percentiles, drawdown, Sharpe), and three distinct modes. Distinguishes from data-fetching tools by emphasizing 'no external data needed'. Provides default ticker assumptions.

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?

Explains three modes with concrete examples and intended use cases. Lists target users (ICP). Does not explicitly state when not to use, but the context is sufficient for most agents.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

Resources