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alexmartinsgomes

mcp-monte-carlo

by alexmartinsgomes
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have fully distinct purposes: one generates forward Monte Carlo forecasts, while the other inspects model fit without simulating. Each description explicitly states when to use it and when not to, so there is no realistic ambiguity.

    Naming Consistency5/5

    Both tool names follow a consistent verb-first snake_case pattern: forecast_asset... and inspect_asset.... The shared '_asset_' segment reinforces that they operate on the same domain, and there is no mix of naming conventions.

    Tool Count3/5

    Two tools is on the low edge of what feels like a reasonable server surface. Each tool serves a necessary role in the workflow, but the server is minimal and could feel thin to agents expecting additional financial utilities.

    Completeness4/5

    For the stated purpose, the core workflow is covered: fit/inspect the model and run forecasts. There is no obvious dead end for the main Monte Carlo use cases, though a direct historical data or backtesting tool would make the surface more complete.

  • Average 4.9/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 4 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior4/5

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

    With no annotations, the description carries the responsibility of explaining behavior. It clearly discloses that no path simulation occurs, describes the diagnostic nature of the tool, and enumerates the returned JSON contents. It does not explicitly state it is read-only or mention failure/edge-case behavior, but 'Inspect' and 'WITHOUT simulating paths' strongly imply a non-destructive diagnostic operation.

    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 well-structured and front-loaded: purpose and key limitation first, usage guidance second, exclusions and alternative third, return value summary fourth, and parameters last. Every sentence adds useful information; the example questions support correct invocation without being redundant.

    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 single-parameter diagnostic tool with an output schema present, the description covers all necessary invocation context: what the tool does, what it does not do, when to use it, what it returns, and the parameter format. Nothing an agent needs to decide between this and the sibling tool is missing.

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

    Parameters5/5

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

    The input schema provides only a title for 'ticker', so the description fully compensates by defining it as a 'Yahoo Finance ticker symbol' with concrete examples ('SPY, AAPL'). This resolves exactly what format the parameter should take, covering the entire gap left by the 0% schema description coverage.

    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 states a specific verb ('Inspect') and a precise resource ('EGARCH + skewed-t model fit for a ticker'), and immediately differentiates itself from the Monte Carlo sibling by saying 'WITHOUT simulating paths.' This makes the tool's purpose unmistakable and distinguishes it from forecast_asset_monte_carlo.

    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?

    It explicitly says when to use this tool ('validate data quality or model sanity before (or instead of) a full Monte Carlo forecast'), gives concrete example questions, and states clearly what NOT to use it for, naming the alternative tool for those cases. This is exemplary usage guidance.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior5/5

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

    With no annotations provided, the description carries the full burden, and it delivers thoroughly. It discloses the data source ('Downloads max adjusted daily closes'), the exact model ('EGARCH(1,1) with leverage (o=1) and skewed-t innovations'), and the simulation step ('simulates n_paths paths'). This gives the agent a clear behavioral model of what happens when invoked.

    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 compact and front-loaded, with each sentence serving a purpose: purpose, when-to-use, model details, sibling routing, and parameter explanation. No filler or repetition of structured schema fields.

    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?

    Given an output schema exists, return-value details need not be in the description. For a two-parameter tool, the description fully covers selection criteria, invocation parameters, model behavior, and alternative routing. There is no missing information an agent would need to call it 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 description coverage is 0%, so the description must compensate. The Args section adds meaningful detail: ticker examples ('SPY, AAPL') and n_paths constraints ('default 5000, minimum 100'). The default is redundant with the schema, but the minimum and ticker examples are new and useful.

    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 opens with a specific verb-object statement: 'Run a forward Monte Carlo forecast of an asset's future price distribution.' It further enumerates concrete outputs (percentiles, volatility, drawdown, loss/gain probabilities) and explicitly contrasts with the sibling inspect_asset_model by noting diagnostics vs. simulation.

    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?

    It gives direct when-to-use guidance: 'Use this when the user wants scenario ranges, risk, or path statistics.' It also names the alternative with an explicit condition: 'Prefer inspect_asset_model first only when you need fit/data diagnostics without simulating paths.' This leaves no ambiguity about tool selection.

    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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