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luxalgo-mcp-server

by io.github.LuxAlgo

Compare challenges for one trader

propfirms_compare

Simulate the SAME trader across several challenges (directory references and/or inline specs, up to 12) under identical options and seed, and return one row per challenge sorted by expected value. THIS IS NOT A RANKING: rows are ordered by EV for the caller's specific inputs - trader stats, risk sizing, and options - and a different trader profile reorders them. The tool computes data for the user's own decision; it implies no endorsement, league table, or recommendation of any firm, and results should be presented that way ('best EV for these inputs', never 'best firm'). Each row carries perAttemptPassProbability, fundedProbability, expectedAttempts, expectedCost, evTotal, pEvPositive, daysToFundedP50, and the challenge's flagsNotSimulated - challenges with more unsimulated rules have optimistic numbers, so compare flags alongside EV, not EV alone. Consistency rules and funded payout gating ARE simulated (engine v1), so EV already reflects them where a ruleset has them. For full per-challenge distributions run propfirms_simulate on the interesting rows. UNITS: every *Pct rule field and every percent-mode risk value is in PERCENT UNITS (5 = 5%, 0.5 = 0.5%). The one exception is winRate, which is a FRACTION in [0, 1] (0.55 = 55% winners). Probabilities in results are fractions in [0, 1]. DETERMINISM: identical inputs including seed reproduce byte-identical results on any platform. Include the seed and path count when reporting numbers so users can reproduce them exactly; re-run with a few different seeds to gauge Monte Carlo spread. ASSUMPTIONS: every result carries assumptions.flags - dataset-declared rules the engine does NOT simulate (e.g. scaling plans or soft daily lockouts, which make real odds worse than simulated) plus engine simplifications - and assumptions.disclaimer. These are material: always surface the flags and the disclaimer to the user alongside the numbers, never just the headline probability. Results are distributions under stated assumptions, not promises.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNoRNG seed (integer or string). Default 42. Same inputs + seed reproduce byte-identical results - include the seed when reporting so users can reproduce the numbers.
pathsNoMonte Carlo paths (independent simulated trader journeys). Default 10,000 (well under a second); capped at 100,000 per tool call. Confidence intervals shrink roughly with the square root of paths.
avgWinRYesAverage winning trade in R-multiples, i.e. multiples of the amount risked per trade (1.5 = winners average 1.5x the risk).
contextYesExplain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): "Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization."
winRateYesProbability a trade is a winner, as a FRACTION in [0, 1] (0.55 = 55% winners) - NOT percent units. The most impactful input: traders routinely overestimate it by a few points, which can flip EV negative, so prefer measured stats over self-reported ones.
winStdRNoStandard deviation of winner sizes in R (0 = every winner is exactly avgWinR). Default 0. Adding spread makes streak damage more realistic.
avgLossRNoAverage losing trade in R, as a POSITIVE number. Default 1 (losers lose exactly the risked amount, i.e. stops are honored). Raise above 1 to model slippage or blown stops.
lossStdRNoStandard deviation of loser sizes in R (0 = every loser is exactly avgLossR). Default 0.
riskModeNoHow riskValue is interpreted. 'percent-of-balance' (default): risk compounds with the current balance. 'percent-of-initial': constant currency risk derived from the initial account size - how most prop traders size, since loss limits are fixed in currency. 'fixed-amount': explicit currency risked per 1R.
riskValueYesRisk per trade - the value of 1R. PERCENT UNITS for percent modes (0.5 = 0.5% risked per trade; a typical prop range is 0.25-2), or a currency amount for 'fixed-amount'. NOT a fraction.
attemptCapNoMaximum challenge attempts per path before that path gives up. Default 25. Journey statistics (expected attempts/cost, P(funded)) are censored at this cap.
challengesYesThe challenges to simulate this trader across (1-12 entries; 2+ for a meaningful comparison). Mix dataset references and inline specs freely.
tradesPerDayYesAverage trades per simulated trading day. More trades per day means more ways to hit the daily loss limit within a single day.
simulateFundedNoWhether to simulate the funded stage (payouts, blowup risk) after passing. Default true - EV is only meaningful with it on; set false to study the evaluation alone.
fundedHorizonDaysNoFunded-stage horizon in trading days for the payout/EV simulation. Default 90 (about 4 calendar months). EV scales with this choice - state it when reporting EV.
tradesPerDayModelNo'fixed' (default): the same count every day. 'poisson': daily count drawn Poisson(tradesPerDay); days can then have zero trades, which do not count as trading days.

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries full behavioral burden, and it does substantial work: it discloses that rows are ordered by EV for the caller's specific inputs, that different trader profiles reorder them, that unsimulated rules make numbers optimistic, that consistency rules and payout gating ARE simulated (engine v1), and that results are distributions under assumptions, not promises. It also explains determinism (byte-identical with same seed) and the expectation to surface assumption flags/disclaimer. Minor shortfalls: it doesn't explicitly state whether the tool performs network access for directory references vs offline inline specs (though the schema does), and it doesn't describe error behavior.

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 long but every section earns its place: purpose, non-ranking caveat, flags caveat, engine version disclosure, units, determinism, and assumptions. It is front-loaded with the core action and the critical "NOT A RANKING" caveat. The UNITS and ASSUMPTIONS sections are wordy and somewhat repetitive (the flagsNotSimulated caveat appears in both the row-list paragraph and the ASSUMPTIONS paragraph), which prevents a 5.

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 16 parameters, no annotations, no output schema, and nested challenge/spec objects, the description is remarkably complete: it explains units, determinism, how to present results, what is simulated vs. not, and where to route for deeper analysis. It does not enumerate return-value shape (no output schema, so a brief mention of the per-row columns exists in the description, which it does provide: perAttemptPassProbability, fundedProbability, expectedAttempts, expectedCost, evTotal, pEvPositive, daysToFundedP50, flagsNotSimulated). It is missing explicit guidance on what errors can occur (e.g., mixing spec and firmId, both/neither error) though the schema covers that, and the description assumes the agent will discover directory references via propfirms_list_simulatable rather than stating it directly.

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 the schema already documents all 16 parameters thoroughly. The description still adds meaningful semantics on top: the UNITS paragraph clarifies the percent-vs-fraction distinction (every *Pct field and percent-mode riskValue in percent units, except winRate as a fraction), the DETERMINISM paragraph instructs the caller to include seed and path count when reporting, and the ASSUMPTIONS paragraph tells the caller to surface flags/disclaimer. That goes beyond the schema, though the heavy lifting is done by the schema's rich per-parameter 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 leads with a specific verb ("Simulate") and resource (the SAME trader across several challenges, up to 12) and immediately clarifies the output (one row per challenge sorted by expected value). It also explicitly differentiates itself from a ranking or endorsement and mentions the sibling propfirms_simulate for full distributions, so an agent can distinguish it from the adjacent simulation tool.

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?

The description states exactly when to use this tool (comparing the same trader across challenges under identical options/seed) and names the alternative ("run propfirms_simulate on the interesting rows" for full per-challenge distributions). It also gives strong negative guidance: "THIS IS NOT A RANKING" and "never 'best firm'", plus explicit handling guidance for unsimulated flags and assumptions.

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

A4.1/5.0
Disambiguation4/5

The domain prefixes (edge_, library_, propfirms_, trackers_) cleanly separate four distinct areas, and within most clusters each tool has a specific job (search vs get vs list vs simulate). The propfirms cluster is the only strain: propfirms_simulate, propfirms_simulate_trades, propfirms_pass_rates, propfirms_compare, and propfirms_validate_strategy all overlap in the broad sense of 'running simulations,' though the descriptions do differentiate them by input type and scope.

Naming Consistency4/5

Each domain follows its own consistent pattern: library_get_*/library_list_*, propfirms_* with an action verb, edge_* and trackers_* as noun-style resources. The convention is recognizable and predictable per domain, with only minor deviations like propfirms_challenge_rules and propfirms_pass_rates being noun-first rather than verb-first.

Tool Count3/5

At 28 tools the server is heavy, but the count is justified by four large, distinct product surfaces (Library, Edge Stats, prop-firm simulation, and market trackers). Each individual cluster is reasonably scoped; the propfirms cluster alone accounts for 12 tools, which pushes the total into the 'too many' range even though the breadth is real.

Completeness5/5

Each domain feels complete: edge stats has catalog, symbol discovery, and report retrieval; the Library has search, browse, get, and source-code access; propfirms has search, get, rule encoding, simulation, trade-series simulation, comparison, optimization, and validation; trackers has dataset discovery, query, latest-publish, and cross-dataset ticker dossiers. There are no obvious dead ends or missing lifecycle steps within the read-only/analysis scope the server targets.

Resources