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luxalgo-mcp-server

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Get a challenge's full ruleset

propfirms_challenge_rules
Read-only

Fetch one directory challenge's complete ruleset (ChallengeSpec), adapted from the live LuxAlgo directory: evaluation steps (profit targets in percent units of the initial account, minimum trading days, time limits); the daily-loss rule with its exact semantics (basis = measured from prior-day balance vs prior-day equity; limitBasis = whether a pct limit is a fixed allowance of the initial balance or recomputed daily from the anchor; evaluation = breached on an intraday touch vs only at the close; includesOpenPnl = whether floating P&L can breach it); the max-loss rule and its drawdown mode (How the max-loss floor behaves - the single most consequential rule difference between firms. 'static-initial': floor fixed at initial balance minus the limit; never moves (classic CFD two-step). 'trailing-realized-eod': floor ratchets up with end-of-day balance highs; intraday highs do not move it. 'trailing-intraday-unrealized': floor trails the peak unrealized equity intraday and never stops trailing (futures-style; the most-miscalculated rule in the industry: it cuts pass probability dramatically). 'trailing-locks-at-initial': trails intraday peak equity until the floor reaches the initial balance, then freezes (common futures variant). Locking is also composable: locksAtInitial adds the same lock to an EOD trail, and lockOffsetAmount shifts the lock level to initial balance + that amount (e.g. 100 models 'stops trailing $100 above the start').); per-step consistency rules (steps[].consistency.maxBestDayProfitPct - SIMULATED: one outsized day effectively raises the target until the best-day share complies); fees (price, one-time vs monthly billing, reset fee, activation fee, refundable-on-pass); funded terms (profit split percent, payout frequency, first-payout minimum days, and funded.payoutRules - SIMULATED payout gating: minWinningDays, winningDayMinProfit, per-payout caps maxPayoutPctOfProfit/maxPayoutAmount, bufferAmount, and a windowed consistencyMaxBestDayPct gate); flagsNotSimulated (rules the entry declares but the engine does not simulate - material caveats to relay to the user); and sources (the firm-page citation when the directory serves one). The result also carries provenance and inferredFields - every rule read from free text instead of a structured column is named there; relay them and treat the firm's page as authoritative. The returned challenge object is exactly the shape the simulation tools accept as inline spec: copy it, change a rule, and re-simulate to quantify how a rule variation moves pass probability and EV. 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]. DATA SOURCE & PROVENANCE: firm data comes live from LuxAlgo's public, keyless prop-firm directory API - the data behind luxalgo.com/prop-firms (origin overridable via the LUXALGO_APP_ORIGIN env var). Rule semantics are used verbatim where the directory serves structured rule columns; where it serves only free text, semantics are inferred ONLY when one reasonable reading exists, and every inferred field is disclosed in inferredFields (provenance 'directory+inferred') - relay those to the user next to any numbers. Challenges whose loss rules cannot be established are refused as not simulatable rather than guessed. Firms change rules; each firm's own page is always authoritative. NOTE: this returns the simulatable encoding of one challenge's rules; the directory listing with every captured field, plus live offers, is propfirms_get and propfirms_search_challenges.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
firmIdYesDirectory firm id or firm name from propfirms_list_simulatable, e.g. 'ftmo'.
contextYesExplain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
challengeIdYesDirectory challenge id from propfirms_list_simulatable.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • changedInput schema / properties / context / description
      Previous value: -"Explain 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.\""New value: +"Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
  2. Changed3 schema fields changed
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / context
      Added value: +{
+  "description": "Explain 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.\"",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "firmId",
-  "challengeId"
-]New value: +[
+  "firmId",
+  "challengeId",
+  "context"
+]
  3. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint and openWorldHint, and the description adds substantial non-redundant context: live data source (LuxAlgo public keyless API with env-var origin override), inference behavior with `inferredFields` disclosure, refusal of challenges whose loss rules cannot be established, the simulated-vs-declared distinction (flagsNotSimulated), and the caveat that each firm's page is authoritative. It even discloses a non-obvious behavioral trait: 'one outsized day effectively raises the target until the best-day share complies'. No contradiction with 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 very long (roughly 450 words) but every block earns its place: rule semantics, drawdown modes, units, provenance, and sibling routing are all consequential for correct use. The main purpose is front-loaded in the first sentence. It is dense rather than padded, though it approaches the upper bound of reasonable length; slightly tighter phrasing on the drawdown-mode enumeration would improve it.

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?

There is no output schema, so the description carries the full burden of explaining return values, and it does so exhaustively: it enumerates every rule category in the ChallengeSpec, explains unit conventions (percent vs fraction, with winRate called out as the exception), describes provenance/inferredFields, covers refusal behavior, and clarifies how results feed downstream simulation tools. Given the tool's genuine complexity and zero output-schema support, an agent has everything needed to call and interpret it correctly.

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

Parameters3/5

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

Schema coverage is 100%, so the baseline is 3. The description adds no parameter-specific semantics beyond the schema; firmId and challengeId are already documented in the schema as originating from propfirms_list_simulatable. The extensive units guidance applies to return values rather than parameters, so the description does not need to compensate for any schema gap.

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+resource: 'Fetch one directory challenge's complete ruleset (ChallengeSpec)'. It clearly scopes the tool to a single challenge's simulatable encoding and explicitly differentiates from siblings in the final sentence: 'the directory listing with every captured field, plus live offers, is propfirms_get and propfirms_search_challenges'. An agent can distinguish this from the sibling prop-firm tools without opening any schema.

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 gives an explicit use case: the returned `challenge` object is exactly the shape simulation tools accept as inline `spec`, so an agent can copy, modify, and re-simulate. It names alternatives and their scope (propfirms_get and propfirms_search_challenges for listings/offers), states prerequisites (ids come from propfirms_list_simulatable), and even specifies the refusal behavior for unsimulatable challenges. When-to-use and when-not-to-use are both explicit.

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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