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luxalgo-mcp-server

by io.github.LuxAlgo

List prop firms and challenges

propfirms_list_simulatable

List the prop firms in the live LuxAlgo directory together with every simulatable challenge (challengeId, display name, account size, currency, price, and its rule-semantics provenance). Call this first to discover the firmId + challengeId pairs accepted by propfirms_challenge_rules, propfirms_simulate, propfirms_optimal_risk, propfirms_compare and propfirms_simulate_trades. Challenges whose loss-rule semantics cannot be established are listed under notSimulatable instead of being guessed. The listing is data, not endorsement: firms are alphabetical - no recommendation or ranking is implied, and none should be presented. 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 lists only the firms and challenges whose rules the engine can encode honestly. The full directory — every visible firm with platforms, prices, payout terms, and live offers/promo codes — is served by propfirms_search, propfirms_search_challenges, and propfirms_search_offers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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."
productTypeNoOptional filter to one instrument class. Omit to list every firm.

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and does so thoroughly. It discloses the live data source, keyless API access, origin override via environment variable, inference provenance, inferredFields disclosure, the notSimulatable refusal policy, and the caveat that firms change rules and their own pages are authoritative.

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 well-structured and front-loaded with the primary purpose and call-this-first guidance. However, the warning that challenges with unestablishable loss rules are listed as notSimulatable rather than guessed appears twice, making the text slightly longer than necessary.

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?

Even though there is no output schema, the description fully explains what the tool returns: simulatable challenges, firm/challenge IDs, account details, price, provenance, and inferredFields. It also addresses ordering, endorsement, data source, and limitations, so an agent has enough context to call and interpret the result 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 description coverage is 100%, so the parameters are already fully documented in the schema. The description does not add substantial parameter-level meaning beyond what the schema provides, so the baseline of 3 is appropriate.

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 clearly states a specific action ('List the prop firms in the live LuxAlgo directory together with every simulatable challenge') and enumerates the exact fields returned. It also distinguishes itself from sibling tools by explaining that the full directory is served by propfirms_search, propfirms_search_challenges, and propfirms_search_offers.

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 explicitly tells the agent to call this first to discover accepted firmId + challengeId pairs for multiple sibling tools. It also states what this tool does NOT cover and routes the full-directory use case to propfirms_search, propfirms_search_challenges, and propfirms_search_offers.

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.

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