luxalgo-mcp-server

Reference pass rates per archetype

propfirms_pass_rates

Read-only

Reference challenge pass rates computed live from the directory's encoded rules with the same engine, seed (42), path count (10,000) and reference archetypes luxalgo.com/prop-firms uses — per challenge and per archetype (developing 45% win rate / consistent 48% / proven edge 52%, all risking conservatively). Returns per-attempt pass probability with 95% CI, P(funded), expected attempts and total cost, EV, payout probability, funded-blowup probability, each cell's assumption flag ids, and the ruleset's provenance (structured directory columns vs fields inferred from listing text — always relay inferred fields). Deterministic per ruleset and cached — cheap to call. These are REFERENCE odds for orientation and comparison, not the user's personal odds: for their own statistics use propfirms_simulate (summary stats) or propfirms_simulate_trades (their real trade series). Not a ranking; a firm's page is authoritative for current rules (check lastVerified). Expected costs use the directory's listed challenge prices; full firm profiles and live offers are directory data (propfirms_get, propfirms_search_offers).

Input Schema

TableJSON Schema

Name	Required	Description
`firmId`	Yes	Directory firm id (propfirmId, e.g. 'ftmo') or firm name — from propfirms_list_simulatable.
`context`	Yes	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."
`challengeId`	No	One challenge id. Omit to compute every simulatable challenge the firm has.

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 8, 2026

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.\""

Changed3 schema fields changedAug 31, 2026

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"
-]New value: +[
+  "firmId",
+  "context"
+]

AddedAug 27, 2026

TDQS

A4.6/5.0

Behavior5/5

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

The annotations already mark the tool as readOnly and openWorld, and the description adds valuable behavioral context: results are deterministic per ruleset, cached, cheap to call, computed from encoded rules, and not a ranking. It discloses the reference archetypes and risk assumptions, the provenance handling for inferred fields, and the caveat to check lastVerified. This goes well beyond the 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 dense but front-loaded with the key purpose and differentiation. It includes a long enumeration of return values und caveats, but each detail supports correct interpretation and prevents misuse. Slightly long, but justified given the complexity and absence of an output schema.

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 thoroughly: probabilities, confidence intervals, expected attempts, cost, EV, payout, blowup probability, assumption flags, and provenance. It also explains relevant caveats about reference odds, authoritative firm pages, and where to go for personal simulations and live offers. Nothing critical is missing for an agent to decide whether and how to call this tool.

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 input parameters are already well documented. The description adds meaning to the overall computation, such as archetype win rates and output dimensions, but it does not add much parameter-specific guidance beyond what the schema provides. The baseline of 3 is appropriate because the schema carries the parameter documentation burden.

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?

States a specific purpose: computing reference challenge pass rates per archetype using the same engine and reference assumptions. Clearly distinguishes itself from propfirms_simulate and propfirms_simulate_trades by emphasizing 'reference odds' rather than personal statistics. The title and description align and identify the exact deliverable.

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?

Explicitly tells when to use this tool vs alternatives: use it for orientation and comparison, but use propfirms_simulate or propfirms_simulate_trades for the user's own statistics. It also directs the agent to propfirms_get and propfirms_search_offers for full profiles and live offers, and warns that a firm's page is authoritative. This is exemplary routing guidance.

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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Reference pass rates per archetype

Input Schema

Schema Changelog

TDQS

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