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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

NameRequiredDescriptionDefault
firmIdYesDirectory firm id (propfirmId, e.g. 'ftmo') or firm name — from propfirms_list_simulatable.
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."
challengeIdNoOne challenge id. Omit to compute every simulatable challenge the firm has.

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"
-]New value: +[
+  "firmId",
+  "context"
+]
  3. Added

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the readOnly/openWorld annotations, it discloses behavioral traits the annotations cannot convey: deterministic per ruleset, cached and cheap to call, fixed seed/path count, and the exact shape of returned odds including provenance flags. It also tells the agent it must relay inferred fields, a real operational constraint.

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?

Purpose and routing are front-loaded, but the middle enumerates many return fields and engine constants, making it a dense wall of text. Most sentences earn their place, though the return-value list could be trimmed since it borders on redundancy.

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?

With no output schema, the description carries the full burden of explaining returns, and it does so (pass probability, 95% CI, P(funded), expected attempts/cost, EV, blowup probability, provenance). Combined with the sibling routing, 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.

Parameters4/5

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

Schema coverage is 100%, so baseline is 3, but the description adds that a challengeId can be omitted to compute every simulatable challenge and clarifies the archetype semantics (developing 45% / consistent 48% / proven edge 52%). That is meaningful supplementary context beyond the raw schema.

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+resource ('Reference challenge pass rates computed live from the directory's encoded rules') and pins down scope per challenge and per archetype, naming the concrete engine parameters (seed 42, 10,000 paths). It clearly distinguishes itself from propfirms_simulate and propfirms_simulate_trades as reference rather than personal odds.

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 explicit when/when-not routing: 'These are REFERENCE odds for orientation and comparison, not the user's personal odds: for their own statistics use propfirms_simulate ... or propfirms_simulate_trades'. It also warns the tool is 'Not a ranking' and that a firm's page is authoritative, adding a provenance caveat to relay inferred fields.

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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