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Stochastly

Stochastly MCP

by Stochastly

reality_check_profiles

Read-onlyIdempotent

Fetch synthetic profitable, marginal, and overfit profiles plus prop-firm rule presets for Stochastly's reality check screen. For real results, use red_flag_check or adversarial_check.

Instructions

Requires the Stochastly desktop app: this tool runs inside the app, which is not installed on this machine. Install it from https://stochastly.com/pricing, open it and activate a licence.

Return the three synthetic example profiles of the reality check screen (profitable, marginal, overfit) and the prop-firm rule presets it can apply (FTMO Standard and Pro 100k phase 1, The5ers Hyper Growth).

Reference data only: to judge a real result use red_flag_check or adversarial_check. Needs Stochastly open with an Atelier or Portfolio licence.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds meaningful context the annotations cannot: the tool executes inside a desktop app that may not be installed, and a specific licence tier (Atelier or Portfolio) is required. It does not, however, describe failure modes when the licence is missing or how the data is scoped.

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 blocking prerequisite (app not installed on this machine, install URL, licence activation) is front-loaded, followed by the return contents and the routing note. Everything earns its place, though the licence/installation detail is somewhat dense in a single opening sentence.

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 input parameters, no output schema and a closed-world read-only annotation set, the description supplies exactly the missing pieces: what it returns (three profiles and named presets), that it is reference data only, the alternative tools, and the environment/licence prerequisite. Nothing an agent needs to invoke or decline the tool is absent.

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?

The tool takes zero parameters, so there are no arguments whose semantics need explaining; the baseline for a no-param tool is 4. The description correctly adds no parameter discussion, and the returned profiles/presets are described at the value level rather than as inputs.

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 concrete verb (return) and enumerates the exact payload: the three synthetic example profiles (profitable, marginal, overfit) plus the prop-firm rule presets (FTMO Standard/Pro 100k phase 1, The5ers Hyper Growth). It also differentiates itself from siblings by naming red_flag_check and adversarial_check as the tools for real judgments.

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 routing advice — 'Reference data only: to judge a real result use red_flag_check or adversarial_check' — and states prerequisites (Stochastly desktop app open with an Atelier or Portfolio licence). Both when-to-use and when-not-to-use are covered with the alternatives named.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.