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List interactive simulations

get_simulations
Read-onlyIdempotent

List the browser-based simulations shipped with the MasterAI Now curriculum (line fitter, perceptron gate, gradient flow, MinHash/LSH, BPE tokenizer, 3D vector space, QKV heat-map, pre-training sandbox, LoRA lab, preference tuner, KV-cache/VRAM sandbox). Each entry gives its key, purpose, concepts, difficulty, prerequisite stage and canonical URL. Use it to recommend hands-on practice; these playgrounds run only in a browser and cannot be executed by this server.

Input Schema

NameRequiredDescriptionDefault
topicNoOptional keyword filter, e.g. "attention", "tokenizer", "embeddings", "vram". Omit to list all simulations.
languageNoLocale code. Supported: en, zh, hi, es, fr, ar, tr, de. Defaults to en; any other value falls back to English and is reported as a fallback.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

The annotations already declare readOnlyHint, idempotentHint, and openWorldHint=false. The description adds genuinely new behavioral information beyond those hints: the simulations are browser-only and cannot be executed server-side, and each entry contains a canonical URL. It does not describe result ordering or the fallback reporting behavior, but with rich annotations the remaining gap is small.

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 purpose and usage come first, followed by a useful enumeration of simulations that helps an agent map user requests to valid topic keywords. The parenthetical list of eleven names is long but carries real matching value, so it earns its space; only minor trimming 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?

For a simple two-parameter, read-only listing tool with no output schema, the description covers purpose, returned fields, the browser-only constraint, and a use case. An agent has everything needed to decide when to call it and what it will get back.

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%: both the topic keyword filter and the language locale, including its fallback behavior, are documented in the schema with examples. The description adds no parameter detail beyond that, so the baseline 3 applies.

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 names a specific resource (the browser-based simulations shipped with the curriculum) and enumerates all eleven of them, plus states the fields each entry returns (key, purpose, concepts, difficulty, prerequisite stage, URL). It is clearly distinguishable from get_simulation_details, which must return a single simulation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives an explicit use case ("recommend hands-on practice") and a hard constraint (playgrounds run only in a browser and cannot be executed by this server), which prevents an agent from trying to run them. It stops short of naming siblings such as get_simulation_details or search_content as alternatives for narrower lookups.

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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