Mastery Pathing

Mastery Pathing — Get Engine Script (large platforms)

mastery_pathing_get_engine

Read-only

For answer logs over 5,000 rows or curricula over 2,000 topics, or any such file already in your code sandbox. Returns a complete numpy-only Python script with the same engine as mastery_pathing; it reads topics, prerequisite links, answers and stored topic settings, prints the decision cards and each learner's next topic, and writes everything to mastery_plan.json. No learner data reaches this server on this path.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "mastery_pathing_get_engine"
  }
}

Input Schema

Name	Required	Description
`params`	No	Other inputs exactly as mastery_pathing takes them (goal, learners ...).
`topics_path`	No	Topics file in your sandbox (CSV/TSV/JSON).
`responses_path`	No	Answers file (learner_id, topic, correct, time).
`prerequisites_path`	No	Optional links file (from, to).
`topic_parameters_path`	No	Optional stored topic settings file.

Output Schema

Name	Required	Description	Default
`notes`	No
`paths`	No
`script`	Yes
`language`	No
`requires`	No
`writes_file`	No
`instructions`	Yes
`engine_version`	Yes
`scipy_required`	No
`data_requirements`	No
`network_access_required`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 23, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already declare read-only, non-destructive, open-world behavior, and the description adds substantial context beyond them: it returns code rather than computed results, reads local files, prints decision cards, writes mastery_plan.json, and — critically — states that no learner data reaches the server. That privacy/execution model is exactly what an agent needs to know and is not in 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three dense sentences with no waste: thresholds and when-to-use come first, then what is returned, then the output artifact and privacy note. Every sentence carries information an agent needs.

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?

Despite the output schema, the description explains the nature of the returned script, what it reads, what it prints, and what it writes, plus the no-data-leaves-server guarantee. For a code-generating tool with 5 optional parameters, nothing material is missing.

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 the schema already documents each path parameter, giving a baseline of 3. The description adds meaning by explaining that the freeform 'params' object mirrors mastery_pathing's inputs and by mapping the paths to topics/prerequisites/answers/stored settings, which helps with the nested freeform object.

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 verb and resource — returns a complete numpy-only Python script running the same engine as mastery_pathing — and immediately distinguishes it from the sibling by size threshold and sandbox-file use. An agent can tell which of the two mastery_pathing tools to call without opening the schema.

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?

Gives concrete selection criteria: answer logs over 5,000 rows, curricula over 2,000 topics, or files already in the sandbox. It references the sibling engine implicitly but never states the converse ('use mastery_pathing otherwise'), so the routing is clear but not fully explicit.

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

All Mastery Pathing tools Inspect

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