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AI School by Lilly Tech Systems

by com.lillytechsystems
OverviewDiscussionsAdmin

Search lessons

search_lessons
Read-onlyIdempotent

Search the AI School curriculum (500+ tracks, thousands of free lessons on AI engineering, governance, security, and applied AI by profession). Returns matching lesson titles and URLs. Use this before answering an AI-topic question when a concrete, citable lesson would help, then read_lesson to get the text.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "search_lessons"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax results (default 10, max 50)
queryYesWords to search for, e.g. "prompt injection defence"

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds the useful behavioral detail that results are lesson titles and URLs, and scopes the search across the given curriculum topics. It does not cover ranking or pagination, but those are minor for this kind of search tool.

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?

The description is three sentences with no filler: scope/curriculum, return value, and usage guidance with the read_lesson follow-up. It is front-loaded with the core action and remains highly readable.

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 search tool with complete parameter documentation, safety annotations, and a clear follow-up sibling, the description covers purpose, usage context, return shape, and workflow. Nothing essential for an agent to invoke it correctly is missing.

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%: 'query' and 'limit' are both documented in the input schema. The description does not add meaning beyond the schema, so the baseline of 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 verb ('Search'), a specific resource ('AI School curriculum'), and the concrete output ('matching lesson titles and URLs'). It also distinguishes itself from siblings by framing read_lesson as the follow-up step rather than the search itself.

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 explicitly states when to use the tool: 'Use this before answering an AI-topic question when a concrete, citable lesson would help.' It also names the next step (read_lesson), though it does not explicitly describe when to prefer list_tracks or get_track instead.

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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