AI PM Lab

Search AI PM Lab lessons

search_lessons

Read-onlyIdempotent

Find AI PM Lab lessons on a topic (e.g. RAG, evals, agents, prompt injection, cost). Returns the best matches with their key idea and link.

Input Schema

Name	Required	Description	Default
`query`	Yes	A topic or question, e.g. 'how do I stop hallucinations'

Output Schema

Name	Required	Description	Default
`text`	Yes	The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text
`links`	Yes	The AI PM Lab pages the answer links to, where each idea can be tried hands-on

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 12, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "additionalProperties": false,
+  "properties": {
+    "links": {
+      "description": "The AI PM Lab pages the answer links to, where each idea can be tried hands-on",
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "text": {
+      "description": "The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text",
+      "type": "string"
+    }
+  },
+  "required": [
+    "text",
+    "links"
+  ],
+  "type": "object"
+}

First observedOct 10, 2026

TDQS

A3.6/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds that results are relevance-ranked ('best matches') with 'key idea and link', which is useful context, but no pagination or result-count limits are disclosed.

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?

Two tight sentences, front-loaded with the action and resource, then the return shape. No wasted words and the topic examples are compact and useful.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so the description need not detail return values, and annotations cover the safety profile. It is nearly complete for a simple one-param read tool; the only gap is not distinguishing itself from the get_lesson sibling.

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% and the single 'query' param is fully documented in the schema with an example. The description's topic examples add marginal framing but no syntax or format detail beyond what the schema already provides, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clear specific verb ('Find') plus resource ('AI PM Lab lessons') with concrete topic examples (RAG, evals, agents). An agent can tell this is a topic-based search tool. However it does not differentiate itself from the close sibling get_lesson, which likely fetches a specific lesson.

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

Usage Guidelines3/5

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

Usage is only implied by 'on a topic'; there is no explicit when-to-use vs when-not, and crucially no routing to the sibling get_lesson for retrieving a single known lesson. The 'best matches' phrasing hints at ranked exploration but doesn't state the condition clearly.

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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