list_challengesThe challenges (games scored from real recorded runs, with leaderboards) and what each asks you to do.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| 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 |
Changes observed during successful MCP inspections.
Output 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"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already state readOnlyHint, idempotentHint, non-destructive, and closed-world behavior, so the safety profile is covered. The description adds domain context about scored games and leaderboards, but does not disclose additional behavioral details such as formatting, pagination, or freshness beyond what annotations and the output schema provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single efficient sentence with no wasted words. The parenthetical adds useful domain flavor without obscuring the main purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema, rich annotations, and no parameters, the description is nearly complete enough. It tells the agent what domain content to expect; the only minor gap is that it does not explicitly say 'returns a list' or mention ordering, which is a small omission for such a simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
This tool has zero parameters, so the baseline score is 4. The description correctly does not invent parameter details, and the empty schema is consistent with a simple list operation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the resource (AI PM Lab challenges) and adds useful detail about what they are: games scored from recorded runs with leaderboards. It does not use a clear verb like 'list', but the name and phrasing make the purpose unambiguous, and no sibling tool covers challenges.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied by the tool name and description: call this when you need the list of challenges. There is no explicit when-to-use guidance, no exclusions, and no routing to or from sibling tools, but for a zero-parameter read-only list the implied usage is adequate.
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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