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AI PM Lab

by io.aipmlab
OverviewDiscussionsAdmin

List recorded experiments

list_experiments
Read-onlyIdempotent

The real runs recorded on one of AI PM Lab's 3D pages or lessons, each described by its setup (e.g. which defences or settings were on). Replay one to see what happened.

Input Schema

NameRequiredDescriptionDefault
pageYesA 3D page (injection, rag, evals, agent-loop, next-token, embeddings) or a lesson's slug (e.g. prompt-injection, model-routing, hallucination-and-grounding, human-in-the-loop)

Output Schema

NameRequiredDescriptionDefault
textYesThe answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text
linksYesThe AI PM Lab pages the answer links to, where each idea can be tried hands-on

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • 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"
+}
  2. Changed2 schema fields changed
    • changedInput schema / properties / page / description
      Previous value: -"injection (prompt injection defences), rag (retrieval), evals (graders), agent-loop, next-token, embeddings"New value: +"A 3D page (injection, rag, evals, agent-loop, next-token, embeddings) or a lesson's slug (e.g. prompt-injection, model-routing, hallucination-and-grounding, human-in-the-loop)"
    • changedInput schema / properties / page / enum
      Previous value: -[
-  "injection",
-  "rag",
-  "evals",
-  "agent-loop",
-  "next-token",
-  "embeddings"
-]New value: +[
+  "injection",
+  "rag",
+  "evals",
+  "agent-loop",
+  "next-token",
+  "embeddings",
+  "how-ai-agents-work",
+  "how-llms-work",
+  "retrieval-augmented-generation",
+  "embeddings-and-semantic-search",
+  "prompt-caching-and-cost",
+  "multi-agent-systems",
+  "prompt-injection",
+  "ai-evals",
+  "prompting-vs-rag-vs-fine-tuning",
+  "human-in-the-loop",
+  "hallucination-and-grounding",
+  "production-monitoring",
+  "writing-good-prompts",
+  "reasoning-models",
+  "model-routing",
+  "structured-outputs",
+  "tool-design-and-mcp",
+  "workflows-vs-agents"
+]
  3. First observed

TDQS

C2.8/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world reads, so the safety profile is covered. The description adds useful domain context — what an experiment record contains ('which defences or settings were on') — but does not clarify list size, ordering, or that nothing is mutated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

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

Two short sentences with no padding, and the resource definition comes first. The second sentence, however, is off-purpose for a listing tool and points at the behaviour of a different tool, which costs it.

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

Completeness3/5

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

An output schema exists so return values need no explanation, and the single required parameter is fully covered by the schema. What is missing is the minimal statement that this retrieves a collection for the given page — and the stray replay instruction can mislead an agent about what calling this tool does.

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 enum is fully documented in the schema, so the baseline is 3. The description loosely mirrors the dual page/lesson nature of the single `page` parameter but adds no constraint or format detail beyond the schema.

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

Purpose3/5

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

The description names the resource precisely (recorded runs on a specific 3D page or lesson, each with its setup), so the agent knows what kind of object is involved. However it never states the verb — it is a noun phrase plus 'Replay one', leaving 'list' to be inferred from the tool name, and it spends its last sentence describing an action that belongs to the sibling replay_experiment.

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

Usage Guidelines2/5

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

There is no explicit when-to-use statement and no named alternative, even though replay_experiment is an obvious sibling for the 'see what happened' path. The trailing sentence gestures at a next step but never says which tool performs it or when to pick this one over searching lessons or challenges.

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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