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meta get study results

meta_get_study_results
Read-onlyIdempotent

Retrieve detailed results for a conversion lift or A/B test study by study ID, including objectives, cells, and lift results.

Instructions

Get detailed results for a conversion lift or A/B test study. Returns objectives, cells, and lift results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
studyIdYesThe Ad Study ID

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYesOriginal tool result: parsed JSON when the text is JSON, otherwise the text or multiple MCP content blocks. Provider fields depend on the selected query.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2.0.0
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": false,
+  "properties": {
+    "result": {
+      "anyOf": [
+        {
+          "additionalProperties": true,
+          "properties": {},
+          "type": "object"
+        },
+        {
+          "items": {},
+          "type": "array"
+        },
+        {
+          "type": "string"
+        },
+        {
+          "type": "number"
+        },
+        {
+          "type": "boolean"
+        },
+        {
+          "type": "null"
+        }
+      ],
+      "description": "Original tool result: parsed JSON when the text is JSON, otherwise the text or multiple MCP content blocks. Provider fields depend on the selected query."
+    }
+  },
+  "required": [
+    "result"
+  ],
+  "type": "object"
+}
  2. First observedv1.0.0

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already cover readOnly, idempotent, and non-destructive behavior, so the description only needs to add context. It adds that results are for conversion lift or A/B tests and enumerates the returned components (objectives, cells, lift results), but it does not mention anything about study status requirements, empty results, or pagination.

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 two short sentences with no filler. The core action and resource are front-loaded, and the second sentence adds the key return categories without unnecessary detail.

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?

For a simple tool with one documented parameter, strong read-only annotations, and an output schema, the description is nearly complete. A small gap is the lack of explicit differentiation from the closely related interpret-experiment-results sibling, but the intended call context is still clear.

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%, so the single required studyId parameter is already documented adequately. The description reinforces the parameter's meaning by referring to 'a conversion lift or A/B test study' but does not need to add more, so the baseline score of 3 is appropriate.

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 opens with a specific verb and resource: 'Get detailed results for a conversion lift or A/B test study,' which is clear and action-oriented. It further distinguishes itself from sibling tools by naming the resource type (study results) and the key return concepts (objectives, cells, lift results), so an agent can tell it apart from tools like meta_get_ad_studies or meta_interpret_experiment_results.

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?

The description implies the tool should be used when an agent needs detailed results for a study, but it does not explicitly say when to prefer this over alternatives such as meta_get_ad_studies or meta_interpret_experiment_results. No exclusions, prerequisites, or when-not-to-use guidance are provided.

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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