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Replai

by tw.replai
OverviewDiscussionsAdmin

List test cases

test_cases_list
Read-onlyIdempotent

Reports retained questions, provenance, draft/live answers and advisory evaluation.

Input Schema

NameRequiredDescriptionDefault
agent_idYesAgent ID from agents_list or agent_create.

Output Schema

NameRequiredDescriptionDefault
casesYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / agent_id / description
      Added value: +"Agent ID from agents_list or agent_create."
  2. Changed3 schema fields changed
    • addedOutput schema / $defs / FreshnessReason
      Added value: +{
+  "properties": {
+    "kind": {
+      "enum": [
+        "unrecorded",
+        "settings",
+        "source_added",
+        "source_removed",
+        "source_changed"
+      ],
+      "title": "Kind",
+      "type": "string"
+    },
+    "source_id": {
+      "anyOf": [
+        {
+          "type": "string"
+        },
+        {
+          "type": "null"
+        }
+      ],
+      "default": null,
+      "title": "Source Id"
+    },
+    "source_name": {
+      "anyOf": [
+        {
+          "type": "string"
+        },
+        {
+          "type": "null"
+        }
+      ],
+      "default": null,
+      "title": "Source Name"
+    }
+  },
+  "required": [
+    "kind"
+  ],
+  "title": "FreshnessReason",
+  "type": "object"
+}
    • addedOutput schema / $defs / PublicTestCase / properties / answer_freshness
      Added value: +{
+  "anyOf": [
+    {
+      "enum": [
+        "current",
+        "changed",
+        "unprovable"
+      ],
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Answer Freshness"
+}
    • addedOutput schema / $defs / PublicTestCase / properties / freshness_reasons
      Added value: +{
+  "items": {
+    "$ref": "#/$defs/FreshnessReason"
+  },
+  "title": "Freshness Reasons",
+  "type": "array"
+}
  3. First observed

TDQS

B3/5.0
Behavior3/5

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

Annotations already provide high-value behavioral context (readOnlyHint: true, destructiveHint: false, idempotentHint: true). The description merely repeats what the output data structure provides, offering no unique behavioral insight.

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

Conciseness4/5

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

The definition is brief and directly describes the payload content. It is efficient, though the sentence structure prioritizes listing data points over explaining function.

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?

Given that output schemas exist and annotations confirm the read-only nature, detailing the specific data points returned is a solid way to inform the AI agent about the tool's payload without being overly verbose.

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?

With 100% schema description coverage, the input parameter 'agent_id' is fully described in the schema. The description adds no meaningful semantic value to this parameter.

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 is a list of attributes the resulting data contains ('retained questions', 'provenance', etc.) rather than clearly stating the tool's operational purpose (e.g., 'Fetches a detailed list of test case execution summaries'). It relies heavily on the tool name for clarity.

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 guidance on when to use this tool versus sibling tools like 'test_case_read' or 'test_case_run'. No context is provided on whether this list is high-level, detailed, or used for specific review steps.

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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