pearl-api-mcp-server

verifyAnswer

verifyAnswer

Use this when a professional needs to validate the correctness, safety, or trustworthiness of a specific AI-generated answer, or when the user asks to have an answer double-checked by a real expert.

Input Schema

TableJSON Schema

Name	Required	Description
`answer`	Yes	The AI-generated answer that requires human verification
`sessionId`	No	Existing session ID to continue; generated if omitted
`chatHistory`	No	Optional prior messages for context (ordered, oldest first)
`enableMockResponse`	No	When true, the API returns a hardcoded mock expert success response instead of routing to a real expert. Useful for testing downstream behavior.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedApr 23, 2026

addedInput schema / properties / enableMockResponse

Added value: +{
+  "description": "When true, the API returns a hardcoded mock expert success response instead of routing to a real expert. Useful for testing downstream behavior.",
+  "type": "boolean"
+}

Changed2 schema fields changedApr 9, 2026
- removedInput schema / additionalProperties
```
Removed value: -false
```
- removedInput schema / properties / chatHistory / items / additionalProperties
```
Removed value: -false
```
Changed2 schema fields changedApr 7, 2026
- changedInput schema / properties / chatHistory / items / properties / role / description
```
Previous value: -"Message role (system|user|assistant|tool)"New value: +"Message role"
```
- addedInput schema / properties / chatHistory / items / properties / role / enum
```
Added value: +[
+  "user",
+  "assistant"
+]
```
First observedJan 31, 2026

TDQS

A3.8/5.0

Behavior3/5

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

Without annotations, the description carries the burden of disclosing behavior. It reveals the key trait that verification is performed by a 'real expert,' which is a meaningful behavioral disclosure. However, it does not mention potential side effects, asynchronicity, cost, or what the return value looks like, leaving significant behavioral ambiguity.

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 description is a single sentence that is front-loaded with the usage trigger ('Use this when...'). It is compact and free of fluff, though slightly verbose with the triad 'correctness, safety, or trustworthiness' which could be condensed without losing meaning.

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?

The description adequately explains what the tool does and when to use it, but given there is no output schema and no annotations, it should disclose the return format or acknowledge that it routes to a human expert asynchronously. This missing information leaves the agent uncertain about what to expect from the tool's response.

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 schema already fully documents all parameters. The description adds minimal semantic value beyond the schema—just framing the 'answer' as AI-generated. This meets the baseline but does not exceed it.

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 clearly states a specific verb ('validate') and resource ('AI-generated answer'), and distinguishes itself from sibling askExpert tools by focusing on verification of an existing answer rather than asking a new question. It also names the exact triggering condition ('when the user asks to have an answer double-checked').

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

Usage Guidelines4/5

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

The description gives explicit 'when to use' context: validating correctness, safety, or trustworthiness, or when the user requests expert double-checking. It does not explicitly list when not to use or name alternative tools, but the purpose is distinct enough that the usage context is clear.

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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