pearl-api-mcp-server

askPearlExpert

askPearlExpert

Use this when the problem is complex, ambiguous, high-stakes, or multidisciplinary and would benefit from AI intake followed by escalation to a human expert. Do not use for simple fact queries (use askPearlAi) or when the user explicitly requests a human directly (use askExpert). Supports phone callback — pass phoneNumber and contactPreference='phone' if the user wants a call.

Input Schema

TableJSON Schema

Name	Required	Description
`question`	Yes	The user's question
`sessionId`	No	Optional session ID for continuing a conversation
`chatHistory`	No	Optional conversation history. This ensures experts see the complete context
`phoneNumber`	No	Customer's phone number for expert callback in E.164 format (e.g., +15551234567). Only pass when the user explicitly provides it.
`contactPreference`	No	Customer's preferred contact method. Set to 'phone' when the user wants a phone callback.
`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.

TDQS

A4.2/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the AI-intake-to-human-escalation flow and phone callback support, but lacks details on side effects, async behavior, response format, or potential delays. This is a reasonable disclosure but not comprehensive.

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 sentences, front-loaded with usage guidance, and every sentence adds value. It is concise and well structured 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 tool that routes to human experts, the description provides strong decision-support context (when/why to use, alternatives) and phone callback instructions. However, it does not explain what happens after invocation (e.g., async response, expected return), which is a minor gap given no output schema.

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 coverage is 100% with all parameters described. The description adds no additional parameter semantics beyond what the schema already provides, so baseline 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 clearly states the tool is for complex, ambiguous, high-stakes, or multidisciplinary problems that benefit from AI intake followed by human expert escalation. It distinguishes itself from sibling tools askPearlAi and askExpert, making its purpose unambiguous.

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

Usage Guidelines5/5

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

Explicitly states when to use (complex/ambiguous/high-stakes/multidisciplinary), when not to use (simple fact queries, direct human request), and names the exact alternative tools. Also provides guidance for phone callback usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All pearl-api-mcp-server tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4/5.0

Disambiguation4/5

The tools are distinguished by request type: AI-only, expert-only, hybrid, and verification. Descriptions explicitly state when to use each, but askExpert and askPearlExpert both lead to human experts and askPearlAi vs askPearlExpert could be confused without careful reading.

Naming Consistency3/5

Three tools share the 'ask' prefix, but verifyAnswer breaks the pattern. Within the 'ask' group, 'askExpert' lacks the 'Pearl' brand used in 'askPearlAi' and 'askPearlExpert', making naming mixed and not fully systematic.

Tool Count5/5

Four tools is well-scoped for a Q&A/advice server, covering distinct interaction modes without being overly minimal or excessive. Each tool has a clear role.

Completeness4/5

The set covers the main ways to obtain an answer (AI, human, hybrid) plus a verification step. A minor gap is lack of a tool for managing or tracking ongoing requests, but the surface feels complete for the apparent synchronous use case.

Resources

Need Help?