pearl-api-mcp-server

Server Details

Hybrid human + AI expertise for faster, trusted answers and decisions via MCP Server.

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Status: Healthy
Uptime: 99.9% over 38 days
Last Tested: 2026-09-24 11:40
Transport: Streamable HTTP · MCP 2025-11-25
URL

TDQS

A4.1/5.0

Scored 2026-09-18 11:55 across 5 tools

Disambiguation4/5

The tools are mostly distinct: askPearlAi is for low-risk AI answers, askExpert for direct human requests, and askPearlExpert for escalated hybrid cases. Some boundary overlap exists between askExpert and askPearlExpert, but the descriptions provide clear enough routing guidance.

Naming Consistency4/5

The naming is generally consistent with verb-first camelCase conventions: askPearlAi, askPearlExpert, askExpert, getConversationHistory, verifyAnswer. The mix of 'ask' prefix and other verbs is slightly inconsistent, but the pattern is still readable and predictable.

Tool Count5/5

Five tools is well-scoped for a Q&A/expert-routing server. Each tool covers a distinct mode of interaction—AI, human, hybrid, verification, and history—without unnecessary bloat or gaps.

Completeness4/5

The tool surface covers the core lifecycle of an advisory interaction: getting AI answers, requesting human experts, escalating complex cases, verifying answers, and retrieving history. Minor features like cancellation or status tracking are missing but are not obvious requirements from the stated purpose.

Available Tools

5 tools

askExpertaskExpertAInspect

Use this when the user explicitly asks to speak with a real human expert, needs personalized advice in a complex or sensitive domain, or says something like 'Can I talk to a real expert?'. Supports phone callback — pass phoneNumber and contactPreference='phone' if the user wants a call.

ParametersJSON 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

A3.9/5.0

Behavior3/5

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

The description reveals that the tool routes to a human expert and supports phone callback via specific parameters, which is useful behavior. With no annotations, the description carries the burden, but it does not explain response behavior, latency, or potential limitations, leaving some uncertainty about what happens after invocation.

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 triggers and the phone callback option. Every word contributes value, with no redundancy or unnecessary details.

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?

For a tool with six parameters and no output schema, the description gives core usage context but omits what the user can expect after submission (e.g., chat reply vs. separate callback). It also does not differentiate from the sibling 'askPearlExpert', which could be a similar function, making the description only partially complete.

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?

The schema provides comprehensive descriptions for all 6 parameters (100% coverage), so the description adds little beyond restating the phone callback combination. It does not clarify enableMockResponse or sessionId beyond schema, so it stays at the baseline for full schema coverage.

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's purpose: to connect the user with a real human expert. It provides specific triggers like 'explicitly asks to speak with a real human expert' and 'needs personalized advice in a complex or sensitive domain', which distinguishes it from siblings like askPearlAi that likely provide AI-based answers.

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 explicitly states when to use the tool: when the user asks for a real expert, needs complex/sensitive advice, or uses phrases like 'Can I talk to a real expert?'. However, it does not name alternative tools or provide when-not-to-use scenarios, so it falls short of full comparative guidance.

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

askPearlAiaskPearlAiAInspect

Use this when the user wants a rapid AI-generated answer, draft, or alternative perspective on a low-risk or exploratory topic that does not require human validation. Do not use for medical, legal, financial, or safety-critical questions — use askExpert or askPearlExpert instead.

ParametersJSON 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

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that the output is AI-generated, rapid, and not human-validated, which is critical behavioral context. It does not mention return format or data handling, but for a low-stakes AI answer tool, the disclosed traits are substantive.

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?

Two sentences, front-loaded with the primary use case, and every clause serves a purpose. The distinction between safe and unsafe usage is made in the second sentence without waste.

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

Completeness5/5

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

Despite no output schema and no annotations, the description covers what the tool does, when to use it, when not to use it, and which alternatives to choose. This is sufficient for an agent to select and invoke the tool correctly.

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 baseline is 3. The description does not add parameter-specific meaning, but the schema already documents all three parameters clearly (question, sessionId, chatHistory). No need for description to compensate.

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's purpose: to provide rapid AI-generated answers, drafts, or alternative perspectives on low-risk topics. It distinguishes itself from sibling tools by explicitly naming alternatives (askExpert, askPearlExpert) for higher-risk questions.

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?

The description provides explicit when-to-use conditions (low-risk, exploratory, no human validation) and when-not-to-use conditions (medical, legal, financial, safety-critical), along with named alternative tools. This is exactly what the dimension expects.

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

askPearlExpertaskPearlExpertAInspect

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.

ParametersJSON 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.

getConversationHistorygetConversationHistoryAInspect

Retrieve the full message history for a session. Includes all messages (after any formatting done during processing). Use to resume discussions.

ParametersJSON Schema

Name	Required	Description	Default
`sessionId`	Yes	The session ID of the conversation

TDQS

A4/5.0

Behavior3/5

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

There are no annotations, so the description carries the burden of disclosing behavior. It adds one useful trait: returned messages are "after any formatting done during processing," signaling that the history may not be raw. However, it does not mention ordering, pagination, read-only semantics, or permissions, leaving some behavioral gaps.

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 compact, with the main action stated first. Each sentence earns its place: the first defines the operation, the second clarifies message formatting, and the third gives the practical use case. There is no redundant filler.

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 one-parameter tool with no output schema, the description is mostly sufficient: it states what is returned, notes post-processing formatting, and explains why the tool would be used. It could be slightly stronger by describing the return structure or ordering, but nothing critical blocks correct invocation.

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%, and the only parameter, sessionId, is already clearly documented in the schema. The description does not add meaning beyond the schema, 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 uses a specific verb and resource: "Retrieve the full message history for a session." This clearly distinguishes it from the sibling tools, which are about asking experts or verifying answers rather than retrieving past conversation content.

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?

"Use to resume discussions" gives a clear, practical scenario for when this tool is appropriate. It does not explicitly name alternatives or exclusion criteria, but the intended usage is understandable without further elaboration.

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

verifyAnswerverifyAnswerAInspect

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.

ParametersJSON 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.

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.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

1 tool updateSep 18, 2026
- AddedgetConversationHistory

3 tool updatesApr 23, 2026

ChangedaskExpert1 field changed

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"
+}

ChangedaskPearlExpert1 field changed

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"
+}

ChangedverifyAnswer1 field changed

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"
+}

4 tool updatesApr 9, 2026
- ChangedaskExpert2 fields changed
  - removedInput schema / additionalProperties
```
Removed value: -false
```
  - removedInput schema / properties / chatHistory / items / additionalProperties
```
Removed value: -false
```
- ChangedaskPearlAi2 fields changed
  - removedInput schema / additionalProperties
```
Removed value: -false
```
  - removedInput schema / properties / chatHistory / items / additionalProperties
```
Removed value: -false
```
- ChangedaskPearlExpert2 fields changed
  - removedInput schema / additionalProperties
```
Removed value: -false
```
  - removedInput schema / properties / chatHistory / items / additionalProperties
```
Removed value: -false
```
- ChangedverifyAnswer2 fields changed
  - removedInput schema / additionalProperties
```
Removed value: -false
```
  - removedInput schema / properties / chatHistory / items / additionalProperties
```
Removed value: -false
```
2 tool updatesApr 8, 2026
- Removedget_verification_state
- Removedsend_answer_for_verification
2 tool updatesApr 8, 2026
- Addedget_verification_state
- Addedsend_answer_for_verification
4 tool updatesApr 7, 2026
- ChangedaskExpert1 field changed
  - addedInput schema / properties / chatHistory / items / properties / role / enum
```
Added value: +[
+  "user",
+  "assistant"
+]
```
- ChangedaskPearlAi1 field changed
  - addedInput schema / properties / chatHistory / items / properties / role / enum
```
Added value: +[
+  "user",
+  "assistant"
+]
```
- ChangedaskPearlExpert1 field changed
  - addedInput schema / properties / chatHistory / items / properties / role / enum
```
Added value: +[
+  "user",
+  "assistant"
+]
```
- ChangedverifyAnswer2 fields changed
  - 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"
+]
```

2 tool updatesMar 30, 2026

ChangedaskExpert2 fields changed

addedInput schema / properties / contactPreference

Added value: +{
+  "description": "Customer's preferred contact method. Set to 'phone' when the user wants a phone callback.",
+  "enum": [
+    "chat",
+    "phone"
+  ],
+  "type": "string"
+}

addedInput schema / properties / phoneNumber

Added value: +{
+  "description": "Customer's phone number for expert callback in E.164 format (e.g., +15551234567). Only pass when the user explicitly provides it.",
+  "type": "string"
+}

ChangedaskPearlExpert2 fields changed

addedInput schema / properties / contactPreference

Added value: +{
+  "description": "Customer's preferred contact method. Set to 'phone' when the user wants a phone callback.",
+  "enum": [
+    "chat",
+    "phone"
+  ],
+  "type": "string"
+}

addedInput schema / properties / phoneNumber

Added value: +{
+  "description": "Customer's phone number for expert callback in E.164 format (e.g., +15551234567). Only pass when the user explicitly provides it.",
+  "type": "string"
+}

4 tool updatesJan 31, 2026
- First observedaskExpert
- First observedaskPearlAi
- First observedaskPearlExpert
- First observedverifyAnswer

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