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@asyntai/mcp

@asyntai/mcp

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by asyntai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
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Ask the chatbot a question

ask_ai

Test a website's chatbot by asking a question and retrieving the visitor-facing answer, using one message from your monthly allowance.

Instructions

Ask one website's chatbot a question and get the answer a real visitor would receive. Use this to test the bot. It spends one message from the account's monthly allowance, and it calls an external model provider.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "message": "<message>"
    },
    "name": "ask_ai"
  }
}

Input Schema

NameRequiredDescriptionDefault
messageYesThe question to ask, as a visitor would type it.
session_idNoReuse the same value across calls to continue one test conversation. Omit to start a fresh one.
website_idNoNumeric id of the website, from list_websites. Omit to use the account's primary website.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.2

TDQS

A4.4/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint false, openWorldHint true), the description discloses two critical side effects: it spends one message from the monthly allowance and calls an external model provider. This informs the agent of cost and external dependencies, which is valuable behavioral context. No contradiction with annotations.

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 with no filler. The primary action and purpose are front-loaded, and the cost/external-call disclosure is placed as a natural follow-up. Every word earns its place.

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 the tool's simplicity, the schema covers parameters, annotations cover safety, and the description covers side effects and intent. It does not detail the return format or error handling, but for a simple ask-and-answer tool with no output schema, the provided information is sufficient for 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 description coverage is 100%, with each parameter already well-documented (e.g., session_id for continuing conversations, website_id defaults to primary). The description adds no additional parameter-level insight, so it stays at the baseline of 3, relying on the schema to carry the semantic load.

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 states a clear verb+resource: 'Ask one website's chatbot a question and get the answer a real visitor would receive.' It adds a specific purpose ('test the bot') that differentiates it from conversation-management tools like send_agent_reply or list_conversations, making its role unambiguous.

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?

It explicitly says 'Use this to test the bot', giving a clear when-to-use context. It does not mention when not to use it or name alternative tools, but the test-oriented purpose is distinct enough to guide an agent without further exclusions.

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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