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Agent.ai MCP Server

Agent.ai MCP Server

by OnStartups
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JavaScript
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company_research_v2_ask_domain_question

Ask custom research questions about a company domain to obtain targeted answers, using cached data and optional live web search for current information.

Instructions

Ask a custom research question about a company domain. Answers are cached per user/domain.

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": {
      "domain": "<domain>",
      "question": "<question>",
      "output_variable_name": "<output_variable_name>"
    },
    "name": "company_research_v2_ask_domain_question"
  }
}

Input Schema

NameRequiredDescriptionDefault
domainYesDomain to research (e.g., hubspot.com).
questionYesYour research question (e.g., 'What is their pricing model?').
use_report_contextNoInclude existing company research data as context for better answers.
use_live_searchNoInclude live web search for more current information.
output_variable_nameYesVariable name for the answer result.question_result

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

No annotations are present, so the description must disclose behavior. It mentions caching per user/domain, which is helpful. However, it does not describe authentication needs, rate limits, data staleness, or the effect of parameters like use_live_search on behavior.

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 extremely concise with two sentences. The first sentence states the core purpose, and the second adds a key behavioral detail (caching). No redundant or unnecessary words.

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?

Given the lack of output schema, the description does not explain the format or structure of the answer. It also omits details on caching behavior implications (e.g., how to force refresh). While it covers basic usage, it leaves gaps for a complete understanding.

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 input schema has 100% description coverage, so the description adds no additional parameter meaning. It does not explain the difference between use_report_context and use_live_search, nor the role of output_variable_name beyond the schema. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Ask a custom research question') and the resource ('about a company domain'). It adds a distinguishing feature (caching per user/domain). However, it does not explicitly differentiate from the very similar sibling tool 'company_research_v2_answer_question', which could cause confusion.

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?

The description provides no guidance on when to use this tool versus alternatives, no prerequisites, and no context on its appropriate use cases. It only states what it does, not when or when not to use it.

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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