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Agent.ai MCP Server

Agent.ai MCP Server

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JavaScript
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prospect_research_search_company_intel

Retrieve comprehensive company intelligence including funding rounds, investor data, contact emails, and industry details. Output integrates directly with Research Prospect for streamlined prospect research.

Instructions

Returns rich company intelligence: funding rounds, investors, contact emails, Crunchbase data, employee count, revenue, industries. Cached 30 days. Output feeds directly into Research Prospect.

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": {
      "query": "<query>",
      "output_variable_name": "<output_variable_name>"
    },
    "name": "prospect_research_search_company_intel"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesCompany name or domain to search (e.g. 'hubspot.com' or 'HubSpot').
output_variable_nameYesVariable name to store the company intelligence.company_intel

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses the 30-day caching behavior, which is useful. However, it does not mention whether the operation is read-only, any rate limits, or output format beyond listing fields. Some behavioral context is provided, but gaps remain.

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-load the key information: what data is returned, caching policy, and integration context. Every sentence adds value with no fluff. Excellent conciseness.

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 with only two simple parameters and no output schema, the description adequately covers purpose, output contents, caching, and downstream usage. It could mention whether the output is structured (e.g., JSON) but overall it's sufficient.

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%, so the schema already documents both parameters. The description adds no new meaning beyond what the schema provides: it repeats 'company name or domain' for query and lacks detail for output_variable_name. Baseline 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 clearly states the tool returns 'rich company intelligence' and lists specific data types (funding, investors, etc.). It uses a specific verb-resource combination ('search company intel') and distinguishes from sibling tools by focusing on a quick, cached snapshot rather than deep research.

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

Usage Guidelines3/5

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

While the description mentions it 'feeds directly into Research Prospect,' it does not explicitly state when to use this tool versus alternatives like company_research_v2_start_research or other company research tools. No usage exclusions or context for 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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