LeadSpark MCP

research_company

Discover comprehensive company intelligence from a domain or name, including tech stack, social links, hosting, and email provider by aggregating data from multiple public sources.

Instructions

Research a company by domain or name. Returns company info, tech stack, social links, hosting, email provider, and more. Aggregates data from website scraping, DNS, SSL certificates, GitHub, and DuckDuckGo.

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>"
    },
    "name": "research_company"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes	Company domain (e.g. "stripe.com"), URL, or company name

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 30, 2026

TDQS

A4/5.0

Behavior4/5

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

With no annotations, the description carries the behavioral burden. It discloses that the tool aggregates data from website scraping, DNS, SSL certificates, GitHub, and DuckDuckGo, which signals network-bound, multi-source research and helps an agent anticipate latency or variability. It does not mention rate limits or failure modes, but the source list is meaningful context beyond a simple 'research company' claim.

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 with no filler. The first sentence front-loads the action and expected outputs; the second adds the data-source context. Every clause 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?

For a single-parameter tool with no output schema and no annotations, the description covers input forms, return categories, and data sources. It stops short of explaining the result structure and the vague 'and more', but an agent has enough to invoke it correctly and interpret the broad result.

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 schema already specifies that the query can be a domain, URL, or company name. The description only echoes 'by domain or name' and adds no new semantic detail, so it meets the baseline for a fully documented parameter.

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 ('Research') and resource ('a company by domain or name') and lists concrete return categories: company info, tech stack, social links, hosting, and email provider. This clearly distinguishes it from broader or narrower sibling tools like lookup_tech_stack and find_contacts, even without naming them explicitly.

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?

The intended use is implied: an agent should call this when it needs a broad company profile rather than just contacts or tech stack. However, the description never references sibling tools or states when to prefer a more focused alternative, leaving routing partially to inference.

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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