Linkup Company Research MCP

research_company

Get a comprehensive company profile by parallel retrieval of overview, funding, and competitor data in a single query.

Instructions

Comprehensive company research using parallel tool calls.

Use this tool for vague queries like "research [company]" or "tell me about [company]". Calls 3 specialized tools in parallel (overview, funding, competitors) and combines the results into a complete company profile.

This is faster than Claude calling 5-6 tools sequentially for general research.

Args: company_name: The name of the company to research max_results: Maximum number of sources per tool (1-50)

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": {
      "company_name": "<company_name>"
    },
    "name": "research_company"
  }
}

Input Schema

Name	Required	Description	Default
`max_results`	No
`company_name`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.4.0Jul 6, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Discloses parallel tool calls (3 in parallel) and combination of results. No annotations provided, but description covers key behaviors. Could mention any rate limits or permissions.

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?

Well-structured: first sentence states purpose, then usage guidance, internal method, and argument descriptions. No wasted words, appropriate length.

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?

Given output schema exists, description adequately explains that tool combines results into a complete profile. Sibling tools list provides context for alternatives.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds meaning to both parameters: company_name is 'the name of the company' and max_results is 'maximum number of sources per tool (1-50)'. Schema has 0% coverage, so description compensates.

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: comprehensive company research by calling specialized tools in parallel. It distinguishes from sibling tools by being a meta-tool for vague queries.

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?

Explicitly states when to use ('vague queries like research [company]'). Does not explicitly state when not to use, but context implies using siblings for specific aspects.

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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