Linkup Company Research MCP

company_technology

Analyze a company's technology stack, patents, and technical approach by examining engineering blogs, job postings, tech detection tools, patents, and open source contributions.

Instructions

Research a company's technology stack, patents, and technical approach.

Analyzes engineering blogs, job postings, tech detection tools, patents, and open source contributions to understand technical capabilities.

Args: company_name: The name of the company to research output_format: "answer" for natural language with sources, "structured" for JSON max_results: Maximum sources to consider (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": "company_technology"
  }
}

Input Schema

Name	Required	Default
`max_results`	No
`company_name`	Yes
`output_format`	No	answer

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.4.0Jul 6, 2026

TDQS

A3.8/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions analyzing multiple source types and output formats but does not disclose potential limitations, rate limits, authentication needs, or whether results are cached.

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 concise, front-loaded with the core purpose, and well-structured with a clear Args breakdown. Every sentence serves a purpose without redundancy.

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 presence of an output schema and the moderate complexity, the description sufficiently covers the tool's scope and arguments. It could add more behavioral details (e.g., how sources are prioritized) but is mostly complete.

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?

Schema description coverage is 0%, but the description's Args section adds value by explaining the purpose and defaults of company_name, output_format, and max_results beyond the schema's basic property definitions.

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 explicitly states the tool researches a company's technology stack, patents, and technical approach, listing specific sources. It clearly distinguishes from sibling tools (e.g., company_overview, company_products) by focusing on technical capabilities.

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 description implies when to use (when needing technical research), but does not provide explicit when-not-to-use guidance or comparisons with alternatives among the 17 sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server