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shauryajain21

Linkup Company Research MCP

company_products

Research a company's products and services, including pricing and use cases. Filter by product name and get results as natural language or structured JSON.

Instructions

Get information about a company's products and services.

Researches the company's product pages, pricing, and documentation to provide detailed information about their offerings, pricing models, and use cases.

Args: company_name: The name of the company to research product_name: Optional filter for a specific product output_format: "answer" for natural language with sources, "structured" for JSON max_results: Maximum number of sources to consider (1-50)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
max_resultsNo
company_nameYes
product_nameNo
output_formatNoanswer

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, the description must disclose behavioral traits. It indicates the tool performs research (likely read-only) but does not explicitly state it is non-destructive, mention authentication needs, rate limits, or error handling. The disclosure is adequate but not thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear purpose sentence followed by an args list. It is concise but could integrate the parameters into flowing text for better readability. Front-loaded with the main action.

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 tool has 4 parameters and an output schema, the description covers the basics but lacks context on data freshness, error handling, or how results are sourced. It is minimally complete for a research tool but could add more usage context.

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

Parameters5/5

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

Schema coverage is 0%, so the description fully compensates. It explains the meaning and allowed values for all four parameters: company_name (required), product_name (optional filter), output_format (two options with descriptions), and max_results (range 1-50). This adds significant value beyond the schema.

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: 'Get information about a company's products and services.' It elaborates on researching product pages, pricing, and documentation, which is specific enough to distinguish it from sibling tools like company_overview or company_technology.

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 this tool (when needing product and service info) but does not explicitly state when not to use it or provide alternatives. No comparison with sibling tools or exclusions are mentioned, leaving some ambiguity.

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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