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shauryajain21

Linkup Company Research MCP

company_partnerships

Find partnerships, integrations, and strategic alliances for a company by researching partner pages, integration marketplaces, press releases, and partner programs.

Instructions

Find partnerships, integrations, and strategic alliances for a company.

Researches partner pages, integration marketplaces, press releases, and partner programs to map the company's ecosystem.

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)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
max_resultsNo
company_nameYes
output_formatNoanswer

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are present, so the description carries full burden. It discloses the research sources and scope (mapping ecosystem). As a read tool, it does not hide destructive behavior. The description adds value beyond a simple 'find partnerships' statement.

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 one short paragraph plus a bulleted arg list, front-loading the main purpose. No extraneous information. Could be slightly improved with clearer separation of purpose vs. args, but overall efficient.

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?

An output schema exists, so return values are covered outside the description. The description explains the research approach and output_format parameter. Given moderate complexity and no nested objects, it provides sufficient context for an agent to select and invoke the tool.

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

Parameters2/5

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

Schema description coverage is 0%, so description must compensate. However, it merely repeats parameter names and types (e.g., 'company_name: The name of the company to research'). It adds no extra detail about data types, constraints, or format expectations 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 verb 'Find' and the resource 'partnerships, integrations, and strategic alliances', specifying the sources researched (partner pages, integration marketplaces, press releases, partner programs). This differentiates it from sibling tools like company_clients or competitive_landscape.

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 usage context ('to map the company's ecosystem') but does not explicitly state when to use this tool versus alternatives. No 'when not to use' guidance is provided, leaving the agent to infer based on the tool name and sibling list.

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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