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1036007003-wq

linkedin-marketing-mcp

draft_connection_request

Draft personalized LinkedIn connection requests using templates based on the recipient's role and your purpose. Simply provide target role and reason for connecting.

Instructions

FREE: Draft a LinkedIn connection request message. Template-based (no API key needed).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
your_nameNoYour name
target_roleYesRole of the person you want to connect with (e.g. "AI Engineer", "Marketing Director")
your_companyNoYour company (optional)
your_purposeYesWhy you want to connect (e.g. "discuss AI marketing tools", "explore partnership")
Behavior2/5

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

No annotations provided, so description carries full burden. Only mentions 'template-based' and 'no API key needed', but omits output format, side effects, or customization details. Minimal behavioral disclosure for a drafting tool.

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?

Two short sentences are efficient and front-loaded, but could incorporate a hint about output (e.g., 'Returns a personalized message') without adding bulk. Still, no wasted words.

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?

Adequate for a simple tool with no output schema, but lacks any mention of return value or behavior beyond drafting. Would benefit from specifying that the result is a text message string.

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 description coverage is 100% with clear descriptions for all 4 parameters. Description adds no extra parameter context, but baseline 3 is appropriate since schema already defines each field adequately.

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?

Clearly states verb 'Draft' and resource 'LinkedIn connection request message'. Distinguishes from sibling tools (analyze, search, generate lists, optimize posts) by focusing on drafting a connection message.

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?

Implies use when needing a free connection request draft without API key, but lacks explicit when-not or alternative recommendations. Context from sibling tools helps, but description alone could be more directive.

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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