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kaistenberg

MCP Server for LinkedIn

by kaistenberg

Connect With Person

connect_with_person
Destructive

Send a LinkedIn connection request or accept incoming ones, optionally adding a personalized note.

Instructions

Send a LinkedIn connection request or accept an incoming one.

The tool is annotated with destructiveHint so MCP clients will prompt for user confirmation before execution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNoOptional note to include with the invitation
linkedin_usernameYesLinkedIn username (e.g., "stickerdaniel", "williamhgates")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

The description explicitly references the destructiveHint annotation and explains that MCP clients will prompt for user confirmation before execution. This adds behavioral context beyond the annotation itself, though it does not discuss other side effects like notifications or rate limits.

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 two sentences long, front-loaded with the core action, and every sentence earns its place. It is appropriately concise and easy to scan.

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?

For a simple two-parameter tool with an output schema, the description covers the essential behavior and the important confirmation side effect. The schema handles parameter details, so no further explanation is needed.

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?

The input schema has 100% description coverage, including examples for linkedin_username and a clear explanation of the optional note parameter. The tool description itself adds no parameter-level semantics, but the schema already carries that weight.

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 opens with a specific verb and resource: 'Send a LinkedIn connection request or accept an incoming one.' It clearly states the tool's action and target, and the dual send/accept behavior distinguishes it from sibling tools like send_message or get_person_profile.

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?

The description gives clear context for when to use the tool: to send a new connection request or accept an incoming one. It does not explicitly name alternatives or exclusions, but the usage scenario is unambiguous.

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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