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dzigi00

LinkedIn Automation MCP Server

by dzigi00

Connect With Person

connect_with_person
Destructive

Send LinkedIn connection requests or accept incoming ones, with an optional note to personalize your invitation and grow your professional network.

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

Behavior3/5

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 confirmation, adding context beyond the raw annotation. However, it does not disclose other behavioral details such as rate limits or side effects on profile visibility.

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: one stating the action and one explaining the annotation behavior. No filler or 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?

For a simple two-parameter tool with an output schema, the description adequately covers the core purpose. It could optionally mention the acceptance flow or edge cases, but it is not incomplete enough to warrant a lower score.

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?

Both parameters are fully documented in the schema with examples (linkedin_username, note), and schema description coverage is 100%. The description adds no extra parameter semantics beyond what the schema already provides.

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 'Send a LinkedIn connection request or accept an incoming one,' using a specific verb and resource. This distinguishes it from sibling tools like send_message and search_people.

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 conveys the general context (connection requests) but does not explicitly mention when to use it versus alternatives like send_message. It implies usage without defining exclusions or alternative scenarios.

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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