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Reach MCP — LinkedIn for AI agents

connect

Send a connection request to a LinkedIn member. Optional message capped at 300 chars (premium) or 200 chars.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageNoOptional personalised invitation note.
account_idYesReach id of the LinkedIn account to act on, from list_accounts.
idempotency_keyNoOptional. A key you choose (a UUID is fine) that names this exact call. If you retry with the same key and the same arguments, the first call's result is returned and nothing is done twice on LinkedIn. Reusing a key with different arguments is refused. Keys expire after 24 hours.
linkedin_id_or_urlYesLinkedIn member ID, vanity name, or profile URL.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
connectedNoInvitation sent

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already establish that this is a non-read, non-destructive, non-idempotent action. The description adds a meaningful behavioral nuance—message length is capped at 300 chars on premium and 200 otherwise—which is not captured in the schema. It does not disclose rate limits or account-level requirements, but the added cap detail earns a mid-score.

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?

Two focused sentences with zero filler. The core action is front-loaded, and the optional-message constraint is stated compactly. Every sentence earns its place.

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?

Given a fully documented schema, an output schema, and relevant annotations, the description is sufficient for correct invocation. It covers the primary action and message constraint; any additional details like authentication or rate limits would be nice but are not essential for this tool.

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%, so the schema already documents all four parameters, including the idempotency_key semantics. The description adds only the message cap detail, which is useful but not necessary for parameter understanding.

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 names a specific verb and resource: 'Send a connection request to a LinkedIn member.' This clearly differentiates it from sibling invitation-management tools like accept_invitation, decline_invitation, and withdraw_invitation, which represent different stages of the invitation lifecycle.

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: use this when you want to initiate a connection request to a LinkedIn member. It does not explicitly contrast with accepting or declining invitations, but the wording is unambiguous enough that a tool-selecting agent would not confuse it with the invite-management siblings.

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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