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Reach MCP — LinkedIn for AI agents

list_received_invitations

Read-onlyIdempotent

List pending connection invitations received by the account (with invitation_secret to accept them).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNoPage size (max 100).
startNoPagination offset, 0-based.
account_idYesReach id of the LinkedIn account to act on, from list_accounts.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNo
startNo
totalNo
invitationsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context: it lists pending invitations and includes the invitation_secret needed to accept them. This goes beyond the annotations by explaining what the returned data enables.

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?

One sentence with no waste. The core purpose is front-loaded, and the parenthetical about invitation_secret adds valuable context without bloating the description.

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?

The tool has an output schema, so return values need not be described. The description covers the essential purpose and the key behavioral detail (invitation_secret). It doesn't mention pagination, but the schema already documents count/start, so nothing critical is missing.

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 three parameters. The description adds no parameter-specific meaning beyond what the schema provides, so the baseline 3 is appropriate.

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 states a specific verb ('List'), a specific resource ('pending connection invitations received by the account'), and adds a key detail (the invitations include invitation_secret to accept them). This clearly distinguishes it from siblings like list_sent_invitations and accept_invitation.

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 implies the tool is for viewing received pending invitations, which is clear context. It doesn't explicitly state when not to use it or name alternatives like list_sent_invitations, but the phrase 'received by the account' and the sibling list make the usage context reasonably clear.

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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