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Reach MCP — LinkedIn for AI agents

invitation_status

Read-onlyIdempotent

Get the connection and invitation status between the connected account and a LinkedIn member. Returns is_connection, invitation_type (SENT/PENDING/WITHDRAWN/REFUSED).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
account_idYesReach id of the LinkedIn account to act on, from list_accounts.
linkedin_id_or_urlYesThe LinkedIn member: profile URL, vanity name (the part after /in/), Sales Navigator URL, or internal member id.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
degreeNoConnection distance (1, 2 or 3).
invitation_idNoInvitation URN, if any.
is_connectionNoTrue if the member is a 1st-degree connection.
invitation_typeNo'SENT' (pending sent), 'PENDING' (received, awaiting acceptance), 'WITHDRAWN', or 'REFUSED'.
invitation_secretNoShared secret, required to accept a received invitation.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already signal read-only, idempotent, non-destructive behaviortons. The description adds behavioral clarity by specifying the returned fields and the valid invitation_type values, which is useful context beyond the annotations. No contradiction with the annotations.

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 tightly written sentences that state the purpose and the key output fields. Every word earns its place, and the most important detail (read-only status check) is front-loaded in the verb 'Get'.

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 read-only status query with a clear schema, the description is mostly sufficient. It includes the output enum values and the object of the query/Tn. It does not explain possible edge cases or what happens if the member is not found, but the output schema likely covers return structure. Minor gap in missing routing guidance to list_sent_invitations/list_received_invitations for broader status queries.

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 coverage is 100%, with both account_id and linkedin_id_or_url clearly described. The tool description adds no additional parameter meaning beyond what the schema already provides, so a baseline score of 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 uses a specific verb ('Get') and clearly names the object: connection and invitation status between the connected account and a LinkedIn member. It also lists the key returned fields, making the purpose immediately understandable and distinct from broader listing tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given on when to use this tool versus siblings like list_sent_invitations, list_received_invitations, or withdraw_invitation. There is no mention of prerequisites or scenarios where this tool is preferred, leaving the agent to infer its role from the name and description alone.

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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