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Reach MCP — LinkedIn for AI agents

get_me

Read-onlyIdempotent

Call LinkedIn Voyager /me for an account through the stored proxy/cookies and return the normalized profile (same fields as Kanbox uses from /me).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
account_idYesReach id of the LinkedIn account to act on, from list_accounts.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pictureNoProfile picture URL derived from VectorImage artifacts.
headlineNo
lastnameNo
firstnameNo
is_premiumNo
linkedin_idNoMiniProfile id (entityUrn suffix after fs_miniProfile:).
linkedin_plain_idNoNumeric member id from objectUrn.
linkedin_public_idNoPublic / vanity identifier.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

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

The annotations already declare read-only, idempotent, and non-destructive behavioraint. The description adds the proxy/cookie mechanism and output normalization, which is helpful, but does not mention failure modes like expired credentials or invalid account IDs.

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 compact sentence conveys endpoint, auth context, return type, and output compatibility without filler. The key action is front-loaded.

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 the output schema exists and this is a simple read operation, the description covers the essential context: what it calls, how it authenticates, and what it returns. Extra detail about auth expiry or error behavior would be nice but is not required.

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%: account_id has a clear description. The tool description adds that it acts 'on an account' and points to list_accounts as the source, but this is marginal beyond the schema.

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 action and target: call LinkedIn's /me endpoint through stored proxy/cookies and return the normalized profile. This clearly differentiates it from general scraping or 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 Guidelines3/5

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

It provides useful context—acting on a specific account via stored credentials and using account_id from list_accounts—but does not explicitly contrast with sibling tools like scrape_profile or profile_viewers. The usage situation is implied rather than stated.

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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