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). Use to confirm which LinkedIn profile an account is (name, headline, premium); one LinkedIn call. Not needed before every action.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "account_id": "<account_id>"
    },
    "name": "get_me"
  }
}

Input Schema

Name	Required	Description	Default
`account_id`	Yes	Reach id of the LinkedIn account to act on, from list_accounts.

Output Schema

Name	Required	Description
`picture`	No	Profile picture URL derived from VectorImage artifacts.
`headline`	No
`lastname`	No
`firstname`	No
`is_premium`	No
`linkedin_id`	No	MiniProfile id (entityUrn suffix after fs_miniProfile:).
`linkedin_plain_id`	No	Numeric member id from objectUrn.
`linkedin_public_id`	No	Public / vanity identifier.

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 10, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld). The description adds real value beyond them: it discloses the auth mechanism (stored proxy/cookies) and the cost/rate signal ('one LinkedIn call'). It stops short of describing failure modes or error behavior, so not a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences, front-loaded with the core action and followed by usage and cost notes. Slightly jargony parenthetical ('same fields as Kanbox uses from /me') but no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists so return values needn't be spelled out, and annotations carry the safety profile. The description fully covers what an agent needs: purpose, when to call, auth path, and cost.

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% and the single account_id param is fully documented in the schema. The description refers to 'an account' generically without adding format or sourcing detail beyond what the schema's 'from list_accounts' already provides, so baseline 3 applies.

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?

States a specific verb and resource (call Voyager /me for an account) and names the concrete return (normalized profile with name, headline, premium). This distinguishes it from siblings like scrape_profile, list_accounts, and get_account_quotas without needing to open any schema.

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

Usage Guidelines5/5

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

Explicitly says when to use it ('Use to confirm which LinkedIn profile an account is') and when not ('Not needed before every action'), and notes it costs one LinkedIn call. The alternative behavior is clearly scoped.

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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