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LinkedIn MCP

LinkedIn MCP

by rohankumbhar-01
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

linkedin_connection_status

Check if a LinkedIn access token is stored before making authorized requests; returns only connection status and never exposes the token.

Instructions

Check whether a LinkedIn access token is stored; never returns the token.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "linkedin_connection_status"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

No annotations exist, so the description carries the full burden. It usefully discloses a security-relevant trait – the token is never returned – but says nothing about whether this is a purely local storage read or a live validation call against LinkedIn, nor about any auth prerequisites for the check itself. Output schema coverage limits how much return-value detail is needed.

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?

A single sentence with two clauses, both load-bearing: what it checks and what it will not expose. Front-loaded verb, zero filler.

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 zero-arg status check with an output schema, this is nearly sufficient – the storage-vs-validity semantics are clear. The remaining gap is whether an agent should treat a positive result as 'ready to post' or whether a separate validation step exists.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Zero parameters, so there is no parameter semantics to explain; the baseline of 4 applies. The description correctly avoids inventing parameter language for a no-arg tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Check whether a LinkedIn access token is stored.' The word 'stored' precisely scopes it to local credential presence rather than token validity, which is a meaningful distinction against siblings like linkedin_start_oauth and linkedin_exchange_code. It stops short of naming an alternative, but the operation is unambiguous.

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?

Usage is only implied – an agent infers it should call this before attempting authenticated operations like linkedin_get_my_profile or linkedin_publish_approved_post, but the description never says so. No exclusions or alternative routing are 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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