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LinkedIn MCP

LinkedIn MCP

by rohankumbhar-01
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

linkedin_capabilities_and_limits

Check which LinkedIn features are implemented and which require API approval before using them.

Instructions

Explain which features are implemented and which depend on LinkedIn API approval.

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_capabilities_and_limits"
  }
}

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?

With no annotations, the description carries the full burden, and it does disclose the key behavioral fact that some features are gated by LinkedIn API approval. However, it does not say whether calling it requires an OAuth token or authenticated session, nor whether the answer varies per account, which matters for an agent deciding whether it can invoke it safely.

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 no filler, and the substance (implemented vs approval-dependent) is front-loaded. Every phrase earns its place.

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?

An output schema exists, so return values need not be described, and with zero parameters the description is nearly complete. The only meaningful gap is the missing note on authentication requirements for invoking the tool.

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 the baseline is 4. The description correctly frames the tool as parameterless and informational rather than suggesting any filtering or scope input.

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?

The description uses a specific verb ('Explain') and states the resource precisely: which features are implemented versus dependent on LinkedIn API approval. That is clearly distinct from the action-oriented siblings (auth, post, profile). It stops short of explicitly contrasting itself with any sibling, so a 4.

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 can infer it should call this when unsure whether a capability is available. There is no explicit when-to-use, when-not-to-use, or naming of alternatives, and no prerequisites 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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