Fresh Linkedin Profile Data MCP Server

Get Recommendation Given

get_recommendation_given

Retrieve recommendations a LinkedIn profile has given to others by providing its URL. Each call uses 1 credit.

Instructions

Get profile’s recommendations (given). 1 credit per call.

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": {
      "linkedin_url": "<linkedin_url>"
    },
    "name": "get_recommendation_given"
  }
}

Input Schema

Name	Required	Description	Default
`linkedin_url`	Yes	Example value: https://www.linkedin.com/in/ajjames

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.0Sep 4, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It adds the per-call credit cost but does not disclose response format, pagination, authentication requirements, failure modes, or whether the operation has side effects.

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?

The description is compact and front-loads the primary purpose and cost. It wastes no words, though the brevity comes at the expense of useful context. For a one-parameter tool, this level of conciseness is mostly appropriate.

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

Completeness2/5

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

For a tool with no annotations and no output schema, the definition is under-specified. It leaves the meaning of 'given' implicit, does not distinguish from get_recommendation_received within the description, and gives no sense of what response the agent should expect.

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 description coverage is 100%, so the linkedin_url parameter is already documented with an example value. The description adds no additional meaning about URL format, validation, or relationship to the profile, so it does not exceed the baseline.

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 clearly identifies the action ('Get') and resource ('profile's recommendations'), and the parenthetical 'given' contrasts with the sibling tool get_recommendation_received. However, it does not explicitly state whether these are recommendations written by or for the profile, leaving some ambiguity.

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?

The description provides no explicit guidance on when to use this tool versus alternatives like get_recommendation_received. The tool name and sibling list imply a distinction, but the definition itself offers no selection criteria or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server