Fresh Linkedin Profile Data MCP Server

Get Recommendation Received

get_recommendation_received

Fetch recommendations received on a LinkedIn profile by providing its URL. Access endorsement data to analyze professional credibility and social proof.

Instructions

Get profile’s recommendations (received). 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_received"
  }
}

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

A3.5/5.0

Behavior3/5

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

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It does add value by stating the credit cost per call, which is a meaningful operational trait, but it does not describe the return format, permissions, or other side effects. For a simple read-style tool, this is adequate but not rich.

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?

The description is a single, front-loaded sentence with no filler. The credit-cost note is immediately relevant and the entire description can be consumed in under three seconds.

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

Completeness3/5

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

For a one-parameter read-type tool, the core purpose is clear and the cost is disclosed. However, without annotations or an output schema, an agent is left to infer the response shape and when this should be chosen over get_recommendation_given. The description is adequate but not complete.

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?

The input schema fully documents the only parameter, linkedin_url, including an example URL, so schema coverage is high. The description adds no additional parameter semantics beyond implying that the profile is identified by a LinkedIn URL, which is already captured by 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 uses a specific verb ('Get') and a clear resource ('profile's recommendations (received)'). The word 'received' directly distinguishes this from the sibling tool get_recommendation_given, so an agent can confidently select between them.

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?

No guidance is given about when to use this tool versus alternatives. The cost note ('1 credit per call') is useful operationally, but the description does not mention get_recommendation_given or any other condition that would route an agent to a different tool.

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

Deploy Server