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Fresh Linkedin Profile Data MCP Server

Fresh Linkedin Profile Data MCP Server

by BACH-AI-Tools
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Get Profile Pdf Cv

get_profile_pdf_cv

Convert any LinkedIn profile URL into a PDF CV. Download the profile's resume as a PDF file.

Instructions

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_profile_pdf_cv"
  }
}

Input Schema

NameRequiredDescriptionDefault
linkedin_urlYesExample value: https://www.linkedin.com/in/williamhgates/

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

D1.6/5.0
Behavior2/5

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

No annotations exist, so the description must carry behavioral disclosure. It does disclose the per-call credit cost, which is a useful non-functional trait, but it omits what the tool returns, errors, or side effects. The main behavior is entirely absent.

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

Conciseness2/5

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

The text is extremely short, but this is under-specification rather than concise description. A single cost note cannot substitute for a functional explanation; the description is not front-loaded with the tool's purpose.

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

Completeness1/5

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

With no output schema and no annotations, an agent needs to know what 'get_profile_pdf_cv' does, what input format is expected, and what the output will be. The description provides none of that, only credit cost, making it inadequate for safe invocation.

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 schema covers 100% of parameters (linkedin_url), and the parameter description includes an example URL. The tool description adds nothing about the parameter's meaning beyond that example, but with high schema coverage the baseline of 3 applies.

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

Purpose1/5

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

The description is only '**1 credit per call.**' — it states a cost but never describes what the tool does. The title hints at fetching a profile PDF CV, but the description itself provides no functional purpose, so an agent cannot infer the operation.

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

Usage Guidelines1/5

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 any of the 40+ siblings. There is no mention of contexts, prerequisites, or alternatives.

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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