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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 Years Of Experience

get_years_of_experience

Retrieve the total years of work experience from a LinkedIn profile URL. Submit the profile link to get the computed experience sum.

Instructions

Get the total number of years of experience of a profile.

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": "get_years_of_experience"
  }
}

Input Schema

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

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Get' implies a read-only operation, but the description does not state what happens when a profile has no experience data, whether 'total' spans the full career or just the current role, whether a publicly visible URL is required, or any auth/error behavior. For a tool with zero annotation coverage, this leaves the agent guessing about edge cases.

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 zero filler: verb, resource, and scope in one breath. For a one-parameter tool this is appropriately sized; every word earns its place.

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?

This is a low-complexity tool (1 parameter, high schema coverage, no enums, no nested objects), and the description covers the invocation contract for the happy path: input a profile URL, get back a number of years. But with no annotations and no output schema, the ambiguity of 'total' (career-wide vs. current role) and missing handling of invalid/private profiles are clear gaps. Adequate as a minimum viable definition.

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%: the single linkedin_url parameter already includes an example value. The description's phrase 'of a profile' loosely confirms the URL identifies the target profile but adds no semantic detail beyond the schema. Baseline 3 is correct when the schema does the heavy lifting.

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 ('Get') and a precise resource ('total number of years of experience of a profile'), so an agent knows exactly what data point is returned. It is unambiguous against closely related siblings like get_extra_profile_data or get_open_profile_status because it pinpoints a single scalar value. However, it never names or explicitly differentiates a sibling, which keeps it at 4 rather than 5.

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 offers no guidance on when to prefer this tool over the many profile-data siblings, no exclusions, and no prerequisites (e.g., whether the linkedin_url must come from a prior lead/enrichment step). Usage is only weakly inferable from the name itself, which the calibration treats as 'no guidance' rather than genuine context.

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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