LinkedIn MCP Zero

by SanthaKumar-K-2004

Overview Schema Source Related Servers Discussions Admin

expand_skill_cluster

Read-onlyIdempotent

Expand a technology or skill into related skills and ecosystem connections using an in-process knowledge graph to support skill discovery and mapping.

Instructions

Explore technology ecosystems and related skills via in-process knowledge graph.

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": {
      "skill": "<skill>"
    },
    "name": "expand_skill_cluster"
  }
}

Input Schema

Name	Required	Description	Default
`skill`	Yes	Technology or skill name (e.g. 'Python', 'FastAPI', 'Docker')

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.4.8Nov 4, 2026

TDQS

A3.6/5.0

Behavior3/5

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

Annotations already establish that the operation is read-only, idempotent, and non-destructive. The phrase "in-process knowledge graph" adds useful source and execution context, but graph coverage, freshness, and limitations remain undisclosed.

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 compact sentence front-loads the purpose and ends with useful implementation context. There is no filler or redundant wording.

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?

For a one-parameter read-only exploration tool with an output schema, the description is sufficient to understand basic invocation. It remains less complete on tool-selection guidance and the knowledge graph's scope or limitations.

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 fully documents the required skill string and supplies examples, so the baseline score applies. The description reinforces the technology domain but adds no naming, normalization, or disambiguation guidance beyond the schema.

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 states an exploration action over technology ecosystems and related skills, with a knowledge graph as the mechanism. It does not explicitly distinguish this skill-focused tool from sibling industry or job-insight tools.

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?

The wording implies using the tool to start from a technology and discover its ecosystem or adjacent skills. However, it provides no explicit when-to-use guidance, exclusions, or alternatives such as get_industry_insights or deep_industry_analysis.

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