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mcp-lightcast

mcp-lightcast

by lawwu
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Get Career Skill Gap

get_career_skill_gap
Read-onlyIdempotent

Compare source and destination occupations within the same taxonomy to identify skill gaps, helping you plan career transitions and target the skills needed for a new role.

Instructions

Compare source and destination occupations within the same taxonomy to identify skill gaps.

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": {
      "source_id": "<source_id>",
      "destination_id": "<destination_id>"
    },
    "name": "get_career_skill_gap"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
nationNous
dimensionNolotspecocc
source_idYes
lot_versionNo7
destination_idYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.1

TDQS

B3.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=true, so safety and repeatability are covered without the description's help. The description adds the same-taxonomy constraint, which is genuine context, but says nothing about how the comparison is scoped, how results are ranked, or what limits apply.

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?

A single well-formed sentence with the operation front-loaded and no filler. It is efficiently written; the problem is omission rather than verbosity, so it does not lose points here for being short.

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?

Given six parameters at zero schema coverage, two enums, and a defaulted nation/dimension/version surface, one sentence is not enough to call this correctly. The existence of an output schema relieves the need to describe return values, but input-side completeness is clearly lacking.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% across six parameters, so the description carries the full burden and largely fails: it clarifies only that source_id/destination_id are occupations in a shared taxonomy. It says nothing about limit (default 20, max 100), nation (default 'us'), dimension (four-value enum), or lot_version.

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 states a specific verb and resource ('Compare source and destination occupations') plus the outcome ('identify skill gaps'), so the agent knows exactly what the tool produces. It does not name or distinguish itself from any sibling, which keeps it shy of a 5.

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 phrase 'within the same taxonomy' implies a precondition (both occupations must share a taxonomy/release), which is useful implied guidance. However, there is no explicit when-to-use, when-not-to-use, or pointer to an alternative such as get_career_next_steps or get_related_skills.

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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