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mcp-lightcast

mcp-lightcast

by lawwu
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Normalize Title And Get Skills

normalize_title_and_get_skills
Read-onlyIdempotent

Normalize a raw job title, classify it into LOT specialized occupations, and retrieve associated skills from Similarity; reports partial failures.

Instructions

Normalize a title, classify to LOT specialized occupations, then retrieve associated skills from Similarity. Requires three API permissions. Reports partial failures; version controls Titles only, classification_version controls classifiers, and lot_version must match the classifier's LOT major version.

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": {
      "raw_title": "<raw_title>"
    },
    "name": "normalize_title_and_get_skills"
  }
}

Input Schema

NameRequiredDescriptionDefault
versionNolatest
raw_titleYes
skill_typeNo
lot_versionNo7
max_occupationsNo
confidence_thresholdNo
classification_versionNolatest
max_skills_per_occupationNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.1

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already cover the safety profile (read-only, idempotent, open-world), yet the description adds genuinely new operational context: three required API permissions, partial-failure reporting, and the version coupling constraint that lot_version must match the classifier's LOT major version. The only gap is that the three permissions are not named.

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?

Three dense sentences, front-loaded with the pipeline and then the operational constraints; almost no filler. The semicolon-joined version clause is compact but slightly cryptic on a first read.

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?

An output schema exists, so return values need no explanation, and the description covers the workflow, permissions, partial failures, and version constraints. For an 8-parameter composite tool it still leaves substantial ambiguity around the tuning parameters, which is the main remaining gap.

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?

With 0% schema description coverage across 8 parameters, the description must carry the burden, and it does explain the three version fields and their distinct scopes, including the lot_version/classifier coupling. But five parameters (skill_type, max_occupations, confidence_threshold, max_skills_per_occupation, and the meaning of 'latest' for version) remain wholly undocumented anywhere.

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 names a concrete three-step pipeline with specific verbs and resources: normalize a title, classify to LOT specialized occupations, then retrieve associated skills from Similarity. This distinguishes it clearly from single-purpose siblings like normalize_job_title, though it never names an alternative explicitly.

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?

Usage is implied by the compound pipeline: an agent can infer this is for getting normalization, classification, and skills in one call. However, there is no explicit when-to-use guidance, no exclusions, and no comparison against close siblings such as normalize_title_and_extract_skills or classify_occupation.

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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