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mcp-lightcast

mcp-lightcast

by lawwu
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Get Core Lmi Metadata

get_core_lmi_metadata
Read-onlyIdempotent

Inspect a Core LMI release’s attributes, dimensions, and available metrics before querying; filter metrics by substring and follow pagination to choose the right dataset version and metric year.

Instructions

Inspect a Core LMI release's attributes, dimensions and available metrics before querying. Filter metrics by substring (e.g. Jobs. or Earnings); follow metrics_pagination.next_offset. Release version is different from metric year.

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": {
      "dataset": "<dataset>"
    },
    "name": "get_core_lmi_metadata"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
offsetNo
datasetYes
versionNolatest
metric_queryNo

Output Schema

NameRequiredDescriptionDefault
metricsYes
attributesYes
dimensionsYes
datasetNameYes
versionNameYes
metrics_paginationYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.1

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, non-destructive, open-world behavior, so safety is covered. The description adds two useful behavioral notes – substring filtering of metrics and following metrics_pagination.next_offset – plus the version-vs-year caveat, but says nothing about response shape or limits. Solid context, not rich.

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?

Front-loaded with the core action, then two compact sentences covering filtering, pagination, and the version/year distinction. No filler, though the phrasing is telegraphic and the pagination pointer references an output field rather than the offset parameter.

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 needn't be described, and annotations cover safety. However, with five undocumented parameters and no output description, the gaps around dataset, limit, and offset leave the definition only adequate for a metadata-inspection tool.

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 coverage is 0% across five parameters, so the description must compensate. It explains metric_query (substring, with examples) and version (distinct from metric year), and hints at offset via next_offset, but leaves dataset, limit, and the difference between limit/offset unexplained.

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?

States a specific verb ("Inspect") and resource (a Core LMI release's attributes, dimensions, and available metrics), plus positions the call before querying. It does not name the sibling it precedes (query_core_lmi) explicitly, so distinction from that tool is implied rather than stated.

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

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

"before querying" gives a clear workflow context and sequencing cue, so an agent knows this is a discovery step prior to pulling data. It stops short of explicit when-not guidance or naming the alternative query tool.

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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