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mcp-lightcast

mcp-lightcast

by lawwu
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Get Core Lmi Versions

get_core_lmi_versions
Read-onlyIdempotent

List available Core LMI dataset releases, comparing numeric version segments instead of array order, so you can pin a release for reproducible queries.

Instructions

List releases for an accessible Core LMI dataset. latest_numeric is discovered numerically, never taken from array order. Pin a returned release for reproducible queries; base/example require explicit selection.

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_versions"
  }
}

Input Schema

NameRequiredDescriptionDefault
datasetYes

Output Schema

NameRequiredDescriptionDefault
datasetYes
versionsYes
latest_numericYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.1

TDQS

B3.4/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, open-world, non-destructive behavior. The description adds non-obvious semantics: latest_numeric is computed numerically rather than from array order, and releases can be pinned for reproducibility. It does not cover error behavior or data-access prerequisites.

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 compact sentences with the release-listing purpose first and key ordering/pinning caveats after. The phrase 'base/example require explicit selection' is terse and somewhat ambiguous, but there is little wasted wording.

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?

With an output schema and annotations present, the description need not explain return shape or safety. It covers release ordering and pinning, but leaves the required dataset argument's semantics and acquisition path unexplained, which is a meaningful gap for a one-parameter tool.

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?

The sole dataset parameter has 0% schema description coverage. The description calls it an 'accessible Core LMI dataset' but does not specify the expected format (ID vs name), how to discover one, or constraints beyond minLength. It therefore does not compensate for the schema gap.

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 'List releases for an accessible Core LMI dataset,' giving a specific verb, resource, and scope. It is distinguishable from dataset-listing siblings by focusing on releases for a dataset, though it does not explicitly name an alternative.

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?

Provides usage context for returned releases, including pinning for reproducibility and explicit selection for base/example. However, it does not say when to use this instead of list_core_lmi_datasets or how to obtain an accessible dataset, leaving the main selection decision implicit.

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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