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mcp-lightcast

mcp-lightcast

by lawwu
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Map Taxonomy Concepts

map_taxonomy_concepts
Read-onlyIdempotent

Maps taxonomy concept IDs to target concepts using a mapping name returned by list_taxonomy_mappings, enabling consistent cross-taxonomy lookups.

Instructions

Map concept IDs using a mapping name returned by list_taxonomy_mappings.

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": {
      "mapping_name": "<mapping_name>",
      "ids": "<ids>"
    },
    "name": "map_taxonomy_concepts"
  }
}

Input Schema

NameRequiredDescriptionDefault
idsYes
mapping_nameYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.1

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so safety is covered. The description adds useful provenance context by tying mapping_name to list_taxonomy_mappings. It does not disclose what happens to IDs that have no mapping, whether unmapped entries are dropped or returned as-is, or any rate/batch limits.

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 tight sentence with the prerequisite called out rather than buried. No filler, no repetition of the tool name. Its brevity is close to optimal for a two-parameter tool, though one more clause on mapping direction would have paid for itself.

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?

An output schema exists, so return values need no explanation, and annotations cover the safety profile. With only two required parameters, the remaining gap is the semantics of the mapping itself (source vs target taxonomy, unmapped-ID behavior). Solid but not fully complete for a transformation 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 description coverage is 0%, so the description must carry the load. It clarifies that 'ids' are concept IDs and that 'mapping_name' is a value obtained from list_taxonomy_mappings, which is real added meaning. It says nothing about the 1000-item cap, ID format/validation, or ordering of results, so the two required parameters remain only partly explained.

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 ('Map') and resource ('concept IDs') plus the driving input ('a mapping name'). It is clearly distinct from sibling tools, and the reference to list_taxonomy_mappings signals the prerequisite step. It stops short of 5 because the actual transformation (which taxonomy is the source, which is the target) stays implicit.

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 definition embeds one workflow rule — mapping_name must come from list_taxonomy_mappings — which is genuine usage guidance. However, it never says when to pick this over normalization or classification siblings, nor when not to use it. Guidance is implied by a prerequisite rather than stated as a when-to-use policy.

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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