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by com.ksaworks
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Resolve Term

resolve_term
Read-only

Where does a word place in the KSA model? The KSA Dictionary is the shared vocabulary that defines the terms a learner uses to assert what they know, do, and are — and the terms a learning offering uses to express what it comprises — so both sides resolve to the same identifiers, never spelling matches. Answers from three linked layers (governed lexicon, O*NET content model, WordNet-derived index), each placement attributed to its layer with provenance; the WordNet placement carries the word's definition (gloss) and synonyms.

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": {
      "term": "<term>"
    },
    "name": "resolve_term"
  }
}

Input Schema

NameRequiredDescriptionDefault
termYesA word for something a person knows or can do, e.g. carpentry or mathematics

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / term / description
      Previous value: -"The word or phrase to resolve, e.g. carpentry or mathematics"New value: +"A word for something a person knows or can do, e.g. carpentry or mathematics"
    • addedInput schema / properties / term / examples
      Added value: +[
+  "carpentry",
+  "mathematics",
+  "welding"
+]
  2. Added

TDQS

A3.7/5.0
Behavior4/5

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

With readOnlyHint=true already covering the safety profile, the description goes further and discloses the return structure: three linked layers (governed lexicon, O*NET, WordNet index), per-layer attribution with provenance, and that the WordNet placement carries a gloss and synonyms. That is genuinely useful output-context that the annotations do not provide, though it says nothing about lookup failures or ambiguous-term handling.

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?

The opening question is a strong front-loaded hook and the rest is one dense but purposeful paragraph. Some phrasing ('so both sides resolve to the same identifiers, never spelling matches') is wordy, but each clause contributes semantic or output information rather than padding.

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?

For a single-parameter, read-only lookup with no output schema, the description supplies the missing return-shape information (layers, provenance, gloss, synonyms) and the conceptual purpose. It is nearly complete; only edge-case behavior (no match, ambiguity) is left unstated.

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 100% for the single 'term' parameter, including examples, so the schema already carries the parameter burden. The description adds only conceptual framing (a word for what a learner knows/does) and no syntax, format, or matching-rule detail beyond the schema, so the baseline 3 applies.

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 — placing a word within the KSA model / dictionary — and explains the semantic-resolution mechanic (shared identifiers, not spelling matches). It is clear what the tool returns, though it never explicitly differentiates itself from the related sibling expand_term, so an agent must infer the boundary.

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 opening question ('Where does a word place in the KSA model?') and by the vocabulary-resolution framing, but there is no explicit when-to-use, no when-not-to-use, and no named alternative such as expand_term or crosswalk_code. The agent gets context but must infer the routing decision.

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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