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Morphologically analyze a Latin form

latin_lemmatize
Read-onlyIdempotent

Analyze one inflected Latin form with the Morpheus morphological analyzer: its lemma(s) plus the grammatical reading (person, number, tense, mood, voice, case…). Useful when you want the morphology itself — for the dictionary entry, latin_search already resolves inflected queries on its own. Returns Markdown plus the analysis as structuredContent matching the declared outputSchema. Results are cached server-side. Analyses via the Perseids Morpheus service.

Input Schema

NameRequiredDescriptionDefault
wordYesOne inflected Latin word form to analyze, e.g. 'amavit', 'aquam'. Macrons optional.

Output Schema

NameRequiredDescriptionDefault
wordYes
foundYes
sourceNoMorpheus morphological analyses via the Perseids Project morphology service (morph.perseids.org), Tufts University / Perseus Digital Library.
analysesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / word / pattern
      Added value: +"^[A-Za-zĀāĒēĪīŌōŪūȲȳÄäÖöËë\\-']{1,80}$"
  2. Changed1 schema field changed
    • changedInput schema / title
      Previous value: -"lemmatize_latinArguments"New value: +"latin_lemmatizeArguments"
  3. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint, so safety is covered. The description goes beyond them by disclosing the return shape (Markdown plus structuredContent matching the outputSchema), server-side caching, and the backing service (Perseids Morpheus). It does not mention rate limits, failure modes for unknown forms, or ambiguity 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?

Four sentences, front-loaded with the core action before the routing note, the return format, and the caching detail. Every sentence carries information; the ellipsis in '(person, number, tense, mood, voice, case…)' is slightly loose but not wasteful.

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?

With a rich output schema and full annotations, the description only needs to cover the purpose, routing, and any non-structured behavior — it does all three (return format, caching, backend service). The only gap is behavior on invalid or ambiguous forms, which an agent might still want to know.

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 100%: the schema documents the single 'word' parameter with a regex pattern, length bounds, examples and a note that macrons are optional. The description adds only the generic phrase 'one inflected Latin form', which does not go 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 names a specific action and resource: analyze a single inflected Latin form to produce lemma(s) plus a grammatical reading, and it lists the reading components (person, number, tense, mood, voice, case). It clearly differentiates itself from latin_search, but says nothing about latin_get_inflections, which is arguably the more confusable sibling given the name mismatch (lemmatize vs. get_inflections).

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?

It states when to use the tool ('when you want the morphology itself') and names an explicit alternative plus the condition that selects it ('for the dictionary entry, latin_search already resolves inflected queries on its own'). No exclusion is given relative to latin_get_inflections, so the routing guidance is good but not exhaustive.

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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