latin
Server Details
Grounds LLMs in real Latin: Wiktionary lemmas, Morpheus inflections, citations.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.6/5 across 3 of 3 tools scored.
Each tool serves a distinct purpose: search_latin for full lookups, get_inflections for detailed paradigms, and lemmatize_latin for morphological analysis. No functional overlap.
All names use snake_case and descriptive verbs, but get_inflections lacks the 'latin' suffix present in the other two, creating a minor inconsistency.
Three tools cover the core functionality of Latin language lookup, inflection, and analysis without being excessive or too sparse.
Covers essential operations (search by word/English, get inflections, morphological analysis) with minor gaps like lack of a direct lemma lookup tool, but most workflows are supported.
Available Tools
3 toolslatin_get_inflectionsGet a Latin inflection tableARead-onlyIdempotentInspect
Fetch the full declension (nominals) or conjugation (verbs) table for a lemma identified by a search result's inflection handle (entry_id + word_class). Use this only when a search ran without inline forms or left a match un-expanded — a default search already returns each match's table inline. Returns Markdown plus the table as structuredContent with the shape {"result": } per the declared outputSchema — switch on result.category ('nominal' | 'verbal' | 'not_found') before reading the body. Content from en.wiktionary.org (CC BY-SA 4.0).
| Name | Required | Description | Default |
|---|---|---|---|
| entry_id | Yes | The Wiktionary page title (the lemma) from a search result's inflection handle, e.g. 'amo', 'aqua'. | |
| word_class | Yes | The part-of-speech section from a search result's inflection handle, e.g. 'verb', 'noun', 'adjective'. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral context: 'Returns Markdown plus the table as structuredContent with the shape {"result": <paradigm>} per the declared outputSchema — switch on result.category before reading the body.' It also notes the content source and license, providing valuable transparency beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences: definition, usage guideline, and return format/license. It is front-loaded with the core purpose, and every sentence adds essential information without redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complete schema (100% coverage, 2 params), rich annotations, and presence of an output schema, the description covers purpose, usage, return format, processing instructions, and licensing. There are no apparent gaps in the information needed for correct tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds context by linking parameters to sibling tool output ('from a search result's inflection handle'), which helps the agent understand how to obtain correct parameter values. This slight extra value justifies a 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Fetch the full declension (nominals) or conjugation (verbs) table for a lemma identified by a search result's inflection handle (entry_id + word_class).' This is a specific verb+resource+scope, and it distinguishes from sibling tools latin_lemmatize and latin_search by focusing on inflection tables after a search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use this only when a search ran without inline forms or left a match un-expanded — a default search already returns each match's table inline.' This provides clear when-to-use and when-not-to-use guidance, including a comparison with the default search behavior.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
latin_lemmatizeMorphologically analyze a Latin formARead-onlyIdempotentInspect
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.
| Name | Required | Description | Default |
|---|---|---|---|
| word | Yes | One inflected Latin word form to analyze, e.g. 'amavit', 'aquam'. Macrons optional. |
Output Schema
| Name | Required | Description |
|---|---|---|
| word | Yes | |
| found | Yes | |
| source | No | |
| analyses | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations indicating readOnlyHint and idempotentHint, the description discloses server-side caching, the specific Morpheus service used, and the output format (Markdown + structuredContent matching outputSchema), giving comprehensive behavioral insight.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is four sentences, front-loaded with the primary action, and every sentence adds value. No redundancy or wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with an output schema and clear annotations, the description covers purpose, usage guidance, behavioral details, and service source. It is fully adequate without needing to explain return values due to output schema availability.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers the single parameter 'word' with examples and constraints. The description adds no new parameter semantics beyond restating the purpose, so baseline 3 applies given 100% schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool analyzes one inflected Latin form using Morpheus to return lemmas and grammatical readings. It explicitly distinguishes from the sibling latin_search, which resolves inflected queries for dictionary entries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises when to use this tool (for morphology) versus latin_search (for dictionary entry), providing clear guidance on alternative tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
latin_searchLook a Latin word upARead-onlyIdempotentInspect
Look a Latin word up on Wiktionary and return its senses plus full declension/conjugation tables — attested content, not invented. Any form of the word works; an inflected query is resolved to its lemma automatically (via previously cached paradigms or the Morpheus analyzer) and the result notes the resolution. With search_language='eng' the query is an English word instead: the result lists its per-sense Latin equivalents (the translations block) plus their expanded entries. Returns Markdown plus the same result as structuredContent matching the declared outputSchema.
Results are cached server-side; first-time queries reach the live upstream politely and calls are rate limited — on a rate-limit error, wait a few seconds and retry. Content is from en.wiktionary.org (CC BY-SA 4.0 — attribute and share alike if republished).
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The word to look up, in the language search_language names. Latin (default): a SINGLE word, any form — an inflected form ('amavit') is resolved to its lemma ('amo') automatically, and the result names it (search_method tells you how; resolved_lemma is the lemma to follow for the full paradigm); macrons are optional ('amo' and 'amō' both work). English (search_language='eng'): the English word whose Latin equivalents you want — multiword entries like 'give up' work. | |
| max_forms | No | Optional override for how many inflection tables to expand this call (0–12). On an uncached query each table is one politely paced upstream fetch, so high values on cold queries are slow. Omit for the server default. | |
| include_forms | No | When true (default), each match's full declension/conjugation table is returned INLINE — usable cases/tenses in ONE call, no follow-up latin_get_inflections. Set false for a cheap screen of which entries exist. Bounded by a per-search cap — use max_forms to adjust per call. For eng queries this governs the expanded Latin entries; the per-sense translations list itself is always returned. | |
| search_language | No | Language the query word is in: 'lat' (default) looks the Latin word up directly; 'eng' finds the Latin equivalents of an English word (per-sense, from Wiktionary's translation tables) and returns their full entries. Glosses are in English either way. | lat |
Output Schema
| Name | Required | Description |
|---|---|---|
| found | Yes | False when nothing matched. For an eng query, True means Latin translations were found — entries may still be empty when none of them could be expanded (see translations). |
| query | Yes | |
| source | No | |
| entries | No | |
| handles | No | |
| language | Yes | |
| translations | No | Populated only for eng queries (always [] for lat): the Latin terms each English sense translates to, in sense order. Entries/handles below are the expanded dictionary entries of those terms. |
| resolved_from | No | The query whose lemma this result names (paired with resolved_lemma); else empty. lat queries only (always empty for eng). Set both when an inflected miss was re-searched (search_method lemma_index/morpheus) and when the query's own page is a form-of stub returned directly (search_method stays 'direct'). |
| search_method | No | How the match was found: 'direct' = the query itself matched; 'lemma_index' = an inflected form resolved via a previously cached paradigm; 'morpheus' = resolved via the Morpheus morphological analyzer; 'translations' = an English query resolved via Wiktionary translation tables. |
| resolved_lemma | No | The lemma this query maps to; else empty. lat queries only. With search_method 'lemma_index'/'morpheus' the entries below ARE this lemma's; with 'direct' the entries are the queried form-of stub and this names the lemma whose page holds the full paradigm (search it / latin_get_inflections it). Only set when unambiguous — a form mapping to several lemmas leaves it empty (use lemmatize); the per-sense form_of still carries each. |
| forms_truncated | No | How many handles did NOT get an inline table because the per-search cap was reached. |
| translations_truncated | No | How many distinct translated lemmas were NOT expanded into entries because the per-search expansion cap was reached (they still appear under translations). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, idempotent, and nondestructive. The description goes beyond this by disclosing that results are cached, first-time queries are politely rate-limited, and on rate-limit errors the agent should wait and retry. It also notes the Markdown and structuredContent return format, and the CC BY-SA 4.0 licensing, providing full behavioral transparency with no contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured, starting with the main purpose and then covering dual-language mode, return format, caching, rate limits, and licensing. Every sentence adds value, though it is slightly verbose. It is front-loaded with the core functionality, making it efficient for an AI agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (inflected-form resolution, dual languages, caching, rate limiting) and the presence of a complete output schema, the description covers most necessary context. It explains how the tool resolves inflected forms, handles English queries, and the return type. Minor omissions like explicit differentiation from siblings are acceptable given the schema coverage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with each parameter already richly described (min/max, defaults, behavior of include_forms for queries, etc.). The tool description does not add significant new meaning beyond what is in the schema, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool looks up a Latin word on Wiktionary and returns senses and full declension/conjugation tables. It also covers the reverse lookup for English words and explicitly distinguishes from sibling tools by noting that include_forms=true eliminates the need for a follow-up latin_get_inflections call. The verb 'look up' with specific resource 'Wiktionary' makes the purpose concrete.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides good context on when to use the tool (for Latin word lookup with or without inflected forms, and for English-to-Latin reverse lookup). It mentions that include_forms=true avoids a separate latin_get_inflections call, which helps the agent decide on alternatives. However, it does not explicitly state when not to use this tool versus its siblings, leaving some ambiguity.
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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