livonian
Server Details
Grounds LLMs in the real Livonian dictionary: attested words, inflections, romanization.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.4/5 across 3 of 3 tools scored.
Tools are largely distinct: search_livonian does dictionary lookup with optional inflection tables, get_inflections provides dedicated inflection data for a handle, romanize_livonian converts orthography. However, search_livonian already includes inflections by default, creating minor functional overlap.
All tool names follow a consistent verb_noun pattern (get_inflections, romanize_livonian, search_livonian) in snake_case, with the domain suffix 'livonian' where appropriate.
Three tools is well-scoped for a specialized Livonian language server. Each covers a distinct aspect of the workflow (lookup, inflection, romanization) without unnecessary bloat.
The tool surface adequately covers core use cases: searching the dictionary, retrieving inflections, and transliterating. Minor gaps exist, such as lacking a direct text translation tool, but the set is functional for the stated purpose.
Available Tools
3 toolsget_inflectionsARead-onlyIdempotentInspect
Fetch the full declension (nouns) or conjugation (verbs) table for a word identified by a search result's inflection handle. This keeps the correct grammatical FORM — the right case, number, tense — instead of a guessed ending. Returns a Markdown table, or a clean not-found message.
| Name | Required | Description | Default |
|---|---|---|---|
| vards_id | Yes | The word id from a search result's inflection handle. Accepts a number or a digit-string (e.g. 17746 or "17746"). | |
| word_class | Yes | The word-class code from a search result's inflection handle (e.g. 's' = noun, 'vi' = intransitive verb, 'vt' = transitive verb). |
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 and idempotentHint. Description adds return format (Markdown table or not-found message) and clarifies it keeps correct grammatical form, providing useful behavioral context 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?
Three sentences with no wasted words. Front-loaded with the core action, then details the benefit and output. Perfectly concise.
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 moderate complexity and full schema coverage, plus annotations and output schema existence, the description provides all necessary context for correct usage.
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% with detailed parameter descriptions (vards_id type and word_class pattern/constraints). Description reiterates 'inflection handle' but adds no new information; baseline 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?
Description clearly states the tool fetches declension/conjugation tables for a word based on an inflection handle, distinguishing it from sibling tools like romanize_livonian and search_livonian.
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?
Specifies that the tool is used for words identified by a search result's inflection handle, implying prior use of search_livonian. Could explicitly mention when not to use, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
romanize_livonianARead-onlyIdempotentInspect
Strip Livonian orthography down to clean, pronounceable ASCII.
Strips macrons, diacritics and capitals, maps each special letter to an English-readable spelling (ā→aa, š→sh, õ→u), softens a palatalized consonant (ņ ļ ŗ ţ ḑ) only before a vowel (ņ+a→nya, else plain so ikšiggiņ→ikshiggin, since a word-edge "ny" would be mis-sung as a diphthong), and inserts a hyphen where an English voice would otherwise blur two sounds into one: the broken tone (stød), splitting a doubled consonant the way the syllable breaks (si'zzõl→siz-zul), and a long vowel before another vowel so a diphthong keeps its glide (kīen→kee-en). An English-trained downstream — a voice/TTS, a search box — then approximates the word instead of choking on the orthography. Pure transform: no dictionary lookup, no network, and the output is always ASCII.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Livonian text to reduce to clean, pronounceable ASCII. Strips macrons, diacritics and capitals (ā→aa, š→sh, õ→u), softens a palatalized consonant (ņ ļ ŗ ţ ḑ) only before a vowel (ņ+a→nya, else ikšiggiņ→ikshiggin), and hyphenates the broken tone (si'zzõl→siz-zul) and long-vowel diphthong glides (kīen→kee-en). Pass a word, phrase, or several lines. |
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 indicate readOnly, idempotent, non-destructive. The description adds rich behavioral details: rules for palatalization, hyphenation, broken tone, and explicitly states it is a pure transform with no network or dictionary lookup. No contradictions with 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?
Description is front-loaded with main purpose but is somewhat long due to detailed rules. However, every sentence adds value and explains rationale. Could be slightly more concise without losing clarity, but overall well-structured.
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 single parameter and presence of output schema, the description fully explains input constraints, transformation rules, and output properties (always ASCII). No missing context for an AI agent to use the tool correctly.
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?
Only one parameter 'text' with schema description already covering the rules. Schema coverage is 100%, so baseline is 3. The description adds no new parameter information beyond what the schema provides.
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 it strips Livonian orthography to ASCII, with a specific verb 'Strip' and resource 'Livonian orthography'. It distinguishes from siblings (get_inflections, search_livonian) which have different purposes.
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 implies use for preparing Livonian text for English-trained systems (TTS, search), but does not explicitly state when not to use or compare with alternatives. Context signals show siblings are different, so usage is clear but lacks exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_livonianARead-onlyIdempotentInspect
Look a word up in the real Livonian–Estonian–Latvian dictionary and return only attested content, so translations are grounded, not invented.
Search the meaning you want (in English/Latvian/Estonian) to find the
Livonian headword, or search a Livonian word to confirm it exists and check
its sense, part of speech and usage examples. search_language selects the
language your QUERY is in (default 'eng'); leave it at 'eng' for
English→Livonian.
By default this also returns each match's full INFLECTION TABLE inline, so
you get the correct cases/tenses for writing (lyrics, verse, prose) in a
single call — no separate get_inflections round-trip. For a broad query
that matches many words, only the first N tables are expanded (a note says
how many were left out); narrow the query or set max_forms. Returns
Markdown: the handles table, the inline inflection tables, and the entries.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The word or phrase to look up. May be Livonian, Latvian, Estonian, or English (e.g. 'jarā', 'away', 'sea'). Matching is bidirectional. | |
| max_forms | No | Optional cap on how many inflection tables to expand this call. Use a small value for broad queries (e.g. 'sea' matches many words); omit to use the server default. | |
| include_forms | No | When true (default), each match's full inflection table is returned INLINE — so you get usable cases/tenses in ONE call, no follow-up get_inflections needed. Bounded by a per-search cap; set false to get just the handles (faster for very broad queries). | |
| search_language | No | Language your query word is in — selects which language the search matches against: 'eng' (default), 'liv', 'lat', or 'est'. For English→Livonian translation, keep 'eng'. Glosses are in English. | eng |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate read-only, idempotent, non-destructive. The description adds that content is attested/not invented, and for broad queries only first N inflection tables are expanded with a note. Also mentions Markdown output format. Some behavioral traits are disclosed 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 front-loaded with core purpose and uses clear paragraphs. It is somewhat lengthy but each sentence adds value. Could be slightly more concise.
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 presence of an output schema (not shown), the description appropriately does not detail return fields. It still explains the Markdown format and handles. It addresses broad query behavior and is complete for a search tool.
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%. The description adds context for each parameter: query bidirectional with examples, search_language default and effect, include_forms default and trade-off, max_forms optional cap. This supplements the schema definitions.
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 searches a dictionary for Livonian words, returning attested content. It specifies the resource (Livonian–Estonian–Latvian dictionary) and action (look up), distinguishing from sibling tools by highlighting inline inflection tables and no need for separate get_inflections call.
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 explains when to use: to find headwords, confirm existence, get senses/POS/examples, and for writing with correct inflections. It provides guidance on max_forms for broad queries and defaults. However, it does not explicitly contrast with romanize_livonian or state when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!