estonian-mcp

find_related_words

Read-onlyIdempotent

Finds contextually related Estonian words by returning fastText nearest neighbours ranked by cosine score, including near-synonyms, related concepts, inflections, and typos.

Instructions

Nearest neighbours of an Estonian word in a fastText model (Common Crawl and Wikipedia), ranked by cosine score. Unlike synonyms (same meaning, WordNet), these are words used in similar contexts: near-synonyms, related concepts, inflections of the input itself (lemmatize to dedupe) and sometimes antonyms (tark: loll). Senses are not separated: lahe gets whichever of "bay" and "cool" dominates the corpus. Any string, even a typo, gets neighbours.

Input Schema

Name	Required	Description	Default
`n`	No	How many nearest-neighbour words to return (1-50).
`word`	Yes	One Estonian word, any form.

Output Schema

Name	Required	Description	Default
`matches`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed9 schema fields changedv0.9.0Oct 24, 2026
- removedInput schema / properties / n / title
```
Removed value: -"N"
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- changedInput schema / properties / word / description
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Previous value: -"A single Estonian word to find semantically related words for."New value: +"One Estonian word, any form."
```
- removedInput schema / properties / word / title
```
Removed value: -"Word"
```
- removedInput schema / title
```
Removed value: -"find_related_wordsArguments"
```
- removedOutput schema / properties / matches / default
```
Removed value: -null
```
- removedOutput schema / properties / matches / items / additionalProperties
```
Removed value: -true
```
- removedOutput schema / properties / matches / title
```
Removed value: -"Matches"
```
- removedOutput schema / properties / word
```
Removed value: -{
-  "default": null,
-  "title": "Word",
-  "type": "string"
-}
```
- removedOutput schema / title
```
Removed value: -"_RelatedWordsResult"
```
First observedv0.2.4Jul 5, 2026

TDQS

A4.9/5.0

Behavior5/5

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

The description adds substantial behavioral context beyond the annotations: it names the fastText model and corpora, explains cosine-score ranking, discloses that senses are not separated, and warns that inflections may appear and should be deduplicated via lemmatization. This is exactly the kind of operational detail an agent needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three dense sentences, front-loads the core operation, and uses the remaining space for sibling differentiation and important caveats. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the annotations, output schema, and 100% parameter coverage, the description is complete: it explains model provenance, ranking, sense ambiguity, and edge-case behavior. No critical invocation detail is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents both parameters, including the `n` range. The description still adds useful meaning for the `word` parameter by stating that it accepts any Estonian word form, including typos, and by explaining that inflected neighbours may appear.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific operation and resource: nearest neighbours of an Estonian word in a fastText model, ranked by cosine score. It explicitly distinguishes itself from the sibling `synonyms` tool by contrasting context-based neighbours with WordNet same-meaning synonyms.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It clearly tells the agent when to use this tool instead of `synonyms`, and explains what kinds of relations to expect: near-synonyms, related concepts, inflections, and sometimes antonyms. It also notes that any string, including typos, will return neighbours, setting expectations for edge cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.