estonian-mcp

pos_tag

Read-onlyIdempotent

Assign part-of-speech tags to each word in Estonian text, omitting punctuation, to support morphological analysis and language-aware processing.

Instructions

Part-of-speech tag of each word (most likely reading); punctuation omitted.

S noun, H proper noun, V verb, A adjective (C comparative, U superlative, G indeclinable), P pronoun, D adverb, K adposition, J conjunction, N cardinal, O ordinal, I interjection, Y abbreviation or symbol (§, €), X verb-bound word (plehku), Z a word of 40+ characters, too long for Vabamorf to analyse.

Input Schema

Name	Required	Description	Default
`text`	Yes	Estonian text.

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed6 schema fields changedv0.9.0Oct 24, 2026
- changedInput schema / properties / text / description
```
Previous value: -"Estonian text to part-of-speech tag."New value: +"Estonian text."
```
- removedInput schema / properties / text / title
```
Removed value: -"Text"
```
- removedInput schema / title
```
Removed value: -"pos_tagArguments"
```
- removedOutput schema / properties / result / items / additionalProperties
```
Removed value: -true
```
- removedOutput schema / properties / result / title
```
Removed value: -"Result"
```
- removedOutput schema / title
```
Removed value: -"pos_tagOutput"
```
First observedv0.2.4Jul 5, 2026

TDQS

A3.5/5.0

Behavior4/5

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

Annotations already cover read-only, idempotent, closed-world behavior, so the description is free to add real semantics: a single most-likely reading is returned per word, punctuation is dropped, and edge cases (X verb-bound words, Z words of 40+ characters too long for Vabamorf) are disclosed. This goes meaningfully beyond the annotation set without contradicting it.

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 operational sentence is front-loaded and short, followed by an enum-key legend that is dense but earns its place since those tag codes would otherwise be opaque. No filler sentences.

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?

An output schema exists, so return structure need not be explained; the description supplies exactly the missing piece, the meaning of each tag code including the two special cases. The only gap is routing guidance against near-duplicate siblings.

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?

Only one parameter, fully described in the schema as 'Estonian text.' The description adds no detail about input format, length limits, or whether multi-sentence input is allowed, so baseline 3 applies with schema coverage at 100%.

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 opens with a specific verb+resource: 'Part-of-speech tag of each word', and qualifies it with 'most likely reading'. It does not name or contrast with the closest sibling, analyze_morphology, so an agent cannot tell from the text alone which of the two to reach for.

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

Usage Guidelines2/5

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

There is no statement of when to use this tool versus analyze_morphology, tokenize, or lemmatize, and no exclusions or prerequisites. Usage must be inferred entirely from the tool name and the tag legend.

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