estonian-mcp

check_officialese

Read-onlyIdempotent

Detect Estonian kantseliit in non-legal prose and offer rewrite suggestions, with metrics for nominalisation, noun-verb ratio, impersonal voice, and clause-stacking.

Instructions

Flag Estonian kantseliit in reports, academic and business prose.

check_legalese is the legal-text sibling; use THIS one for anything that is not a statute or contract, where check_legalese finds nothing because its lexicon and length gate are tuned for legislation.

Returns issues (each with an Estonian rule_estonian label, an explanation and a concrete suggestion) plus metrics:

nominalisation: -mine verbal nouns per 100 words, each paired with the verb to use instead (hindamine → hindama)
noun_verb_ratio: nimisõnastiil density; over ~2.0 reads heavy
impersonal_voice: umbisikuline tegumood, correctly counted (negated impersonals included, ei/ära and attributive -tud participles excluded)
clause-stacking: 3+ subordinate-clause openers in one sentence, the 'mille käigus … ning …' pile-up that a word count alone misses
long-sentence: 25+ content words, calibrated for Estonian
officialese filler: omama → olema, kujutab endast → on, viidi läbi → tehti, X-i poolt tehtud → X-i tehtud

Heuristic and precision-first — no flags does not prove the text is plain. Input capped at 100,000 characters.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`text`	Yes	Estonian non-legal text (report, academic, business) to check for kantseliit / bureaucratic density.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`note`	No
`text`	No
`issues`	No
`metrics`	No
`summary_estonian`	No

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.5.6Aug 23, 2026

TDQS

A4.9/5.0

Behavior5/5

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

The description goes well beyond the annotations by stating the tool is heuristic and precision-first, that absence of flags does not prove plain language, and by detailing counting rules for each metric (e.g., negated impersonals included, attributive -tud participles excluded). It also discloses the 100,000-character input cap.

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 front-loaded with the core purpose, then a short sibling-disambiguation, then bulleted metric details. Every sentence and bullet adds distinct value; the structure makes a sizable amount of information easy to scan.

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 tool's combinatorial and metric-rich output, the description is remarkably complete: it enumerates the issue field structure, each computed metric with its interpretation, examples of filler substitutions, caveats, and the input limit. The presence of an output schema reduces pressure to describe return values, but the description still covers them.

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?

The schema already fully describes the single text parameter, so the baseline is high. The description adds further useful semantics by emphasizing non-legal prose, explaining the sibling exclusion, and stating the character limit, which are not visible in the schema.

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 opens with a specific verb and resource: 'Flag Estonian kantseliit in reports, academic and business prose.' It later names the sibling tool check_legalese and explicitly distinguishes it, making its identity unmistakable.

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 gives explicit when-to-use guidance: use this tool for anything that is not a statute or contract, and explains why check_legalese would fail there due to its legal-tuned lexicon and length gate. This is a clear condition plus a concrete alternative.

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