Convert Estonian text words to their dictionary forms using the most likely morphological analysis, with punctuation omitted.
Lemma (dictionary form) of each word, from its most likely analysis. Punctuation is omitted.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Estonian text. |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.9.0Input schema / properties / text / descriptionPrevious value: -"Estonian text to reduce to dictionary-form lemmas."New value: +"Estonian text."Input schema / properties / text / titleRemoved value: -"Text"Input schema / titleRemoved value: -"lemmatizeArguments"Output schema / properties / result / items / additionalPropertiesRemoved value: -trueOutput schema / properties / result / titleRemoved value: -"Result"Output schema / titleRemoved value: -"lemmatizeOutput"v0.2.4Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and closed-world behavior, so the safety profile is covered. The description adds genuinely behavioral detail beyond that: lemmas come from the single most likely analysis (a disambiguation policy) and punctuation is omitted from output, both of which affect how the agent interprets results.
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?
Two short sentences, zero filler, with the core output definition front-loaded ahead of the punctuation caveat. Nothing could be removed without losing information.
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?
With an output schema present, the description need not explain return values, and annotations cover the safety profile. What it supplies — per-word lemmas, most-likely-analysis selection, punctuation omission — is sufficient to call the tool correctly, though behavior on unknown or out-of-vocabulary words is unstated.
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% for the single `text` parameter ('Estonian text.'), so the baseline is 3. The description's 'of each word' hints that input may be multi-word text, but it adds no format or language guidance beyond what the schema already says.
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 names the concrete resource produced (the lemma/dictionary form) on a per-word basis and specifies the disambiguation rule ('most likely analysis'). It is clear what the tool returns, but it never names its siblings (analyze_morphology, tokenize, pos_tag), so the agent gets no in-description help distinguishing lemmatization from full morphological analysis.
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?
There is no statement of when to use this versus analyze_morphology, pos_tag, or tokenize, and no mention of prerequisites or excluded inputs. The agent must infer the use case from the noun phrase alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.