Danish words starting with prefix (at least 3 characters), at most
max_results, as {"autocompletions": [...]} where lemmas are plain strings
and inflected forms of a matching lemma are [lemma, form] pairs.
| Name | Required | Description | Default |
|---|---|---|---|
| prefix | Yes | ||
| max_results | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -{
- "properties": {
- "result": {
- "title": "Result",
- "type": "string"
- }
- },
- "required": [
- "result"
- ],
- "title": "autocomplete_danish_wordOutput",
- "type": "object"
-}New value: +nullDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden. It successfully discloses the return format ({"autocompletions": [...]}) and the distinction between plain-string lemmas and [lemma, form] pairs, which is valuable. But it omits edge-case behavior such as what happens with prefixes shorter than 3 characters, empty results, or case sensitivity.
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 compact and every sentence earns its place, covering function, constraints, and return shape in a single dense block. It is not overly verbose, though the compressed inline JSON format could be slightly more readable with clearer separation of ideas.
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?
For a two-parameter tool with no output schema and no annotations, the description covers the core essentials: what it does, parameter meanings, result format, and lemma/form data structure. It is missing usage guidance and boundary-condition behavior (e.g., invalid prefix length), which leaves some gaps for an agent deciding how to call it correctly in unusual cases.
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 description coverage is 0%, so the description must compensate, and it does. It clarifies that 'prefix' is the starting substring (with a minimum length of 3 characters) and that 'max_results' caps the output size. It adds meaningful semantic weight to both parameters, though it does not specify whether counts include inflected forms.
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 states a specific verb and resource: returning Danish words starting with a given prefix, with a clear condition ('at least 3 characters') and an explicit result-shape definition. Its focus on prefix-based autocompletion and lemma/form pairs clearly distinguishes it from siblings that fetch definitions, entity info, word overviews, and synsets.
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 use case (autocompletion for Danish words starting with a prefix) is clearly implied, and the sibling names suggest it is the right choice for prefix-based lookups rather than exact definitions or entity queries. However, the description provides no explicit guidance on when to prefer this tool over alternatives or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are clearly distinct, but get_word_overview and get_word_synsets both retrieve senses for a given word, overlapping in purpose. Their descriptions clarify different output structures (custom summary vs. JSON-LD), but an agent could still be uncertain which to use for a given task.
Five of six tools follow a verb_noun pattern (autocomplete_danish_word, get_entity_info, get_word_overview, get_word_synsets, fetch_ddo_definition), and the shared 'get_word_' prefix for the overlapping tools is helpful. The exception is sparql_query, which is a noun phrase rather than a verb-led name, creating a minor inconsistency.
With six tools, the server is well-scoped for a wordnet/RDF resource. Each tool addresses a distinct need: prefix search, entity lookup by ID, word sense summaries, JSON-LD retrieval, external definitions, and arbitrary SPARQL queries. No tool feels redundant at a count level, even though two serve similar word sense retrieval.
The tool set covers the core use cases for a Danish wordnet: autocomplete, word sense lookup, detailed entity retrieval, external dictionary definitions, and flexible SPARQL querying. The SPARQL tool allows arbitrary queries, making the surface effectively exhaustive for read-only access. No obvious dead ends or missing lifecycle operations are apparent.