Every sense of the Danish word in one call: a list with synset_id,
label, definition, lexfile, ontological_types, synonyms (words sharing the
synset) and hypernym ({synset_id, label} or null) per synset. Only synsets
where the word itself has a sense count, not multi-word expressions
containing it.
| Name | Required | Description | Default |
|---|---|---|---|
| word | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It does reveal a non-obvious behavior: it excludes multi-word expressions and only includes synsets where the word itself has a sense count. However, it does not mention whether the operation is read-only, potential empty results, or any error conditions. This is adequate but not thorough.
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 dense but every sentence contributes valuable detail. It front-loads the core purpose and return structure, then adds the important filtering rule. No filler or repetition.
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 single-parameter read-like tool with an output schema present, the description gives enough detail about the return payload and scoping for an agent to make a correct call. It lacks usage-context guidance and explicit read-only clarity, but these are partially covered by the detailed return structure and the lack of side-effect verbs.
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?
The schema has 0% coverage, but the description clarifies that the single parameter `word` is a Danish word, and implies it should be a lemma for which sense information is requested. This adds meaning beyond the bare schema field name 'Word', although it could be more explicit about expected format (e.g., inflected vs. base form).
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 ('get'), a precise resource (word overview), and enumerates the exact contents per synset (synset_id, label, definition, lexfile, ontological_types, synonyms, hypernym). It also clarifies a key scoping rule (only synsets where the word itself has a sense count), which makes the tool's purpose distinct from simply listing 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?
There is no mention of when to use this tool versus its siblings such as get_word_synsets or fetch_ddo_definition. The description explains what the tool returns but does not guide the agent on selecting it over alternatives or provide exclusions.
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.