top_exampleReturn the single highest-quality usage example sentence for a word from Wordnik's corpus.
| Name | Required | Description | Default |
|---|---|---|---|
| word | Yes | ||
| useCanonical | No |
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | Example ID | |
| url | No | Source URL | |
| text | No | Example text | |
| year | No | Year of example | |
| title | No | Example source title |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "word": "zenith"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "id": {
+ "description": "Example ID",
+ "type": "number"
+ },
+ "text": {
+ "description": "Example text",
+ "type": "string"
+ },
+ "title": {
+ "description": "Example source title",
+ "type": "string"
+ },
+ "url": {
+ "description": "Source URL",
+ "type": "string"
+ },
+ "year": {
+ "description": "Year of example",
+ "type": "number"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnly, openWorld, idempotent, and non-destructive hints, so the description's additional claim of returning a single sentence adds minimal behavioral insight. However, it does not disclose error behavior or quality criteria.
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 a single sentence of 14 words, front-loading the key action and result. No extraneous information is present.
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?
Given the presence of an output schema (which likely explains return values) and annotations covering safety, the description is mostly complete for a simple tool. It could mention edge cases (e.g., word not found) but is generally adequate.
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?
With 0% schema description coverage, the description should explain parameters but does not. While 'word' is self-explanatory, 'useCanonical' is not described at all, leaving the agent guessing about its meaning.
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 clearly states the tool returns a single highest-quality usage example sentence for a word from Wordnik's corpus. It specifies the verb 'Return', the resource 'usage example sentence', and includes a qualifier 'single highest-quality', which distinguishes it from sibling tools like 'examples' that likely return multiple examples.
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 description implies usage for obtaining the top example but does not explicitly state when to use this versus alternatives (e.g., 'examples' for multiple). It also omits guidance on the optional 'useCanonical' parameter.
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 have clearly distinct purposes, especially between Wordnik and Pipeworx domains. However, some overlap exists among data query tools (e.g., ask_pipeworx vs deep_research) and company lookups (entity_profile vs compare_entities), but descriptions are detailed enough to differentiate them in most cases.
Naming conventions are mixed: some tools use snake_case (ai_visibility_check), others use descriptive phrases (ask_pipeworx_grounded), and some are single words (remember, recall). There is no uniform verb_noun pattern, though groups like polymarket_* and scan_* provide some consistency within their subsets.
With 42 tools, the server is overloaded. It combines two distinct services (Wordnik dictionary and Pipeworx data) into one set, making it feel like two servers merged. Many tools are niche (e.g., hyphenation, random_words), increasing count without clear benefit. A split would improve coherence.
The Wordnik coverage is thorough (definitions, examples, pronunciation, frequency, etc.), and Pipeworx covers a wide range of data sources with tools for basic lookups, comparisons, research, and subscriptions. Minor gaps exist (e.g., no update/delete for Wordnik data), but overall the surface is comprehensive for the intended use.