frequencyReturn year-by-year corpus frequency counts for a word from Wordnik; optionally constrain to a startYear–endYear range.
| Name | Required | Description | Default |
|---|---|---|---|
| word | Yes | ||
| endYear | No | ||
| startYear | No | ||
| useCanonical | No |
| Name | Required | Description | Default |
|---|---|---|---|
| word | No | The word | |
| frequency | No | Frequency data by year | |
| totalCount | No | Total frequency count |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "word": "algorithm"
+ },
+ {
+ "endYear": 2020,
+ "startYear": 1990,
+ "word": "internet"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "frequency": {
+ "description": "Frequency data by year",
+ "items": {
+ "properties": {
+ "count": {
+ "description": "Usage count",
+ "type": "number"
+ },
+ "year": {
+ "description": "Year",
+ "type": "number"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "totalCount": {
+ "description": "Total frequency count",
+ "type": "number"
+ },
+ "word": {
+ "description": "The word",
+ "type": "string"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. Description adds source 'from Wordnik' and the specific output structure (year-by-year counts), which aligns with annotations and provides useful context beyond structured data.
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?
Single sentence, highly efficient, front-loaded with the core action and resource. No superfluous words.
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, the description does not need to detail return values. It adequately covers the main purpose and optional parameters. However, it could hint at the range of years or data source limitations.
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, description must compensate. It clarifies 'word' as the query term and the optional 'startYear–endYear' range, but does not explain the 'useCanonical' boolean parameter, leaving it ambiguous.
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?
Clear action verb 'Return' with specific resource 'year-by-year corpus frequency counts' from Wordnik. Uniquely distinguishes from siblings like definitions or pronunciations.
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?
Describes optional year range constraint but lacks explicit guidance on when to use or avoid this tool versus alternatives. No mention of prerequisites or preferred scenarios.
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.