Definitions of an English word from Wiktionary, grouped by part of speech, with example sentences where Wiktionary gives them, plus the page link and the CC BY-SA attribution to show. Use for writing help, vocabulary, translation checks and word games. Costs $0.001 in USDC on Base per successful call, paid with x402.
| Name | Required | Description | Default |
|---|---|---|---|
| word | Yes | An English word |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds genuinely non-structured context: the $0.001 USDC-on-Base x402 payment per successful call and the CC BY-SA attribution obligation the caller must surface. It does not state failure modes or rate limits, so not a 5, but the payment and licensing disclosure is real value beyond annotations.
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?
One dense sentence covering return shape, then a use-case sentence and a pricing sentence. Front-loaded and mostly waste-free, though the use-case list is the least essential part.
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?
No output schema exists, and the description compensates by describing the return structure (definitions grouped by part of speech, examples, page link, attribution) plus cost. Only failure/edge behavior is absent, which is minor for a single-param lookup.
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?
Only one parameter (word), and schema description coverage is 100%, so the schema already carries the meaning. The description adds no constraints such as expected casing, single-word vs phrase, or language handling, so the baseline 3 applies.
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?
States a precise verb+resource+source: definitions of an English word from Wiktionary, grouped by part of speech, with examples, page link and attribution. An agent can distinguish this from siblings like wikipedia or ai_translate immediately.
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?
Lists use cases (writing help, vocabulary, translation checks, word games) which implies when to call it, but gives no explicit when-not and does not contrast with the nearby wikipedia or ai_translate tools. Usage is implied rather than routed.
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.