summaryFetch the Wiktionary REST page summary (title, extract, thumbnail) for a word on the specified language subdomain (default en).
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | ||
| word | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
Annotations (readOnlyHint, idempotentHint, destructiveHint false) already inform the agent of safe, idempotent behavior. The description adds that it fetches from the Wiktionary REST API with a default language subdomain, providing context beyond annotations. It doesn't mention rate limits or pagination, but given annotations, this is sufficient.
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 that front-loads the action ('Fetch the Wiktionary REST page summary') and includes parameters inline. Every word contributes meaning; there is no redundancy or filler.
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 tool's simplicity (2 parameters, no enums, output schema present) and thorough annotations, the description covers purpose, parameters, and default behavior. It enables correct selection and invocation without gaps.
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 has 0% description coverage, so the description must explain parameters. It clearly defines 'word' as the term to look up and 'lang' as the language subdomain (default en), adding semantic meaning that the schema (only names and types) lacks. This fully compensates for the lack of schema descriptions.
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 fetches a Wiktionary REST page summary (title, extract, thumbnail) for a word on a specified language subdomain, defaulting to English. This specificity distinguishes it from sibling tools like 'definition' or 'etymology' which likely provide more detailed or different content.
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 the tool is for fetching brief summaries from Wiktionary but does not explicitly state when to use it over alternatives like 'definition' or 'etymology'. There is no mention of when not to use it or any prerequisites.
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.
The toolset is overwhelmingly fragmented: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical entry points; polymarket_edges, polymarket_arbitrage, and polymarket_fill_risk heavily overlap; and ai_visibility_check vs scan_competitor_ai_presence cover the same task. The five actual Wiktionary tools are distinct but are lost among dozens of unrelated research and prediction-market tools, making selection highly ambiguous.
All names use snake_case and several logical prefixes (ask_pipeworx, polymarket_, pipeworx_) create local patterns. However, the naming mixes noun-style commands (definition, etymology, pronunciations, summary) with verb-style commands (search, remember, forget, validate_claim), and no consistent verb_noun convention carries across the whole set.
36 tools is already heavy, but the deeper problem is that only 5 tools actually belong to a Wiktionary server while 31 tools serve unrelated Pipeworx, Polymarket, memory, and marketing-audit functions. The count is wildly inappropriate for the declared server purpose.
The Wiktionary-relevant tools cover basic word lookup—search, summary, definition, etymology, pronunciations—but omit common dictionary operations like translations, usage examples, inflected forms, or random entries. The non-Wiktionary majority does not fill these gaps; it just makes the surface area incoherent and hard to reason about.