$0.01 via x402: Wikipedia knowledge lookup — article titles, extract snippets and canonical URLs for any topic. The cheap fact-grounding read agents make to reduce hallucination. Free upstream (Wikimedia).
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Topic / search query | |
| limit | No | Results 1-10 (default 3) | |
| x_payment | No |
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?
No annotations are provided, so the description carries the full burden. It discloses the cost ($0.01 via x402) and notes the upstream is free, which is valuable behavioral context. It also implies read-only by calling it a 'read' tool, but it does not explicitly state it is non-destructive or mention any rate limits, auth requirements, or error behavior. For a simple lookup, this is adequate but not comprehensive.
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 two sentences with zero waste. It front-loads the cost and purpose, then briefly states the output and value proposition. Every sentence earns its place, and it is appropriately sized for a simple lookup tool.
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 is a straightforward read operation with no output schema, the description adequately covers the key aspects: what it returns, the cost, and the use case. It does not describe the return format in detail, but it lists the outputs (titles, snippets, URLs). It also doesn't mention error handling or edge cases, but for a simple Wikipedia lookup with schema-documented limit, this is likely sufficient.
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 coverage is 67% (q and limit have descriptions; x_payment does not). The description does not add any parameter-specific meaning beyond what the schema already provides. It mentions outputs but not parameter semantics. Since the schema covers the main parameters well, the baseline of 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?
The description clearly states the tool does a Wikipedia knowledge lookup, returning article titles, extract snippets, and canonical URLs for any topic. The verb 'lookup' and resource 'Wikipedia' are specific. It implicitly distinguishes from web_search or tavily_search by focusing on Wikipedia and positioning as a fact-grounding read. However, it does not explicitly name sibling alternatives, so it doesn't fully separate itself from other search/lookup tools.
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 fact-grounding to reduce hallucination, which gives a clear use case. However, it does not explicitly state when not to use it or mention alternatives like web_search or tavily_search. The guidance is implied rather than explicit, leaving an agent to infer when Wikipedia is preferred over general web search.
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.
Many tools occupy the same conceptual space: web_scrape vs markdown_web_scraper, post_check vs brand_ai_visibility_check, llm_chat_completions vs post_api_v1_chat_completions, chain_transaction_status vs chain_confirmations, and connect_token vs token_security_check + dex_token_data. Descriptions help in places, but for an agent facing 92 tools these near-overlapping endpoints will frequently cause misselection.
Everything is snake_case, but the conventions diverge sharply: get_chain_* and chain_* coexist for the same RPC family, post_* names are HTTP-route artifacts, api_generate reverses noun_verb order, and many names are bare nouns rather than verb_noun. There is no predictable naming pattern an agent can rely on.
At 92 tools this is far beyond the range where an agent can keep the surface coherent, even for a store. The flat tool list mixes products, bundles, aliases, proxies and single-use verticals, so most of the count is noise for any given task. A catalog/search/payment model with fewer exposed tools would fit the storefront purpose better.
The server has impressive breadth and covers key storefront/market workflows: catalog, samples, credits, directory listing, notary, and the task lifecycle. But each domain is shallow: there is no chain transaction broadcast, no task update/cancel/dispute, no AI-visibility history, and many verticals are a single tool with no follow-on operation. The surface is broad but not deeply complete.