randomRandom Wikipedia article summary — pick a random article from the encyclopedia. Use for "give me a random Wikipedia article", "surprise me", or seeding exploration. Returns the same shape as page_summary.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | ||
| project | No |
| 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 already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds behavioral context by noting the random selection and that the output matches page_summary, which goes beyond the annotations and helps set expectations.
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 consists of two sentences, front-loaded with the core action ('Random Wikipedia article summary'). Every sentence adds value: purpose, use cases, and output format, with no filler or redundancy.
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?
The tool is simple and the annotations plus mention of page_summary's shape cover safety and return format reasonably well. However, the optional lang/project parameters are not explained, and there is no mention of alternatives, creating a minor completeness gap. It is adequate but not fully complete.
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?
The input schema has two parameters (lang, project) with zero descriptions, and schema description coverage is 0%. The description does not mention these parameters at all, leaving their meaning and purpose undefined for the agent. This is a clear gap that is not compensated.
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 it picks a random Wikipedia article summary with a specific verb ('pick a random article') and identifies the resource (Wikipedia article). It distinguishes itself from sibling tools like page_summary or featured by emphasizing the random selection and the summary return shape.
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 explicitly lists use cases ('give me a random Wikipedia article', 'surprise me', or seeding exploration), which tells the agent when to invoke this tool. It does not mention alternatives or when not to use, but the provided context is strong enough for basic scenario selection.
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 set mixes several overlapping families—ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded, six Polymarket tools, ai_visibility_check/scan_competitor_ai_presence, and the memory tools—so an agent can easily misselect. Although many descriptions are rich, the tool boundaries are not distinct enough, and two tools are explicitly near-identical at present.
Names are all lowercase snake_case with some logical prefixes (page_*, polymarket_*, pipeworx_*), but conventions mix imperative verbs (remember, forget, subscribe), bare nouns/adjectives (random, featured, onthisday), and descriptive noun phrases (entity_profile, page_html). The pattern is readable but not consistent enough to predict tool names reliably.
42 tools is far beyond the well-scoped range, especially given that the server is labeled 'Wikimedia Rest' but most tools target Pipeworx data research, prediction markets, npm scanning, memory, and AI marketing. Many tools could be consolidated or split into separate servers.
For reading Wikipedia content the page_* tools are fairly complete, and the Pipeworx side covers ask/research/resolve/validate workflows. However, major gaps exist for a coherent user: no Wikipedia search/resolve-title tool, no article diff or edit workflow, and the 'Wikimedia Rest' server lacks any write or query surface matching its name; the overall domain is so diffuse that completeness is hard to assess.