Wikipedia summary — Concise summary/extract for any Wikipedia topic, with the canonical URL. Source: Wikimedia REST API. JSON. Price: $0.003 USDC (Base, via x402).
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | article title or search term |
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 establish read-only, open-world, non-destructive behavior. The description adds valuable context beyond annotations: it uses the Wikimedia REST API, returns JSON, includes the canonical URL, and incurs a specific price via x402. It does not describe edge cases like missing topics, but this is minor for a read-only tool.
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 compact, front-loaded with the core purpose, and includes only essential operational details: source, format, and price. No filler or redundant rephrasing of the schema.
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?
For a single-parameter, read-only tool with no output schema, the description explains what the agent gets back (summary/extract and canonical URL), the format (JSON), the source, and cost. It slightly underspecifies failure behavior and language scope, but overall it is complete enough for correct invocation.
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 fully documents 'topic' as 'article title or search term', so schema coverage is 100%. The description only restates that the tool covers 'any Wikipedia topic' without adding deeper semantic detail, so it meets the baseline but does not exceed it.
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's function: it returns a concise Wikipedia summary/extract plus the canonical URL. This distinguishes it from sibling tools like text_summarize, which handles general text, and other domain-specific 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 phrase 'any Wikipedia topic' gives clear context for when to use this tool, and the topic parameter accepts either an article title or search term. It does not explicitly name alternatives or exclusions, but the scope is unambiguous enough for an agent to route to it correctly.
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 are clearly separated by prefix and data source, but several bundled products overlap heavily: vehicle_deal_check vs vehicle_report, realestate_property_report vs realestate_site_risk, finance_company_360 vs finance_health_scan, and domain_due_diligence vs email_domain_check/business_vet. An agent would frequently struggle to pick the correct premium bundle.
Tool names overwhelmingly follow a consistent snake_case category-prefix pattern like weather_, crypto_, vehicle_, finance_, and geo_. Minor deviations such as bare names (domain, ip) and noun-verb forms (dns_lookup, url_check) are easy to learn and don't create real confusion.
50 tools is far beyond the typical well-scoped 3–15 range and will require heavy filtering to navigate. The broad multi-domain data marketplace partially justifies the size, but it would be more coherent split into per-domain servers or consolidated further.
For a read-only data/diligence marketplace, the surface is quite comprehensive: weather, vehicle, crypto, SEC/finance, domain/email, sanctions, and geo workflows all have core operations plus fused verdict bundles. Minor gaps exist—such as a simple crypto price lookup or vehicle market value—but agents can usually work around them.