URL status & redirect check — HTTP status, final URL after redirects, content-type and key response headers for a URL — without downloading the body. For link vetting and uptime checks. JSON. Price: $0.003 USDC (Base, via x402).
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | http(s) URL to check |
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 indicate read-only, non-destructive behavior. The description adds meaningful behavioral context beyond that: it explicitly says the body is not downloaded, that redirects are followed to the final URL, and that the response is JSON. It also discloses the price, which is useful practical information.
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 and information-dense, with two sentences that front-load the purpose and immediately give the key outputs and constraints. Every clause adds value: what is checked, what is returned, what is not downloaded, the intended use, the response format, and the cost.
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 tool with one required parameter and no output schema, the description is complete enough to invoke and interpret: it lists the response fields, specifies JSON as the format, clarifies the non-downloading behavior, and states the cost. No critical behavioral or return-value information is missing for an agent to use this tool correctly.
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 already documents the single url parameter with 100% coverage. The description mentions 'http(s) URL' indirectly but adds no deeper semantic detail beyond what the schema provides, so the schema carries the weight and the baseline score 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 identifies the resource (a URL) and the check being performed, and it enumerates the specific outputs: HTTP status, final URL after redirects, content-type, and key response headers. It is distinguishable from sibling tools like dns_lookup or ip, though it does not explicitly name or contrast itself against a close sibling.
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 'For link vetting and uptime checks' gives a clear intended use case, so the usage context is implied but not fully explicit. There is no statement about when not to use it or which sibling tool might be better for a different URL-related task.
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.