DNS record lookup — Resolve any DNS record type (A, AAAA, MX, TXT, NS, CNAME, SOA, CAA) for a hostname via DNS-over-HTTPS. JSON. Price: $0.002 USDC (Base, via x402).
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | hostname e.g. example.com | |
| type | No | record type: A, AAAA, MX, TXT, NS, CNAME, SOA, CAA (default A) |
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=true and destructiveHint=false, and the description's 'Resolve' wording is consistent with those. The description adds useful behavioral context beyond the annotations: the DNS-over-HTTPS mechanism, the JSON response format, and the $0.002 cost per call via x402. It does not mention rate limits or error behavior, but the annotations reduce the burden.
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 definition is a single front-loaded sentence that states the action first, then enumerates record types, transport, output format, and price. Every clause carries distinct information with no redundant or filler wording.
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 simple read-only two-parameter tool, the description covers purpose, transport, supported types, and cost, making it workable. However, because there is no output schema, it does not specify the JSON response structure, and it also omits error behavior and rate limits. These gaps prevent a higher score.
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 description coverage is 100%, so both `name` and `type` are already documented in the input schema. The description repeats the supported record types but adds no additional syntax, formatting, or edge-case guidance for the parameters. With high schema coverage, the baseline of 3 is appropriate.
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 opens with 'DNS record lookup' and elaborates with a specific verb ('Resolve'), the resource ('any DNS record type for a hostname'), and a concrete list of supported record types. It also names the transport ('DNS-over-HTTPS'), making the tool's function unambiguous and distinct from sibling tools like 'domain' or 'ip'.
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 makes it evident that the tool is for retrieving DNS records, so an agent can infer when to use it. However, it provides no explicit guidance about when to prefer this tool over alternatives such as 'domain', 'ip', or 'url_check', and it gives no exclusions. This is implied usage rather than explicit routing.
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.