securitytrails_domainDomain intel (DNS, subdomain count) for . Returns current A/MX/NS records, subdomain count, and Alexa rank. Example: securitytrails_domain({ hostname: "github.com", _apiKey: "your-key" })
| Name | Required | Description | Default |
|---|---|---|---|
| _apiKey | Yes | SecurityTrails API key (get one free at securitytrails.com — 50 lookups/mo) | |
| hostname | Yes | Apex domain / hostname, e.g. "github.com" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, so the safe, read-only nature is clear. The description adds value by detailing the specific return data (DNS records, subdomain count, Alexa rank), which is not covered by annotations. No contradictions.
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 plus an example, with no unnecessary words. It front-loads the purpose and immediately provides a concrete usage example. Every sentence earns its place.
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 has only two parameters, no output schema, and clear annotations, the description fully covers what the agent needs: what it does, what it returns, and how to call it (example). No gaps remain.
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 100% with descriptions for 'hostname' and '_apiKey'. The description provides an example call but does not add new semantic meaning beyond the schema. 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 specifies the tool returns current A/MX/NS records, subdomain count, and Alexa rank for a given hostname. It clearly differentiates from sibling tools like securitytrails_dns_history (historical DNS) and securitytrails_subdomains (listing subdomains) by focusing on current domain intel.
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 use for current DNS and ranking data, but does not explicitly state when to prefer this over siblings (e.g., history vs. current; subdomains vs. overview). Agent must infer from sibling names. No explicit when-not or alternatives mentioned.
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.
Multiple tools occupy the same general query/research space: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, deep_research, validate_claim, entity_profile, and recent_changes all overlap in what they can return. The descriptions are detailed and try to steer usage, but the boundaries are fuzzy enough that agents can easily select the wrong tool.
All names are snake_case and descriptive, but there is no consistent verb_noun convention across the set. It mixes bare verbs (remember, recall, forget), noun phrases (entity_profile, recent_changes), domain-prefixed families (securitytrails_*, polymarket_*), and Pipeworx meta-tools (ask_pipeworx_*), so the pattern is predictable only within each family.
35 tools is above the 25+ threshold for a coherent MCP surface, and many are highly specialized (Polymarket arbitrage, AI visibility checks, npm dependency scans) rather than core Securitytrails functionality. The set feels like multiple products merged into one rather than a well-scoped toolset.
The broad data-research workflows are well covered: routing, entity resolution, profiling, comparison, validation, subscriptions, memory, and basic Securitytrails domain lookups. But for a server named Securitytrails, there are obvious missing security-intelligence operations such as associated domains, IP/certificate enrichment, and broader DNS infrastructure enumeration.