Check any domain (cataloged or not) for a v=AEO1 DNS record at _aeo., the open standard (aeorecord.org) declaring whether a verified fact record exists and at what trust tier. Verifies the Ed25519 sig= against the live catalog content when present.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully carries the burden. It discloses that it checks a DNS record and verifies the Ed25519 signature against live catalog content, implying a read-only network operation. It does not explicitly state there are no side effects or mention permissions, but the 'check' and 'verifies' wording provides strong behavioral context.
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?
Two sentences, front-loaded with the main action, and no filler. Every phrase adds useful detail: the DNS record type, the standard, the purpose, and the signature verification.
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 has a single parameter and no output schema. The description explains what it checks and what the result indicates (existence, trust tier, signature verification), which is sufficient for an AI to understand the tool's purpose. However, it does not specify the exact return format or error conditions, so it is 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 schema only defines 'domain' as a string with no description. The description adds meaning by showing how the domain is used ('at _aeo.<domain>') and what it is the target of the lookup. It does not specify format (e.g., no protocol, trailing dot), but the meaning is clear enough for typical usage.
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 states a specific verb ('Check') and a precise resource ('v=AEO1 DNS record at _aeo.<domain>'), clarifying it verifies existence and trust tier. This clearly distinguishes it from siblings like get_fact or check_serp.
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?
It explicitly notes 'any domain (cataloged or not)', giving clear usage context. However, it does not mention alternatives or when not to use this tool, so it lacks explicit exclusion guidance.
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.
Each tool targets a distinct resource or action: checking AEO records, reading SERP data, generating records, retrieving facts, citations, competitor snapshots, engine visibility, keyword gaps, etc. Even similar-sounding tools like get_citations and get_cited_domains are clearly delineated by their descriptions. No two tools appear to overlap in purpose.
All tool names follow a consistent verb_noun convention using snake_case: check_*, get_*, generate_*, propose_*, run_*, search_*. There is no mixing of casing or verb styles, and each name instantly communicates the action and subject.
With 14 tools, the server is well-scoped for the domain of AI citation optimization. Each tool contributes a unique capability, and the count is neither sparse nor bloated. The number fits comfortably within the ideal 3-15 range for a focused server.
The tool surface covers the core lifecycle: checking existing records, generating new records, auditing and fixing sites, retrieving data, analyzing gaps, and proposing briefs. Minor gaps exist such as no direct tool for claiming a domain or managing tracked keywords/competitors, but those may be external to the MCP server. Overall, the set supports the intended workflows well.