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OrtaMarco

domain-security-mcp-server

by OrtaMarco

DNSBL Blacklist Check

blacklist_check
Read-onlyIdempotent

Check if an IP or domain is listed on email DNS blocklists (DNSBLs) to detect blacklisting issues.

Instructions

Check whether an IPv4 address (or a domain's A records) appears on email DNS blocklists (DNSBLs). Only open-access lists are queried (SORBS, SpamCop, UCEPROTECT-1, DroneBL, s5h); Spamhaus and Barracuda refuse public-resolver queries and are excluded.

Args:

  • query (string): an IPv4 address or a domain.

  • response_format ('markdown' | 'json'): output format (default 'markdown').

Returns: { ips[], listedCount, checked, results[{ip, hits[{list, listed, reason}]}], note }.

Example: "Is 203.0.113.5 blacklisted?" -> blacklist_check(query="203.0.113.5").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesAn IPv4 address or a domain to check against DNSBLs.
response_formatNoOutput format: 'markdown' for a human-readable summary (default) or 'json' for the full structured payload.markdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
ipsYes
listedCountYes
checkedYes
resultsYes
noteYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations mark it as read-only, idempotent, and non-destructive, which the description implicitly supports. It adds value by detailing the return structure (ips, listedCount, etc.) and the specific lists queried, going beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is succinct and well-structured with sections for Args, Returns, and an Example. Every sentence is informative, with no redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with two parameters and clear annotations, the description is comprehensive. It covers purpose, usage, parameter details, and return structure. The presence of an output schema (inferred from context signals) further reduces the need for extensive description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline 3. The description adds meaning by clarifying query accepts IPv4 or domain (not just IP), describing response_format options with default, and providing an example.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool checks IPv4 addresses or domains against email DNS blocklists (DNSBLs). It specifies the exact lists queried (SORBS, SpamCop, etc.) and excludes others (Spamhaus, Barracuda), distinguishing its scope from any potential sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains which DNSBLs are used and why some are excluded, giving context for when the tool is appropriate. However, it doesn't explicitly mention alternatives for excluded lists or when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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