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Get IP Context

get_ip_context

Combines RDAP, GreyNoise/AbuseIPDB reputation, and Loggly traffic into one IP profile, flagging cross-account activity for bot-traffic triage.

Instructions

Combines RDAP, GreyNoise/AbuseIPDB reputation, and Loggly traffic (1h/24h/30d counts, first/last seen, hosts, top paths — checked across every configured Loggly account unless account is given) into one normalized profile for an IP. Flags cross_domain_correlation when the IP shows activity in more than one account, which the bot-traffic-triage playbook treats as the single strongest escalation signal.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ipYes
accountNo
ip_fieldNo
host_fieldNo
path_fieldNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and discloses genuinely non-obvious behavior: it queries every configured Loggly account by default unless `account` is given, and it flags `cross_domain_correlation` when an IP appears in more than one account. It stops short of covering auth requirements, rate limits, or response structure, but the default-scope and flag semantics are valuable 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.

Conciseness4/5

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

Two sentences, front-loaded with the core purpose and the composite sources. The parenthetical detail on counts and fields is informative rather than redundant, and no sentence is wasted, though the second sentence is fairly dense.

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

Completeness3/5

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

For a composite lookup with no annotations and no output schema, the description adequately conveys what is returned and the key correlation flag, but leaves the field-override parameters and edge/error behavior unaddressed. It is minimum-viable rather than complete.

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

Parameters2/5

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

Schema description coverage is 0% across 5 parameters. The description clarifies only `account` ('unless `account` is given'); the field-override parameters `ip_field`, `host_field`, and `path_field` are never mentioned and remain entirely undocumented in both schema and description. The description does not compensate for the coverage gap.

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

Purpose4/5

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

States a specific verb and resource: it combines three named sources (RDAP, GreyNoise/AbuseIPDB reputation, Loggly traffic) into a 'normalized profile for an IP'. This clearly identifies it as the composite/aggregation tool relative to narrower siblings like rdap_lookup, ip_reputation, and traffic_by_ip, though it never explicitly names those alternatives.

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

Usage Guidelines3/5

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

Usage is only implied: the description ties the tool to the 'bot-traffic-triage playbook' and flags the strongest escalation signal, which gives a context of use. It does not state when to prefer this over the sibling lookups or any when-not condition, so guidance remains inferential.

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