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gnql-query

Search GreyNoise threat data using GNQL to retrieve full IP context including raw scan information and network details.

Instructions

Search GreyNoise data using GNQL (GreyNoise Query Language). Returns full IP context results including raw scan data.

GNQL is a domain-specific query language that uses Lucene deep under the hood.

Facets:

  • "ip" - The IP address of the scanning device

  • "classification" - Whether the device has been categorized as unknown, benign, or malicious

  • "first_seen" / "last_seen" - Date the device was first/most recently observed

  • "actor" - The benign actor the device has been associated with (Shodan, Censys, etc)

  • "tags" - Tags assigned to the device over the past 90 days

  • "cve" - CVEs associated with the device

  • "vpn" / "vpn_service" / "bot" / "tor" - Boolean/string indicators

  • "metadata.category" - Network category (business, isp, hosting, education, mobile)

  • "metadata.source_country" / "metadata.source_country_code" - Source location

  • "metadata.organization" / "metadata.asn" / "metadata.rdns" - Network info

  • "raw_data.scan.port" / "raw_data.scan.protocol" - Scan targets

  • "raw_data.web.paths" / "raw_data.web.useragents" - HTTP activity

  • "raw_data.ja3.fingerprint" / "raw_data.hassh.fingerprint" - TLS/SSH fingerprints

Examples:

  • "classification:malicious last_seen:1d" - Malicious IPs seen in last day

  • "tags:Mirai" - Devices tagged as Mirai

  • "raw_data.scan.port:445 metadata.os:Windows*" - Windows hosts scanning port 445

  • "cve:CVE-2021-30461" - Devices associated with a CVE

  • "source_country:Iran destination_country:Ukraine single_destination:true" - Targeted scanning

Results are paginated. Use the scroll parameter to retrieve additional pages.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesGNQL query string
sizeNoResults per page (default: 25, max: 10000)
scrollNoPagination scroll token from a previous response

Tool Definition Quality

A4.3/5.0
Behavior4/5

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

With no annotations, the description covers behavior adequately by stating it returns full results, uses pagination, and is read-only (implied). It does not mention rate limits or auth, but for a data query tool this is acceptable.

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?

The description is well-structured with a summary, facet list, examples, and pagination note. It is slightly verbose but every section contributes useful information, earning a high score.

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?

Given no output schema, the description could be more explicit about the response structure (fields returned). While it mentions 'full IP context results including raw scan data', a brief description of common response fields would improve completeness.

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

Parameters5/5

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

The schema covers all 3 parameters, and the description adds significant value: explains the query language, provides default and max for size, and clarifies scroll as a pagination token. Examples further illustrate query construction.

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 that the tool searches GreyNoise data using GNQL and returns full IP context results including raw scan data. This distinguishes it from sibling tools like gnql-stats (aggregate stats) or gnql-metadata-query (metadata only).

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 provides extensive examples of GNQL queries and explains pagination with the scroll parameter, giving clear usage context. However, it does not explicitly compare with sibling tools or advise when to use alternatives, though the purpose alone differentiates sufficiently.

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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