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legionultramax

Harris HawkEye MCP

cve_to_detection

Convert a CVE into ready-to-use detection queries for Sentinel, Splunk, and Sigma by fetching NVD data, mapping to MITRE ATT&CK techniques, and including hunting hypotheses, false positives, and response actions.

Instructions

Convert a CVE into actionable SIEM detection logic. Fetches CVE details from NVD, maps to MITRE ATT&CK techniques, and generates detection queries in KQL (Sentinel), Splunk SPL, and Sigma formats with threat hunting hypotheses, false positive considerations, and response actions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cve_idYesCVE identifier (e.g., CVE-2024-1234)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations present, the description carries the burden of behavioral disclosure, and it does a solid job: it reveals that the tool fetches external CVE data from NVD, maps it to ATT&CK, and produces multi-format detections with additional artifacts. It does not discuss output format beyond formats and components, or side effects, but the described behavior is clearly non-mutating.

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 dense but efficient: the primary purpose is front-loaded, and the subsequent clauses list the process and deliverables without filler. It is slightly long and could be broken into cleaner sentences, but every segment adds useful information.

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 complex generate-from-CVE tool with no output schema, the description covers the important bases: input, data sources, output formats, and auxiliary content such as false positives and response actions. It doesn't specify the exact response structure or failure modes, but enough is present for an agent to invoke it and interpret the result.

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

Parameters3/5

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

Schema description coverage is 100%, so the structural baseline applies: the schema already explains cve_id with an example. The description adds no parameter-specific semantics or syntax details, so it neither improves nor harms parameter understanding.

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 states a specific verb and resource: converts a CVE into SIEM detection logic. It goes beyond a simple lookup by naming the pipeline (NVD fetch, MITRE ATT&CK mapping) and concrete outputs (KQL, Splunk SPL, Sigma), which clearly distinguishes it from sibling tools like nvd_cve_lookup, list_by_cve, and the standalone Yara/Sigma converters.

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 use case is clear: when an agent has a CVE identifier and needs actionable detection content, this is the tool. It doesn't explicitly state exclusions or name alternatives such as nvd_cve_lookup for plain CVE metadata, so it falls just short of explicit routing guidance.

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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