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drewrukin

dtrack-mcp

by drewrukin

group_findings_by_alias

Deduplicate vulnerability findings by grouping those linked through aliases into clusters, each with a canonical ID and merge trace.

Instructions

Group findings by alias (transitive closure) — dedup CVE/GHSA/OSV.

Vulnerabilities reported under different ids (e.g. CVE-2024-X and GHSA-Y-Z) often refer to the same issue and are linked via DT's aliases. This tool runs union-find over that alias graph and returns one cluster per real issue. Each cluster carries a canonical id (CVE first, then GHSA, then OSV, then SNYK, then INTERNAL, then alphabetical), the full alias list, a merge_reason trace of the edges that joined the cluster, and every finding in the project belonging to it.

Same filters as list_findings. Pagination applies to groups, not to the findings inside them — a group always ships with all its findings intact. Sorted by highest CVSS score (v3 or v4) descending. Read-only.

include_details=True (v0.3) embeds title/description/references in every finding's vulnerability summary. The same description text repeats on each finding inside a group — acceptable tradeoff for a single-call triage flow.

Args: project_uuid: DT project UUID. suppressed: Include suppressed findings. analysis_states: Whitelist of analysis state strings. severities: Whitelist of severity strings. page: 1-based page of groups (not findings). page_size: Groups per page (max 500). include_details: If true, embed title/description/references in each finding's vulnerability summary (v0.3). Default false.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_uuidYes
suppressedNo
analysis_statesNo
severitiesNo
pageNo
page_sizeNo
include_detailsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description fully discloses behavior: union-find algorithm, canonical id priority, merge_reason trace, pagination on groups, sorting by CVSS, include_details behavior, and read-only status. It even warns about repeated descriptions in findings. This is comprehensive.

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 front-loaded with the core purpose, then logically covers clustering details, behavior, and parameter list. Every sentence adds value without redundancy. Length is appropriate for the tool's complexity.

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

Completeness5/5

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

Given 7 parameters, no annotations, and an existing output schema, the description explains output structure (canonical id, alias list, merge_reason, all findings), pagination, sorting, and the effect of include_details. This is sufficient for an AI agent to use the tool correctly.

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 description coverage is 0%, so the description must compensate. It lists all 7 parameters with explanations (e.g., 'page: 1-based page of groups (not findings)') and defaults. Some details like possible values for analysis_states are omitted, but the coverage is strong overall.

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 opens with 'Group findings by alias (transitive closure) — dedup CVE/GHSA/OSV,' which precisely states the tool's action and resource. It clearly distinguishes from sibling tools like list_findings by explaining the clustering over alias graph, making the purpose unambiguous.

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 when to use (deduplication via aliases) and notes 'Same filters as list_findings,' implying an alternative. It lacks explicit 'when not to use' or direct comparison to other sibling tools, but the context of deduplication is clear enough.

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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