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adog0822

@loxeai/mcp-server

by adog0822

Group findings into action items by blast radius

classify_blast_radius
Read-onlyIdempotent

Turn a list of security findings into deduplicated action items, then classify each by fix scope: account-wide, per-region, per-resource, or per-person. Reduces hundreds of findings to a manageable set of coordinated actions.

Instructions

Collapses a list of findings into deduplicated action items and classifies each by how widely the fix applies: account-wide (fix once), per-region (rollout), per-resource (each resource), or per-person (human coordination).

This is what turns '400 findings' into '9 action items across 400 resources'. Pass a batchId to classify a whole scan, or pass findings inline to classify results from elsewhere.

Pure and offline: no scan, no network.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
batchIdNoClassify every finding in this batch.
findingsNoClassify these findings instead of a batch. Ignored when batchId is given.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
groupsYes
byScopeYes
totalFindingsYes
totalActionItemsYes

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds concrete behavioral detail: it deduplicates, classifies by blast radius, and is 'pure and offline: no scan, no network.' No side effects or caveats are omitted.

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?

Three sentences, front-loaded with the core transformation, then invocation modes, then a property. No filler or redundant restatement of schema fields.

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?

With an output schema, rich annotations, and 100% parameter coverage, the description covers purpose, invocation, and constraints. The parameter precedence (findings ignored when batchId is given) is in the schema, so no gap remains.

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 coverage is 100%, so the baseline is 3. The description adds slight contextual flavor by linking batchId to 'whole scan' and findings to 'results from elsewhere,' but this mostly restates schema semantics rather than adding substantive parameter meaning.

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?

States a specific verb 'collapses' and resource 'findings' into 'deduplicated action items' with explicit classification categories. The phrase 'turns 400 findings into 9 action items across 400 resources' clearly differentiates it from sibling tools that list or scan findings.

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?

Clearly frames the tool as a post-processing step and offers two invocation modes: batchId for whole scans, findings inline for external results. It does not name sibling alternatives or state when not to use it, but the context is unambiguous.

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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