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Blast Radius Analysis

blast_radius
Read-onlyIdempotent

Assess a CVE's blast radius across your AI agents and infrastructure. Retrieve risk score, affected servers/agents, exposed credentials, and tools to prioritize remediation.

Instructions

Look up the blast radius of a specific CVE across your AI agent setup.

    Reads the tenant's persisted control-plane finding evidence when it is
    available. Standalone MCP mode falls back to a local MCP configuration
    scan and labels that narrower source explicitly.

    Args:
        cve_id: The CVE identifier (e.g. "CVE-2024-1234" or "GHSA-xxxx").

    Returns:
        JSON with blast radius details including risk_score,
        affected_servers, affected_agents, exposed_credentials, and
        exposed_tools. Returns found=false if CVE not found.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cve_idYesCVE identifier to look up, e.g. 'CVE-2024-1234' or 'GHSA-xxxx'.
scan_idNoOptional persisted scan scope.
tenant_idNoTenant scope for persisted findings. Defaults to 'default'.default

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds valuable behavioral context: the fallback to a local MCP configuration scan, the explicit labeling of that narrower source, and the `found=false` return case. This goes beyond the annotations without contradicting them.

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 concise (~100 words), front-loaded with the primary action, and structured with Args and Returns sections. Every sentence earns its place: the two behavioral notes (persisted vs. local source) and the return format are all useful. No redundancy or fluff.

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?

For a read-only lookup tool with strong annotations, a 3-param schema at 100% coverage, and an output schema, this description is complete. It covers purpose, two distinct operational modes, parameter requirement (cve_id), and a not-found outcome. The output schema presumably handles return-field details, but the description already gives the key returning fields.

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 each parameter (cve_id, scan_id, tenant_id) has a description. The description's Args section only restates cve_id syntax ('e.g. CVE-2024-1234 or GHSA-xxxx') without adding new semantics. Per the calibration baseline, 3 is appropriate.

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 a specific verb+resource+scope: 'Look up the blast radius of a specific CVE across your AI agent setup.' It clearly distinguishes this from siblings like exposure_paths and intel_lookup by focusing on CVE-specific blast radius. The two data-source modes (persisted control-plane evidence vs. standalone local scan) are also explicitly described.

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 clear behavioral context: it reads persisted evidence when available and falls back to a local scan in standalone mode, labeling the narrower source explicitly. However, it does not explicitly name alternative tools or say when not to use this tool, so it stops short of a full when/when-not guideline.

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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