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get_cyber_advisories

Read-only

Sourced cyber-defense advisories (free: headlines from the last 30 days; subscribers: full detail and history) affecting AI agent stacks (platforms, packages, frameworks, consumer bots). Read-only. Filter by date (YYYY-MM-DD) and/or component (e.g. 'litellm', 'railway', 'grok-bot', 'package:pypi/litellm').

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
sinceNo
componentNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, so safety is covered. The description earns credit by disclosing the tier-gating behavior (free = headlines from last 30 days; subscribers = full detail and history), which is a real result-shaping constraint not present in any structured field.

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?

A single front-loaded sentence that leads with purpose, then tiers, then read-only, then filters. Dense but every clause carries information; the parenthetical component examples are slightly bulky but justified given the 0% schema coverage.

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 read-only list tool with no output schema, the description covers purpose, filtering, tier-dependent return depth, and component syntax. The gap is 'limit' — no default, no hint that results are capped/paginated — so an agent cannot fully predict result volume.

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 coverage is 0%, so the description must carry the load. It supplies the date format for 'since' (matching the pattern) and, more valuably, concrete component syntax including the qualified form 'package:pypi/litellm' that the bare maxLength:80 schema does not convey. It says nothing about 'limit', leaving one of three parameters undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('Sourced cyber-defense advisories') and scopes it to AI agent stacks with concrete component categories. It is clear what the tool returns, though it never names sibling alternatives like search_advisories or get_advisory to sharpen the boundary.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explains the two filter axes (date and/or component) and that free vs. subscriber tiers change the depth of what comes back, which implies when it is useful. However, there is no explicit when-to-use-this-vs-search_advisories/get_advisory guidance, leaving the agent to infer routing.

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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