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by dadepo

ripe_expand_as_set

Expand RIPE AS-SET objects into concrete ASN numbers for network analysis, route filtering, and policy generation with configurable recursion depth.

Instructions

Efficiently expand AS-SET objects from the RIPE NCC database into concrete ASNs with configurable depth. This tool is specifically for the RIPE RIR (Europe/Middle East/Central Asia region). Use this instead of whois_query when you need ASNs from a RIPE AS-SET. CRITICAL: For 'top-level', 'direct', or 'immediate' members, use max_depth=1. For complete expansion, use max_depth=10+. Large AS-SETs like 'AS-RETN' have hundreds of nested AS-SETs - choose depth carefully to balance completeness vs speed. Automatically handles recursive expansion, deduplication, and cycle detection. Perfect for network analysis, route filtering, and policy generation for RIPE-managed AS-SETs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
setnameYesAS-SET name to recursively expand into concrete ASN numbers from RIPE database. Examples: 'AS-CLOUDFLARE', 'AS-GOOGLE', 'AS-RETN'. The tool will automatically resolve all nested AS-SETs and return a complete list of individual ASNs contained within the hierarchy from RIPE NCC records.
max_depthNoMaximum recursion depth for AS-SET expansion (1-20 levels, default: 10). IMPORTANT: Use depth=1 for 'top-level' or 'direct' members only. Use depth=2-3 for shallow analysis, depth=10 for complete expansion. Higher values provide more complete results but take much longer to process. For questions about 'immediate' or 'direct' members, always use depth=1.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: 'Automatically handles recursive expansion, deduplication, and cycle detection,' mentions performance characteristics ('balance completeness vs speed'), and specifies the geographic scope ('RIPE RIR (Europe/Middle East/Central Asia region)'). It doesn't cover error handling or authentication requirements, but provides substantial operational context.

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 well-structured and front-loaded with the core purpose. Every sentence adds value: the first states the purpose, the second specifies regional scope and alternative tool, the third provides critical depth guidance, the fourth explains handling of large AS-SETs, and the fifth describes automation features and use cases. It could be slightly more concise but remains efficient.

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 the tool's complexity (recursive expansion with depth control), the description provides excellent context. It covers purpose, regional scope, when to use vs alternatives, depth guidance, performance considerations, automation features, and use cases. With an output schema present, it doesn't need to explain return values, making this description complete for agent understanding.

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 schema already documents both parameters thoroughly. The description adds some practical context about depth usage ('For 'top-level', 'direct', or 'immediate' members, use max_depth=1') and performance implications, but doesn't provide significant additional semantic information beyond what's in the schema descriptions.

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 clearly states the tool's purpose: 'Efficiently expand AS-SET objects from the RIPE NCC database into concrete ASNs with configurable depth.' It specifies the verb (expand), resource (AS-SET objects), and distinguishes it from sibling tools by mentioning 'Use this instead of whois_query when you need ASNs from a RIPE AS-SET.'

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool vs alternatives: 'Use this instead of whois_query when you need ASNs from a RIPE AS-SET.' It also gives detailed context on depth selection for different scenarios (e.g., 'For 'top-level', 'direct', or 'immediate' members, use max_depth=1'), and mentions performance trade-offs for large AS-SETs.

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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