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compare_providers

Translate an architecture specification across cloud providers to evaluate portability and service equivalence. Returns alternative architectures for each target provider with components and connections.

Instructions

Compare an architecture's service mapping across cloud providers.

Returns one translated ArchSpec per target provider, showing which services the original would become on each. Use this to understand architectural portability and equivalent services.

When to use vs compare_provider_costs: This tool returns full alternative architectures (with components, connections, tiers). compare_provider_costs returns only numeric cost totals per provider — use that when you only care about the bill, not the shape.

Behavior: Calls an LLM to resolve ambiguous service mappings where the static equivalence table is insufficient. Does not deploy.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
spec_jsonYesArchSpec to translate across providers. Original provider's services are mapped to equivalents on each target provider using 22 cross-cloud equivalence pairs (e.g. ec2 <-> compute_engine <-> virtual_machines).
providersYesList of target provider slugs to compare against. Values: 'aws', 'gcp', 'azure', 'databricks'. Returns one result per target.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Discloses that the tool calls an LLM for ambiguous mappings and does not deploy anything. This is beyond basic annotations (none provided). However, it does not explicitly state side effects like whether it modifies data or is read-only, but the nature of comparison implies no mutation. Still, good transparency.

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?

Well-structured with clear sections: main purpose, when-to-use, behavior. Each sentence is informative and concise. No unnecessary text.

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?

Covers all necessary aspects: purpose, usage guidelines, behavioral traits, and parameter context. Output schema existence means return values need not be detailed. Complete for a comparison tool with complex input.

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 100% but description adds value: explains 'spec_json' is an ArchSpec, 'providers' are target slugs with examples, and mentions the equivalence pairs. This goes beyond schema fields.

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?

Clearly states the tool compares service mappings across cloud providers, returning translated ArchSpecs. The verb 'compare' and resource 'service mapping' are specific, and it distinguishes itself from the sibling 'compare_provider_costs' by detailing the type of output.

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?

Explicitly explains when to use this tool versus 'compare_provider_costs', stating this one returns full architectures while the other returns only cost totals. No alternative tool is left ambiguous.

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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