compare_agent_platforms

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

With no annotations, the description carries the full burden, and it does substantial work: it discloses the output's cost breakdown (hosting, tools, memory, model tokens), that the model is priced on each cloud's own service or marked unavailable, and that it reports a breakeven 'busy share' below which AgentCore wins. It does not state read-only/idempotency, assumptions about pricing region/currency, or whether results are cached, which keeps it from a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The cost-model explanation is front-loaded and dense with no filler, and the 'Use for' examples make intent skimmable. The single long opening sentence is heavy but each clause adds a distinct fact; some minor trimming of the quoted example phrases would help without losing information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 10-parameter, no-output-schema comparison tool, the description covers what is compared, how costs are decomposed, and the key breakeven output — enough for an agent to call it correctly. Gaps remain around defaults for the undocumented sizing parameters (vcpu, memory_gb, runs_per_month) and the output format/currency, which a reader must infer.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 60%, leaving vcpu, memory_gb, runs_per_month, and tool_calls_per_run undocumented in the schema, but the description compensates by framing the whole model: run sizing at vCPU/memory, per-run seconds including waiting, and the active-CPU share that drives the breakeven comparison. It also ties input/output token params to estimate_agent_cost's default 8-step run and explains that memory retrievals map to AgentCore Memory / Agent Engine Memory Bank.

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

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

The description names a specific verb-and-resource combination — pricing the same AI agent across four named hosting options (Bedrock AgentCore, Azure Foundry, Vertex Agent Engine, plain serverless). It distinguishes itself from neighbors by naming model tokens, memory, and tools as cost components, and from cost-estimation siblings by scoping to hosting/platform comparison.

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

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

It gives explicit trigger phrases ('AgentCore vs Foundry vs Vertex', 'Lambda or AgentCore', 'hosting 50,000 agent runs a month cost') and an explicit handoff rule: 'For token counts per run, use estimate_agent_cost first.' That is a clear when-to-use, when-not, and named alternative.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.