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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds important behavioral context: it clarifies that the 'market' parameter filters routes by proximity (~1.2°), and it specifies the return format (GeoJSON FeatureCollection with properties). It also explains the MPP payment flow (challenge and token), which goes beyond the schema. Minor gap: no mention of rate limits or data freshness, but the description adds substantial value beyond annotations.

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 description is dense but well-organized, front-loading the core use case and example, then covering params, return format, and exclusions. It is longer than average but every sentence serves a purpose. Slight rambling in the MPP explanation, but the structure is logical and scannable.

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?

With a rich output schema present, the description doesn't need to explain return values in depth, but it does. It covers full parameter usage, the payment workflow, return format ready for Leaflet, and explicit alternatives. For a moderately complex tool with no required params, this is comprehensive and leaves no critical gaps for an agent to call it correctly.

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 100%, so baseline is 3. The description adds meaning for key parameters: it explains 'market' works with 'route_type=longhaul' to map backbones, and details the 'mpp_pay' and 'mpp_credential' payment steps (challenge, token, $0.50 per call). It clarifies 'carrier' can be omitted for all carriers and 'route_type' values are enumerated in prose. Since the description enriches the schema and clarifies the payment flow, it earns a 4.

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 clearly states the tool's purpose: scoring candidate sites for fiber depth, mapping long-haul routes, and assessing dark-fiber availability. It names specific verbs and resources (e.g., 'map long-haul routes', 'assess dark-fiber availability') and distinguishes it from siblings like get_facility and analyze_site. The example with 'Zayo long-haul routes through Northern Virginia' concretely illustrates usage.

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?

The description provides explicit when-to-use guidance (e.g., scoring fiber depth, mapping routes) and explicitly lists what not to do: 'Do NOT use to count fiber providers at a single facility (use get_facility) or for IX interconnection-density scores (use analyze_site).' This is a model of clear routing to alternatives.

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