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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds genuine behavioral context beyond that: it specifies the '~1.2°' proximity rule for market filtering, explains the GeoJSON return structure, and clarifies that the tool returns ONLY routes touching the named metro. The mpp_pay/mpp_credential parameters are also transparently explained in the schema. No contradiction with 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: usage context first, then a concrete example, then the market-scoping behavior, then the return shape, then exclusions. Every sentence earns its place and the structure front-loads the most decision-relevant information. The return schema and param enums are not needlessly duplicated, though the description is fairly long and could be trimmed slightly.

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 read-only geodata query with six parameters, zero required fields, an output schema, and 100% schema coverage, the description is complete. It states the geographic scoping rule, names the sibling alternatives for the two most likely misconceptions, documents the payment flow, and describes the GeoJSON shape. An agent can select this tool, set parameters, and interpret the result without any additional tool discovery.

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 description coverage is 100%, so the schema already documents every parameter. The description nevertheless adds value by explaining the market filter's ~1.2° proximity behavior, giving concrete enum examples for carrier and route_type, and showing how route_type=longhaul pairs with market. It also covers the free-vs-paid semantics of mpp_pay. This exceeds the baseline-3 for fully covered schemas.

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 opens with a specific verb-resource pairing ('scoring a candidate site for fiber depth, mapping long-haul routes between metros, or assessing dark-fiber availability') and immediately distinguishes the tool from siblings by name ('Do NOT use ... use get_acility' and 'use analyze_site'). The worked example with carrier=Zayo route_type=longhaul further anchors what the tool returns and how it is invoked.

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 ('Use when scoring a candidate site for fiber depth, mapping long-haul routes...') and also explicit when-not-to-use guidance with named alternatives ('Do NOT use to count fiber providers at a single facility (use get_acility) or for IX interconnection-density scores (use analyze_site)'). It even includes a concrete example query and parameter mapping, leaving little to inference.

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