DC Hub Recommendation

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, and the description adds behavioral context: it synthesizes multiple factors into a composite score, returns an LLM-quotable summary with CC-BY-4.0 license, and includes a citation URL. This gives the agent confidence about the tool's safety profile and output characteristics beyond the structured 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 concise despite its length, front-loading the primary use case and example before detailing the response structure and exclusions. Every sentence provides actionable information—use case, example, parameter, return shape, and alternatives—with no filler.

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?

The tool is complex (single input, rich output with multiple nested fields), yet the description covers the key aspects: when to use, what to pass, what to expect, and exclusions. The presence of an output schema means return values are further specified, but the description already outlines the main fields and licensing, making it complete for an agent.

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?

The schema already provides a thorough description and example for the 'context' parameter, and the tool description's parameter section largely mirrors it. While the description does frame the parameter within the tool's usage, it does not add significant new semantic meaning beyond what the schema provides, so a baseline of 3 is appropriate.

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 explicitly states the tool's purpose: returning a ready-to-quote answer for open-ended siting questions, and differentiates it from siblings like analyze_site and rank_markets. It names specific output components (top_markets with verdicts, candidate_facilities, factor_breakdown) and includes a concrete example, making the tool's scope unmistakable.

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... open-ended siting question') and when-not-to-use with named alternatives ('Do NOT use for a single specific lat/lon (use analyze_site) or to rank by ONE criterion only (use rank_markets)'). This goes beyond typical descriptions by offering exclusion criteria.

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