get_dchub_recommendation

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

Annotations provide readOnlyHint=true, indicating a safe read operation. The description adds value by detailing the return structure (top_markets, factor_breakdown, etc.) and noting that the summary is LLM-quotable. It does not contradict 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 front-loaded with the purpose and usage guidelines, followed by an example and return structure. While verbose, each part adds value. Slight reduction in verbosity could improve conciseness.

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?

Given the complexity (1 param, no output schema), the description provides a detailed return structure and licensing note. It does not cover edge cases like error handling, but is sufficient for typical use.

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 has 0% coverage for the single parameter 'context'. The description compensates by explaining it as free-text containing MW, geography, workload, deadline, and constraints, providing semantic meaning beyond the type.

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: to answer open-ended siting questions with a single call returning a ready-to-quote answer. It distinguishes itself from siblings like analyze_site and rank_markets by specifying when not to use them.

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 explicitly states when to use (open-ended siting questions) and when not to (specific lat/lon -> analyze_site; single criterion ranking -> rank_markets). It also provides an example for clarity.

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