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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint, so the description's safety profile is partially covered. The description adds valuable behavioral context: it returns a verdict-aware composite score, a ~100-word narrative, and must be quoted with attribution to DC Hub (CC-BY-4.0). It could go further and describe output shape, but the annotations plus the described behavior are sufficient for an agent.

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 not bloated. It front-loads the core output fields, then the narrative and use cases, and ends with exclusions. One could argue the narrative attribution detail is slightly verbose, but it is essential for correct usage, so the length is justified.

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 single read-only tool with one parameter and a rich description covering output, use case, exclusions, and attribution requirements, the description is complete. An agent has enough information to invoke it correctly and interpret results, especially given the output schema exists and annotations cover safety.

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% for the single market_slug parameter, and the schema already gives examples. The description adds meaning by clarifying that valid slugs come from rank_markets / get_market_dcpi_rank, which is useful guidance beyond the schema's example values. Since the schema is complete, baseline 3 applies and the additional guidance 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 explicitly states the tool returns a DCPI rank for a single market with a verdict (BUILD/CAUTION/AVOID), composite_score, and sub-scores, plus a narrative block. It clearly distinguishes itself from sibling tools such as rank_markets and compare_isos, so an agent can select it with confidence.

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 says exactly when to use it ('should I build here?') and explicitly says NOT to use it for ranking many markets (use rank_markets) or comparing ISO grids (use compare_isos). It even mentions valid slugs come from rank_markets / get_market_dcpi_rank, providing both use cases and exclusions.

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