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

Annotations already declare read-only, idempotent, and open-world hints. The description adds that it 'is not a live engine' and clarifies api_key handling on different endpoints, which are meaningful behavioral details beyond the annotations, especially regarding authentication requirements.

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?

Two concise paragraphs: the first defines the purpose, the second notes api_key endpoint differences. No filler, and the core purpose is front-loaded. It could be slightly more structured but remains efficient.

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?

Despite having an output schema, the description omits parameter semantics, input requirements (ranking_id is required but not stated), and usage context relative to siblings. Given the statistical complexity of CrowdBT, this is insufficient for an agent to invoke the tool correctly without additional investigation.

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 0%, and the description offers no explanation of ranking_id, max_iterations, min_votes_per_entity, or min_votes_per_annotator. It only mentions the model concept ('entity scores', 'reliability η'), leaving agents without any guidance on how to set these parameters correctly.

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?

States it performs a 'joint fit of entity scores + per-annotator reliability η' and explicitly identifies itself as a 'methodology audit, not a live engine.' This clearly defines the tool's scope and distinguishes it from live-ranking siblings like get_leaderboard, though it doesn't name any sibling explicitly.

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?

Provides context that it's for auditing methodology rather than live use, and notes api_key behavior per endpoint ('optional on the public endpoint; on /mcp/mcp a missing key is refused'). However, it doesn't specify when to choose this over related tools like aimrank_get_agreement or aimrank_get_plackett_luce, leaving the routing to inference.

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