flag_contest_discrepancy

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

With no annotations, the description carries the full burden. It clearly discloses the non-modification of Contests and that it only records the finding. However, it does not mention whether the tool appends or overwrites existing flags, nor does it detail any return behavior beyond what an output schema might cover.

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 compact and well-structured. It opens with a one-sentence purpose, then gives context on when to use it, and closes with a critical behavioral note. Every sentence adds value without redundancy, making it easy to parse quickly.

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 tool's simplicity and the presence of an output schema (indicated in context), the description fully covers what an agent needs: purpose, invocation context, side-effect clarification, and a pointer to subsequent human steps. No significant gaps remain.

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%, with each parameter documented in the input schema. The main description does not add parameter-level meaning beyond the schema, so the baseline of 3 is appropriate. The schema's detailed example for discrepancies_json compensates well for any lack of description-level detail.

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 starts with a specific verb and resource: 'Flag a factual discrepancy found in CONTEST DATA during AI research.' It clearly distinguishes this tool from siblings like update_document or apply_migration_patch by stating it records findings to a separate collection and does not modify Contest data.

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 the trigger condition: when a chatbot discovers a concrete, verifiable error during AI research. It also provides an exclusion ('This tool does NOT modify the Contests collection') and points to human review via other pipeline tools (apply_migration_patch, etc.), making usage boundaries clear.

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