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

Adds meaningful behavior beyond annotations: flags change no value, are visible to the owner in every overlay view, and return proposal ids. Also discloses the overlay rule (source rows never overwritten, reads default to source, with_overlay shows accepted edits with provenance) and an auth requirement (AUTARIO_API_KEY). It stops short of describing what happens on invalid row keys or duplicate flags, and the overlay paragraph mostly documents query_dataset rather than this tool.

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?

Front-loads the core action and effect, then fills in scope, return value, supported sources, and overlay behavior. It is somewhat long and devotes several sentences to overlay semantics that predominantly concern query_dataset, slightly diluting focus on invoking propose_flags.

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 a 3-parameter mutation tool with annotations but no output schema, the description covers the essentials: effect on data (none), return value (proposal ids), auth, supported sources, and overlay consequences. Remaining gaps (error behavior, duplicate handling, whether flags are visible to other users immediately) are minor.

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%, so the schema already documents dataset_id, rows, row_key, column, and reason. The description adds example reason strings ('negative spend', 'duplicate of another row', 'currency differs from the account') but does not clarify semantics like how the top-level reason overrides per-row reasons or where row_key comes from.

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 a specific verb and resource: flag rows or single cells in a dataset, with a reason. Distinguishes the action from editing (it changes no value) but never names or contrasts sibling tools like propose_corrections, report_data_issue, or propose_column, so an agent must infer which proposal tool to choose.

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 a clear when-to-use clause: use it when you find inconsistencies you should not fix yourself. It also mentions overlay views, implying downstream review. However, it never explicitly names the alternative tools (e.g. propose_corrections for actual fixes) or states exclusions, so routing still requires inference.

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