update_row

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

No annotations are provided, so the description carries full burden. It discloses behavioral traits: 'Supports partial column updates with change tracking' and 'Returns old/new values for updated columns.' This adds context on what the tool does (partial updates, tracking) and output behavior. However, it misses details like error handling, permissions, or side effects. The description does not contradict annotations (none provided).

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 appropriately sized and front-loaded: the first sentence states the core purpose, and subsequent sentences add key behavioral details. Every sentence earns its place with no waste. It is concise and well-structured for quick understanding.

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 complexity (mutation tool with 2 parameters), no annotations, and an output schema (implied by 'Has output schema: true'), the description is fairly complete. It covers purpose, behavior (partial updates, change tracking), and output (returns old/new values). The output schema likely details return values, so the description need not explain them. However, it could improve by addressing error cases or prerequisites.

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%, so the schema already documents both parameters ('row_index' and 'data') with descriptions. The description adds marginal value by implying 'data' is for column updates, but does not provide additional semantics beyond what the schema states (e.g., format details or examples). Baseline is 3 when schema does heavy lifting.

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: 'Update specific columns in row with selective updates.' This specifies the verb ('update'), resource ('columns in row'), and scope ('selective updates'). It distinguishes from siblings like 'set_cell_value' (single cell) and 'update_column' (entire column), though not explicitly. The purpose is specific but could be more explicit about sibling differentiation.

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 implies usage for partial column updates with change tracking, but does not explicitly state when to use this tool versus alternatives like 'set_cell_value' (for single cells) or 'update_column' (for entire columns). No exclusions or prerequisites are mentioned. Usage is implied from the context of selective updates, but lacks explicit guidance.

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