merge_dataframes

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

With no annotations provided, the description carries the full burden. It discloses a key behavior (result stored as a new dataframe) and warns about join key verification, but does not describe error handling, duplicate key behavior, the default 'inner' already in the schema, or side effects on input dataframes. It adds some transparency but leaves gaps.

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?

All five sentences are purposeful and front-loaded with the core action. There is no redundant phrasing, and the descriptive content efficiently covers the operation, parameters, and usage prerequisite.

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?

The description covers the core operation, usage context, and a key prerequisite. It does not mention parameter-specific details like how multiple join keys are handled or the default behavior when result_name is omitted, but with no schema descriptions and no annotations, it provides a usable baseline. The complexity of SQL JOIN is moderate, and the description is sufficient for an agent to select and call the tool with minimal guidance.

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%, so the description must explain parameters. It explicitly lists valid values for 'how', implies 'left_name'/'right_name' via 'two dataframes', implies 'on' via 'join keys', and implies 'result_name' via 'stored as a new dataframe'. However, it does not explicitly map each parameter name to its meaning, so a slight deductive leap is needed.

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 uses a specific verb 'merge' and clarifies with 'like SQL JOIN', immediately distinguishing from concat_dataframes among siblings. The 'How' options further clarify the join types, making the purpose unambiguous.

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 provides an explicit use case ('when data is split across files') and a prerequisite ('verify join keys exist... with get_info first'). It does not explicitly state when not to use it or name alternative tools, but the SQL JOIN analogy implicitly differentiates from concat_dataframes.

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