get_tail

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

With no annotations, the description must carry the burden of behavioral disclosure. It reveals the output format ('formatted table') and implies a non-mutating operation by using 'Return.' It does not explain the effect of the df_name parameter (e.g., whether it operates on the current dataframe or a named one) or any potential side effects, leaving some behavioral 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?

The description is fairly concise and front-loaded with the primary purpose. The second sentence 'Check the last rows.' is somewhat redundant, but the example adds clarity without being overly verbose.

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?

For a simple tool, the description covers purpose, usage context, and an example. However, it omits details about df_name, which is a parameter with no schema description, and does not clarify what 'formatted table' entails beyond the output schema hint. This leaves some gaps in completeness.

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?

The schema has 0% description coverage, so the description must compensate. It explains the 'n' parameter through the example 'get_tail(n=10)' and the phrase 'last N rows,' but the df_name parameter is not mentioned at all. This leaves half the parameters ambiguous.

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 function: 'Return the last N rows of the dataframe as a formatted table.' It uses a specific verb ('return'), specifies the resource ('last N rows of the dataframe'), and describes the output format. It also differentiates from siblings like get_head by focusing on the last rows and mentions time-series utility.

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 a clear usage context: 'Useful for time-series to see the most recent data.' This tells the agent when to consider this tool. However, it does not explicitly mention alternatives or when-not-to-use, so it lacks full exclusion guidance.

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