get_table_data

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

Without annotations, the description carries the burden of behavioral disclosure. It clearly communicates a read-only operation by saying 'Return data' and enumerates the full return structure, but it does not mention side effects, error behavior, or file-related assumptions.

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 concise, front-loaded with a clear purpose, and uses a readable bulleted return format. The return-field list may overlap with the output schema, but it is still well organized and economical.

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 read tool with an output schema, the description is mostly adequate: it states the resource, key parameter usage, and return shape. The missing file_path semantics and lack of sibling differentiation leave some gaps for an agent selecting this tool.

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 compensate. It adds meaning to table_name by explaining it is a display name from list_tables, but the required file_path parameter is completely undocumented.

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 returns data from a named Excel table, identifying the specific resource and operation. It is distinct from sibling get_sheet_data by referring to 'named Excel table', though it does not explicitly differentiate from alternatives.

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 a workflow by saying table_name comes from list_tables, giving useful context for when this tool applies. However, it does not contrast with get_sheet_data or other siblings, so an agent receives no explicit selection guidance.

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