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

Annotations already cover read-only and non-destructive behavior. The description adds useful context by clarifying that the output is metadata (column definitions) rather than table data, and that a usage note with required filters, ticker format, and market coverage may be included where one exists.

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 a single, front-loaded sentence that states the core return value first and the optional usage-note detail second. There is no filler, redundancy, or repeated schema information.

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 one-parameter, read-only metadata tool with no output schema, the description explains the return value sufficiently: column names, types, descriptions, and optional usage notes. The enum schema covers valid inputs, so nothing critical is missing for correct invocation.

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 single parameter table_name is self-explanatory and the enum exhaustively enumerates valid table names, so the agent can invoke the tool correctly despite 0% schema description coverage. However, the description itself adds little semantic detail about the parameter beyond referring to 'a data table'.

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 that the tool returns column definitions (name, type, description) for a data table, plus an optional usage note. It names a specific resource and output, and the behavior is distinct from siblings like list_tables or run_sql.

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 it is the tool for inspecting table metadata, but it never explicitly states when to use it instead of alternatives like list_tables or run_sql. The usage context is inferable from context but not made explicit.

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