get_table_schema

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

Annotations provide readOnlyHint=true, but the description adds valuable context beyond this: it specifies the return format in detail (including success flag, table metadata, columns list with name/type/description), mentions permissions requirement ('Permissions:{'all_of': ['Query Data Lake']}'), and explains limitations (e.g., nested structures only shown as 'object' type). No contradiction with annotations.

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 well-structured with clear sections (purpose, important notes, workflow example, return format, permissions). While slightly longer due to the detailed example, every sentence adds value—no wasted words. It could be slightly more concise but remains highly efficient.

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 the tool's complexity (metadata retrieval with permissions), rich annotations (readOnlyHint), and detailed output schema (implied by the return format description), the description is complete. It covers purpose, usage guidelines, behavioral context, permissions, and integration with other tools, leaving no gaps for agent understanding.

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%, providing clear documentation for both parameters. The description doesn't add additional parameter semantics beyond what's in the schema, but it contextually explains how parameters fit into the workflow (e.g., using 'panther_logs.public' and 'aws_cloudtrail' in the example). Baseline 3 is appropriate given high schema coverage.

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 specific action ('Get column details') and resource ('for a specific data lake table'), distinguishing it from sibling tools like 'list_database_tables' (which lists tables) or 'get_log_type_schema_details' (which provides log structure details). It precisely defines the tool's scope.

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 explicitly provides when to use this tool ('to get column names/types') and when to use an alternative ('For writing optimal queries, ALSO call get_panther_log_type_schema()'), including a detailed example workflow. It clearly differentiates this tool's role from complementary tools.

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