get_table_schema

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

Since no annotations are provided, the description must disclose behavioral details. It does so by listing what the response includes (column list, data types, primary key indicators, descriptions, table metadata) and noting the case-sensitivity of the table name. It doesn't mention potential errors or permission requirements, but for a read-only schema lookup, the disclosure is reasonably thorough.

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 longer than average but well-structured with clear headings and bullet points. Each section (use cases, expected output, parameters, examples, best practices, next steps) contributes to usability. There is some minor redundancy with schema details, but the organization makes it easy to scan quickly.

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 tool with only 2 parameters, no output schema, and no annotations, the description is extremely comprehensive. It covers when to use the tool, what results to expect, parameter constraints, example queries, best practices, and follow-up steps. An agent has all the information needed to select and invoke this tool correctly.

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 coverage is 100%, so the input schema already fully documents both parameters with examples and uppercase requirements. The description's 'Required parameters' section and example queries reinforce this but add little new meaning beyond the schema. It provides contextual usage examples, but the baseline of 3 applies because the schema does the heavy lifting.

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 'Get detailed schema information for a specific table or view' with a specific verb and resource. It distinguishes itself from sibling tools like search_tables (which finds table names) and execute_query (which runs queries) by specifying that this tool retrieves schema details, and provides example queries that make 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 tool explicitly lists 'Use this tool when:' scenarios, 'Best practices' (including 'Use search_tables() first if you don't know the exact table name'), and 'Next steps' (suggesting execute_query()). This gives clear when-to-use guidance and names alternative tools, making it easy for an agent to decide between this and related tools.

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