get_table_sample

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

Annotations already mark the tool as readOnlyHint=true and destructiveHint=false. The description adds transparency by specifying 'small sample' and the default/max limit behavior, which is valuable beyond annotations. No contradictions detected.

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 two sentences, no redundant words. The first sentence front-loads the core purpose; the second adds usage and naming tips. Every sentence earns its place.

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 with two parameters and no output schema, the description covers the essential aspects: what it does, how to use it, and naming conventions. It is complete enough for an AI agent to select and invoke 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 description coverage is 100% (both parameters described). The description adds value by explaining that table_name can be bare (using MYSQL_DATABASE) or in database.table format, which goes beyond the schema's description. For limit, the schema already states default and max, so no further addition needed.

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 action ('Fetch a small sample'), the resource ('from a table'), and the purpose ('to understand its data format and content'). It distinguishes itself from sibling tools by mentioning alongside get_schema_info and before writing complex queries, implying this tool is for exploration, not execution or schema understanding.

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 advises using the tool alongside get_schema_info before writing complex queries, providing clear context for when to use it. It also explains naming conventions (bare table vs database.table). However, it lacks explicit guidance on when not to use it or comparison to execute_sql for arbitrary queries.

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