read_rows

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

With no annotations provided, the description carries the full burden. It effectively discloses key behaviors: it's a read operation (implied by 'read'), supports multiple query methods, returns a dictionary structure with table name, rows, and count, and includes a max_rows limit for result control. It doesn't mention permissions, rate limits, or error handling, but covers core functionality well.

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 and front-loaded: the first sentence states the purpose, followed by parameter guidance, a detailed Args section, and a Returns section. Every sentence adds value—no fluff. It efficiently covers complex functionality in a compact format.

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 (6 parameters, read operation), no annotations, and an output schema provided, the description is complete. It explains all parameters thoroughly, details the return structure, and provides usage examples. The output schema likely defines the return dictionary, so the description doesn't need to duplicate that, focusing instead on practical guidance.

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 fully. It does so excellently: it explains each parameter's purpose (e.g., 'table: Table name', 'filter: WHERE clause without WHERE keyword'), provides examples ('dbo.Users', "status = 'active'"), clarifies defaults ('columns: default: all columns'), and notes constraints ('for composite keys, use filter'). This adds substantial meaning beyond the bare schema.

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's purpose: 'Read rows from a table by primary key or filter.' It specifies the verb ('read'), resource ('rows from a table'), and mechanism ('by primary key or filter'), distinguishing it from siblings like execute_query (general queries) or describe_table (metadata).

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 provides clear usage guidance: 'Provide one of: id (single row), ids (multiple rows), or filter (WHERE clause).' This helps the agent choose between parameters. However, it doesn't explicitly contrast with alternatives like execute_query for complex queries or list_tables for metadata, leaving some sibling differentiation implicit.

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