query_table

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool filters tables but lacks details on permissions, error handling, performance implications, or what happens if the query fails. This is a significant gap for a tool with mutation-like filtering operations.

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 efficiently structured with a clear purpose statement, parameter explanations, and return information in three concise sentences. Every sentence adds value without redundancy, making it easy to parse and understand 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?

Given the tool's moderate complexity (2 parameters, no annotations, but with an output schema), the description is adequate but incomplete. It covers the basic operation and parameters but lacks behavioral context and usage guidelines. The output schema existence reduces the need to explain return values, but more guidance is needed for effective use.

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. It adds meaningful context by explaining that 'name' is the table name and 'query' uses pandas syntax with an example, clarifying semantics beyond the bare schema. However, it doesn't detail constraints like valid table names or query limitations.

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 as 'Filter a table using pandas query syntax,' which specifies the verb (filter) and resource (table). It distinguishes from siblings like 'list_tables' or 'show_table' by focusing on filtering, but doesn't explicitly differentiate from all alternatives like 'compare_cities' or 'analyze_correlation'.

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 no guidance on when to use this tool versus alternatives. It mentions pandas query syntax but doesn't specify scenarios where filtering is preferred over other operations like comparing or describing tables, leaving the agent to infer usage from context.

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