📂 Retrieving table entries…

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

Annotations already indicate read-only, non-destructive, idempotent. Description adds operational context: reads raw data frame, requires primary key and materialized graph. No contradictions; adds value beyond 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?

Six sentences, each serving a purpose: main action, differentiation, anti-pattern, usage advice, prerequisites. No fluff, information is front-loaded and well organized.

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 presence of output schema and full parameter coverage, description provides all necessary contextual completeness: prerequisites, usage restrictions, differentiation, and a concrete use case. Sibling tools are referenced appropriately.

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 baseline is 3. Description clarifies that 'ids' are primary keys and that the table must have a primary key, adding meaning beyond the schema's parameter types.

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?

Clearly states verb 'lookup' and resource 'rows in the raw data frame of a table for primary keys'. Distinguishes from 'inspect_table_files' by contrasting row query vs schema analysis. Purpose is specific and actionable.

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?

Explicitly contrasts with 'inspect_table_files', states when to use (look up detailed info about recommended items) and when not to (schema analysis). Also lists prerequisites: table must have a primary key and graph must be materialized.

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