check_table_completeness

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the agent knows this is a read-only, potentially incomplete operation. The description adds that it returns percentages per field, which is useful context. However, it does not disclose behavioral details such as sampling methodology, impact of sample_size, or limitations like openWorldHint implying results may not cover all records.

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 a single, concise sentence that front-loads the primary action and output. It earns its place with no redundancy, making it highly efficient.

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?

Without an output schema, the description provides essential return information (percentage of non-empty values per field). It mentions the input type (ServiceNow table) and the analysis focus. However, it omits potential edge-case behavior or calculation details (e.g., how empty strings are treated), which would be valuable for full completeness. Still, it is sufficiently complete for a read-only analysis tool with strong schema coverage.

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%, so all parameters are already documented. The description does not add additional meaning beyond the schema; it does not explain how 'sample_size' or 'query' affect the analysis results. Baseline 3 is appropriate when the schema carries the full parameter documentation.

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 function: 'Analyze data quality and field completeness for a ServiceNow table — returns percentage of non-empty values per field'. It uses specific verbs ('Analyze', 'returns') and identifies the resource ('ServiceNow table'), distinguishing it from siblings like analyze_data_quality, get_table_record_count, and query_records.

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 implies usage for data quality analysis, but does not explicitly state when to use this tool versus alternatives. No mention of exclusions or comparison with other analysis tools like analyze_data_quality or query_records. Usage is implied by the purpose, but lacks direct guidance.

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