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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses critical behaviors: headers need not match, columns are unioned with blanks for missing columns, rows never shift silently, and row counts are reported and validated against the sum. This adds substantial value beyond the 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?

Three sentences, perfectly front-loaded with the main purpose, followed by behavioral detail and then use cases. Every sentence earns its place with no redundancy or filler.

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 tool with simple inputs (two optional params) and an output schema, the description covers the core functionality, edge cases (drifting headers), validation behavior, and typical use cases. It is complete enough for an 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?

The schema already covers both parameters (urls and texts) with descriptions, so the baseline is 3. The description doesn't add much parameter-specific detail beyond what the schema provides, though it reinforces the 'at least two' constraint for urls implicitly via 'up to 20 CSVs'.

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 combines up to 20 CSVs into a single table, with a specific verb and resource. It also differentiates from sibling tools like clean_table or extract_tables by emphasizing multi-file merging with column unioning and row count verification.

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 explicit use cases: 'monthly exports, per-store sheets, or any set of files with the same subject but drifting headers.' It does not explicitly mention when not to use it or alternatives, but the context is clear enough to guide selection.

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