Table extraction

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

Beyond annotations, the description discloses that the tool costs credits from the account wallet, which is a meaningful side effect. It also mentions support for scanned documents and output normalization, adding value without contradicting the provided hints.

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 two concise sentences that front-load the core function before the cost note. Every word 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?

With no output schema and four optional parameters, the description covers the core value and cost, and the schema covers parameter semantics. However, the exact output shape (beyond 'headers and rows') and input combination expectations are left unspecified, making it adequate but not exhaustive.

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?

All four parameters have complete schema descriptions (100% coverage), so the description adds no parameter-specific meaning. It does not explain when to use text versus fileUrl versus fileBase64, but the schema already handles that, warranting the baseline score.

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: extracting every table from a document, including scanned ones, as clean headers and rows. This is specific to tables and distinguishes it from the many parserail_* siblings, though it does not explicitly name an alternative.

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 converting document tables into spreadsheet/DB-ready data, but provides no explicit guidance on when to choose this over sibling tools like parserail_extract or parserail_structure, nor any exclusions. The use case is clear but under-specified.

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