process_dataframe

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

With no annotations provided, the description carries the full burden. It mentions in-browser processing and speed, but does not disclose data privacy, limits, error handling, or whether data stays local. Moderate transparency.

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?

Two sentences are efficient and front-loaded with the core purpose. Every sentence adds value: first sentence states technology and scope, second lists capabilities. No extraneous information.

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 3-parameter tool with no output schema, the description covers functionality, technology, and supported operations. However, it lacks details on error handling, performance bounds, or return format (though output_format param exists). Mostly complete.

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. The description adds context (CSV/TSV processing) but does not significantly expand on parameter meanings beyond the schema. The query parameter is already well-described in the schema.

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 processes CSV/TSV data using a Rust-based Polars engine in-browser with SQL support, which is a specific verb-resource combination. It distinguishes itself from unrelated siblings like disassemble_binary or optimize_images.

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 tabular data processing and lists supported operations (filtering, sorting, aggregation, JOIN), but does not explicitly state when not to use it or mention alternatives. Context is clear given sibling tools are unrelated.

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