parse_csv_and_suggest

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

With no annotations, the description carries full burden. It clearly states the tool is read-only ('no data is written') and describes its analysis actions. However, it does not mention error handling for malformed CSV or specific behavior if schema inference fails, which would improve 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?

The description is extremely concise, consisting of two short sentences. The first covers purpose and actions, the second provides critical usage guidance. No unnecessary words.

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?

The description adequately covers purpose and usage but lacks details about the return format of the suggestions (e.g., structure of proposed fields, eligibility rules). Since there is no output schema, the description could be more complete about what the agent can expect from the tool's output.

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 single parameter 'csv_text' has 100% schema description coverage, so the schema already fully defines it. The description adds no additional semantic meaning beyond what the schema provides, resulting in a baseline score of 3.

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 specific actions: parse CSV, infer XDM types, and suggest schema fields, eligibility rules, and ranking formulas. It also explicitly distinguishes itself from sibling tools by stating 'no data is written,' making it clear as a read-only analysis tool.

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 explicitly instructs 'Always call this first — no data is written,' providing clear guidance on when to use this tool (before any writing operations) and implying it is a prerequisite step, effectively differentiating from sibling tools that create or modify data.

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