csv_to_markdown

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

Since no annotations are provided, the description carries the full burden. It discloses that the tool handles optional quoted fields, which is a useful behavioral detail beyond the schema. However, it does not mention edge cases like empty fields, newlines, or error handling, leaving some transparency gaps for a transformation tool.

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 core function. Every word adds value, 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 simple two-parameter converter with no output schema, the description adequately specifies input format (CSV with optional quoted fields) and output (GitHub-flavored markdown table). It is complete enough for the tool's complexity, though it could mention handling of delimiters beyond the schema default.

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 input schema covers both parameters (csv and delimiter) with 100% description coverage. The tool description does not add any additional semantic meaning beyond what the schema already provides, so it stays at the baseline for high schema coverage.

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: converting CSV text into a GitHub-flavored markdown table. It uses a specific verb ('convert') and names both the input (CSV text) and output (markdown table), distinguishing it from sibling conversion tools like json_to_ts.

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 clearly implies when to use this tool: whenever you have CSV text and want a markdown table. However, it does not explicitly mention alternatives or when not to use it, though no sibling tool appears to be a direct alternative.

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