Get Doc Schema

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

Annotations already indicate the tool is read-only, idempotent, and non-destructive. The description adds behavioral insight by explaining that it returns the full schema in one call and details the output structure (tables with columns, types, formulas). This goes beyond the annotations without contradicting them.

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 relatively concise, using four sentences plus a bullet-style Args/Returns section. It front-loads the core functionality and then explains the value. The structure is clear but could be tightened slightly (e.g., integrating Args into the prose).

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?

Given the rich annotations and full schema coverage, the description provides sufficient context: it explains the tool's utility, the output structure, and its relationship to sibling tools. While no output schema is provided, the description outlines the return data sufficiently for an AI agent.

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 all parameters are described. The description repeats the param names and values (doc_id, response_format with 'markdown' or 'json'). It adds the default response_format behavior implicitly. This meets the baseline but does not significantly enrich the schema's existing description.

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 purpose: getting the full schema of a Coda document, including all tables, columns, types, and formulas in a single call. It explicitly differentiates from sibling tools like coda_list_tables and coda_list_columns by noting the equivalence of multiple calls, making the intent unambiguous.

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 clear usage context: it is equivalent to running multiple list commands and is especially useful before writing data or debugging formulas. While it doesn't explicitly state when not to use it or enumerate all alternatives, the comparison to sibling tools gives sufficient guidance for the AI to decide.

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