get_work_text

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

With no annotations, the description fully discloses the trade-offs: bypasses mini reader, returns raw text, can be huge (150k+ words), and why it's an escape hatch. Also mentions the max_words parameter to mitigate context impact.

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 thorough and well-structured, front-loading the critical warning and recommendation. While slightly verbose, every part adds value, though a shorter alternative could be possible.

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?

Covers usage, parameters, and behavioral impact adequately. With an output schema present, the description need not detail return values. Minor gap: doesn't specify if text includes HTML or formatting.

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?

Despite 0% schema coverage, the description explains work_id as 'numeric AO3 work id' and max_words as 'cap the text to first N words, 0 = whole fic', with a suggestion to sample. This adds necessary context missing from 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 it returns the raw full text of a fic, and explicitly distinguishes it from read_works by contrasting structured reports vs raw text. The purpose is unambiguous and specific.

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?

Provides explicit when-to-use (exact wording needed, user asks to quote/close-read) and when-not-to-use (prefer read_works in almost every case), with reasoning about context burden and token waste.

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