parse_literature_csv_data

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

With no annotations provided, the description carries the full behavioral disclosure burden. It implies a read-only parse operation ('Membaca dan mem-parsing') and discloses the extracted columns and outputs. However, it omits failure modes (e.g., missing columns, malformed CSV), whether it mutates the workspace, and any side effects. It adds value beyond the bare name but leaves meaningful behavior undisclosed.

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 with no filler. The purpose, source, extraction columns, and outputs are packed efficiently into a compact description. The only minor inefficiency is that column extraction and output generation are listed in one long sentence, but nothing is redundant or wasted.

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 tool with no output schema and no annotations, the description reasonably explains the inputs (CSV from workspace) and outputs (table matrix, Harvard references). However, it leaves gaps around parameter usage (csv_content vs csv_filename), error handling, and the structure of the produced matrix/references. Moderate complexity tool that could benefit from a usage note and clearer parameter semantics.

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 description coverage is 0%, so the description must compensate. It references the default filename literature.csv, which clarifies csv_filename, but it never explains the csv_content parameter (when to pass raw CSV content vs relying on the workspace file, format expectations, or the interaction between the two parameters). Parameter names are somewhat self-explanatory, but the description adds little semantic depth beyond what the schema names convey.

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 states a specific verb (reads/parses), a clear resource (literature CSV files from workspace), the exact columns extracted (researcher, year, title, method, results, research gap), and the concrete outputs produced (literature review table matrix, Harvard reference list). This clearly distinguishes it from sibling tools that focus on proposals, compliance, or checklists.

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 notes the tool reads from workspace files (e.g., literature.csv), which gives mild context. However, it provides no explicit when-to-use guidance, no exclusions, and no mention of alternatives. An agent must infer when parsing a literature CSV is the right call versus using proposal-generation siblings.

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