load_dataset

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

Annotations are absent, so the description must carry the behavioral disclosure. It does add useful context: the file is local, loaded from disk into a session on the user's machine, rather than a remote or code-level operation. It does not disclose side effects such as whether loading replaces an existing session, memory limits, or file-size constraints, which matters for a stateful load operation.

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?

Three short sentences, with the core action in the second sentence and the mandatory-use rule front-loaded. The first and third sentences are somewhat redundant in urgency but each adds a distinct fact: when to use and what not to use instead. Overall compact with little waste.

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?

With no annotations and 0% schema descriptions, the description is the only source of behavioral and usage context; it covers file location and supported formats. It omits semantics for the optional format and sample_rows parameters and any statement about session replacement, though an output schema does exist. It is adequate for a basic call but not complete for robust agent decision-making.

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 free-text description must compensate. It names supported formats (CSV, JSON, Excel, Parquet) and implies a filesystem path, which helps with the path and format parameters. It does not explain the format parameter's auto-detection behavior or the sample_rows parameter at all, so parameter guidance is incomplete.

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 concrete operation: loads a local dataset file from disk into a session, and it names supported file types. However, it also claims the tool is mandatory for analyzing, inspecting, or querying datasets, which blurs the line with sibling tools like get_dataset_info, preview_dataset, and describe_dataset. The core load action is clear, but the scope claim is somewhat overbroad.

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?

It explicitly states when to call it ('MUST be used whenever loading, analyzing, inspecting, or querying any dataset') and explicitly forbids alternatives ('ALWAYS call this tool instead of using bash, shell, or writing python code'). It does not name sibling tools as alternatives, but the alternative it does name is direct and relevant. The guidance is clear but does not explain sequencing relative to the analysis siblings.

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