load_dataset

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

With no annotations, the description carries full burden and does disclose real behavior: the file is held in memory, restricted to an allowed root, and clean_numbers alters column types with concrete examples. It omits permissions/auth, size limits, and failure behavior for unsupported or out-of-root paths.

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 tight sentences, front-loaded with the core action and scope; the clean_numbers example is the only elaboration and it earns its place. No redundant or padded text.

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?

The explicit output schema means return values (dataset name and schema) need not be repeated. Still, with no annotations and 0% parameter coverage, the unexplained 'name' parameter and absent failure/permission behavior leave gaps for a load-and-retain-in-memory tool.

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 0%, so the description must compensate. It explains clean_numbers well with concrete examples ('1.234,50 €', '12%') and scopes path to the allowed root, but 'name' is never mentioned and path's expected format is left unclear.

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?

States a specific verb (load) and resource (CSV/TSV/Excel/JSON/Parquet files) plus a key trait (kept in memory under an allowed root). An agent can distinguish it from unload_dataset, but the description never names or contrasts siblings explicitly.

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?

Usage is only implied: you must load a dataset before describing/querying/profiling it. There is no explicit when-to-use, when-not-to-use, or named alternative guidance, so the agent has to infer the workflow position.

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