preprocess_data

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

Annotations indicate the tool is not read-only, and the description confirms it preprocesses data (write operation). However, it does not disclose specifics about side effects, data modification, or reversibility beyond the listed steps.

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 concise (two sentences plus bullet args) and front-loads the purpose. However, the args list is minimal and could be more informative without increasing length.

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 tool's complexity (many nested parameters) and presence of output schema, the description lacks details on prerequisites, data requirements, or expected outcomes. It is too brief for complete understanding.

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?

The description adds minimal to parameter semantics: it labels data_id as Dataset ID and params as Preprocessing parameters with sensible defaults. Schema coverage is 0% for top-level parameters, and the description does not compensate by explaining their purpose or usage.

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 preprocesses spatial transcriptomics data with a list of steps (QC, normalization, HVGs, PCA, clustering, spatial neighbors). It distinguishes from sibling analysis tools by clearly indicating its role as a preprocessing step.

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 implies usage before analysis tasks, but does not explicitly state when to use or not use this tool, nor provide alternatives. The context from siblings partially clarifies, but no direct guidance.

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