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

With no annotations, the description carries the full burden. It reveals key behaviors: auto-detection of distributions/polarity/units, auto-building objective function, and immediate simulation execution when run_simulation is true. However, it does not clarify side effects (e.g., persistence, reversibility) or authorization needs.

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 sentences with no wasted words. First sentence states core action, second lists capabilities, third handles multi-table behavior. Information is front-loaded and easy to scan.

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 description covers ingestion and simulation features but lacks output details. No output schema exists, and the description only says 'get results' without explaining what the return format includes (e.g., mapped payload, simulation results, error handling). This leaves ambiguity for the agent.

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 100%, so baseline is 3. The description adds meaning beyond the schema: 'run_simulation' executes immediately, 'domain' is a hint for mapping, and multiple tables are auto-joined. This provides helpful context for parameter 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's purpose: 'Auto-map tabular data to a simulation payload.' It lists specific actions like detecting distributions, polarity (revenue=positive, cost=negative), units, and building the objective function. This distinguishes it from siblings like 'simulate' or 'simulate_repository', which likely run simulations on pre-existing data rather than ingesting and preparing data.

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 through features like auto-detection and optional simulation execution, but it does not explicitly state when to use this tool versus alternatives. No 'when not to use' guidance is provided, leaving the agent to infer context.

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