run-simulation

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

With no annotations, the description carries full burden. It discloses key behaviors: state reset, sequential events (factory addition, discovery, disruption, self-healing), and monthly output. However, it does not clarify whether state changes are persistent or if the simulation affects shared data.

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 a single sentence that covers the tool's action chain effectively. Though somewhat lengthy, it is front-loaded with the tool identity and essential outcomes. No redundant information.

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 explains the output briefly (monthly snapshots with score progression) but lacks detail on snapshot structure or whether output is an array. Given no output schema, this leaves ambiguity. Additionally, the state reset is mentioned without specifying scope or persistence.

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% with descriptions for both parameters. The tool description adds no new semantic detail beyond what the schema already provides (e.g., default months, disruption inclusion). Baseline 3 is appropriate.

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 explicitly states the tool's purpose: running a 12-month ecosystem evolution simulation, including specific actions like resetting state, adding factories, and returning monthly snapshots. This clearly distinguishes it from sibling tools focused on static data or specific metrics.

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 for simulating ecosystem evolution over time but does not provide explicit when-to-use or when-not-to-use guidance relative to siblings. An agent can infer the purpose but lacks direct comparison to alternatives.

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