COMPOSITE (simulate x decide). Take an options-vs-scenarios decision and stress ONE scenario assumption across a range applied to every option; report how often the baseline winner survives (robustness) and where it flips. Same args as evaluate_options_with_scenarios plus 'stress': {variable, variation, steps}.
| Name | Required | Description | Default |
|---|---|---|---|
| inputs | No | ||
| method | No | ||
| stress | Yes | ||
| horizon | No | ||
| options | Yes | ||
| criteria | Yes | ||
| template | No | ||
| period_label | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the output (robustness and flips) and the composite nature, which is helpful. However, it does not explicitly state whether the tool is read-only, if it has side effects, or any limitations or prerequisites. The phrase 'report how often...' implies a non-mutating calculation, but this is not confirmed.
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 two sentences, starts with a clear category label, and packs key information: composite nature, operation, output, and parameter guidance. Every word earns its place; no filler or redundancy.
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?
For a complex composite tool with 8 parameters, no output schema, and no annotations, the description is incomplete. It explains the core behavior and the stress parameter but omits details about the other arguments, the exact output format, and any constraints. It relies on knowledge of evaluate_options_with_scenarios, which may not be assumed for a standalone description.
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 description must explain all parameters. It only details the 'stress' parameter structure ({variable, variation, steps}) and says 'Same args as evaluate_options_with_scenarios' for the rest. This indirect reference does not compensate for the remaining 7 undocumented parameters, such as options, criteria, and method, leaving a significant semantic gap.
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 opens with 'COMPOSITE (simulate x decide)' and clearly states the tool's function: stress one scenario assumption across all options and report robustness and flips. This distinguishes it from sibling tools like sim_sensitivity and decide, which are simpler or more focused. The verb 'stress' and resource 'options-vs-scenarios decision' provide a specific and unambiguous purpose.
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 when to use it: for an options-vs-scenarios decision where you want to stress a single assumption. It also references evaluate_options_with_scenarios as the base argument set, suggesting a shared use case. However, it does not explicitly state when not to use this tool or name alternative siblings for comparison, so it stops short of full guidelines.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clear, distinct purposes across three namespaces (calc_, decide_, sim_) plus composites. Some conceptual overlap exists (e.g., decide_sensitivity vs. sim_sensitivity, decide_score vs. decide), but descriptions clarify the boundaries well.
Names follow a consistent snake_case convention with a namespace prefix (calc_, decide_, sim_) and a descriptive verb_noun structure. Even composite tools and utilities like health_check and list_capabilities fit the pattern.
24 tools is on the heavier side, but it's justified for a meta-server exposing three distinct engines plus cross-domain composites. The count is appropriately scoped for the breadth of capabilities advertised.
The set covers all core domains with discovery (list_capabilities, *_list_*), health_check, and composite tools linking simulation to decision and valuation. Minor gaps include lack of a template management tool, but sim_run accepts free-form models, mitigating this.