SIMULATE. Vary one or more scenario inputs and show the impact on a target output metric (one-at-a-time), with elasticity + most-influential ranking. Requires 'template' and 'variable' (or 'variables').
| Name | Required | Description | Default |
|---|---|---|---|
| max | No | ||
| min | No | ||
| steps | No | ||
| inputs | No | ||
| values | No | ||
| horizon | No | ||
| template | Yes | ||
| variable | No | ||
| variables | No | ||
| variation | No | ||
| period_label | No | ||
| target_metric | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the sensitivity analysis method (one-at-a-time) and the output (elasticity, ranking), but does not mention whether the operation is read-only, any side effects, or return format details.
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, front-loaded with 'SIMULATE', and every word adds value. It efficiently covers purpose, method, output, and key requirements without fluff.
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 (12 parameters, nested objects, no output schema, no annotations), a two-sentence description is insufficient for an agent to invoke it correctly. It lacks guidance on parameter combinations, value ranges, and expected result structure.
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 schema has 12 parameters with 0% description coverage. The description only clarifies the role of 'template' and 'variable'/'variables', leaving other parameters like min, max, steps, variation, and target_metric unexplained. This does not adequately compensate for the lack of schema descriptions.
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 function: varying scenario inputs to show impact on a target metric. It explicitly specifies 'one-at-a-time' and includes 'elasticity + most-influential ranking', which distinguishes it from sibling tools like sim_run or sim_compare.
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 guidance is implied rather than explicit. The description indicates it requires 'template' and 'variable' (or 'variables'), but does not mention when to prefer this tool over alternatives like decide_sensitivity or stress_test_decision, nor any exclusions.
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.