COMPUTE. Exact business/SaaS/finance metric: ltv, cac, ltv_cac_ratio, payback_period_months, contribution_margin, gross_margin, churn_rate, mrr_growth_rate, arr, break_even_units, nrr, grr, rule_of_40, magic_number. Rates/margins are decimals (0.05=5%). Call calc_list_metrics for schemas.
| Name | Required | Description | Default |
|---|---|---|---|
| metric | Yes | ||
| params | Yes | ||
| currency | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the decimal convention for rates/margins ('0.05=5%') and the 'Exact' computation guarantee, but it does not mention error handling, output format, or behavior for unsupported metrics, leaving notable gaps.
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: a front-loaded 'COMPUTE' command followed by a compact list of metrics and a pointer to calc_list_metrics. There is no fluff; every word serves a purpose.
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 (14 metrics, nested params object, no output schema, no annotations), the description provides the metric list and decimal convention but relies heavily on another tool (calc_list_metrics) for parameter schemas. It does not explain return values or edge cases, making it moderately complete but not self-contained.
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 compensate. It adds value by enumerating valid values for the 'metric' parameter, but does not describe the structure of the 'params' object or the 'currency' parameter. Pointing to calc_list_metrics for schemas is partial compensation, but the tool is not fully self-descriptive.
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?
Description starts with 'COMPUTE' and specifies 'Exact business/SaaS/finance metric', followed by an explicit enumeration of 14 metric names (ltv, cac, etc.). This clearly identifies both the verb and resource, and distinguishes it from sibling calc_* tools (e.g., calc_business_days, calc_irr).
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 explicitly lists all supported metric names, making the scope clear, and directs users to 'Call calc_list_metrics for schemas' to obtain parameter details. It does not explicitly say when not to use the tool versus other calc tools, but the metric list effectively bounds usage.
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.