Reorder point = lead-time demand + safety stock. If on_hand is given, returns whether to reorder now and the days of cover remaining.
| Name | Required | Description | Default |
|---|---|---|---|
| on_hand | No | ||
| safety_stock | No | ||
| lead_time_days | Yes | ||
| avg_daily_sales | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description indicates a pure computation with conditional behavior. No annotations provided, so description carries burden. It does not disclose error handling, edge cases, or return type details, but the core behavior is clear.
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?
Two concise sentences, no redundancy, and front-loaded with the formula. Every word adds value.
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?
Tool is simple and compute-only, but lacks output schema. Description vaguely mentions return values without structure (e.g., boolean and number). For a 4-parameter tool with no annotations, more detail would improve completeness.
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%. The description mentions 'lead-time demand' and 'safety stock' but does not explicitly map 'avg_daily_sales' and 'lead_time_days' to lead-time demand, nor explain the 'safety_stock' parameter. Only 'on_hand' is partially explained.
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 computes a reorder point and conditionally returns reorder decision and days of cover based on on_hand. The verb 'returns' and the formula make the purpose unambiguous.
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?
No guidance on when to use this tool versus alternatives. While siblings are distinct (validations), the description does not provide usage context or 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.
Each tool targets a distinct type of validation or calculation (email domain, payout, reorder point, payment split, ABA routing, EORI, EU VAT, GTIN, IBAN, VAT rate). There is no overlap in purpose, and descriptions clarify the specific identifier or operation.
Most tool names use a verb_noun pattern (e.g., validate_aba_routing, check_email_domain), but some are noun phrases (payout_reconciliation, reorder_point, stripe_connect_split, vat_rate_by_country). While clearly descriptive, the verb-prefix inconsistency slightly reduces predictability.
With 10 tools, the server covers a focused set of commerce validation and calculation tasks without being too sparse or overwhelming. Each tool serves a clear, independent purpose.
The tool set covers key commerce validations (email, bank routing, IBAN, VAT, EORI, GTIN) plus useful calculations (payout, reorder, payment split, VAT rates). Minor gaps exist (e.g., phone/address/credit card validation, currency conversion), but the coverage is coherent for the stated domain.