get_lead_timeDelivery lead-time band for a product (weeks, purchase order to shipment) plus Entogo's company-wide lead-time and warranty commitments. Bands: catalogue EV chargers 4–8, medium manufactured equipment 12–18, large engineered-to-order systems 16–24 weeks; new UL / CSA certification can extend a band within a guaranteed 36-week worst case.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Product id / URL slug |
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?
The annotations already mark the tool as read-only, and the description adds meaningful behavioral context: it reveals exact lead-time bands, the purchase-order-to-shipment basis, certification-related extensions, and a guaranteed 36-week worst case. This goes well beyond what the schema or annotations provide, though it does not describe response formatting or error behavior.
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 compact and front-loaded with the core purpose, then adds useful concrete band values in the second sentence. It contains some dense detail but every part contributes to the agent's understanding of the output.
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?
With only one required parameter and no output schema, the description does a good job of explaining what the agent should expect: lead-time bands, company-wide commitments, and worst-case extension behavior. It is slightly incomplete regarding the exact return structure or unknown-id handling, but these are minor for such a focused lookup tool.
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 100% and the single id parameter is already described as a product id / URL slug. The description reinforces that the id refers to a product and that the result is lead-time related, but it does not add substantial new meaning beyond the schema.
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 returns a delivery lead-time band for a product, expressed in weeks, plus company-wide commitment information. It is specific about the resource and output, though it does not explicitly distinguish itself from sibling tools such as get_product or get_company_facts.
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 the tool should be used when a delivery lead-time estimate or warranty commitment is needed, and the product-focused scope is clear. However, it does not explicitly state when to use this tool instead of a sibling like get_product or get_company_facts, nor does it mention exclusions or alternatives.
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 target clearly distinct resources and actions, such as products, company facts, lead times, RFQ handoff, semantic search, and EV capacity estimation. The only mild overlap is get_lead_time versus the lead-time band already embedded in get_product, and search_knowledge can return passages that overlap with structured tools.
Every tool follows a consistent verb_noun snake_case pattern: estimate_, get_, list_, request_, and search_. There are no mixed naming conventions, generic verbs, or vague tool names.
Seven tools is a well-scoped count for a knowledge-and-estimation server. Each tool covers a distinct need without filler or redundant entries.
The tool set supports product discovery, detailed product lookup, company facts, lead times, quote handoff, semantic search, and EV charging estimation, covering core user journeys. A minor gap is that other configurators mentioned in the quote handoff, such as the transformer configurator, are only exposed as links rather than executable tools.