list_ordersВозвращает заказы клиента с прогрессом по ссылкам. Ничего не изменяет.
| Name | Required | Description | Default |
|---|---|---|---|
| geo | No | all | |
| page | No | ||
| per_page | No |
| Name | Required | Description | Default |
|---|---|---|---|
| data | 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?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds context about the result including progress by links and reiterates that it changes nothing, complementing the annotations without contradicting them.
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 a single sentence stating the core function plus a short reassurance that it changes nothing. It is succinct, front-loaded, and contains no unnecessary wording.
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?
The description covers the core purpose and safety, and an output schema exists that presumably documents return values. However, it lacks explicit guidance on pagination (page/per_page) and differentiation from get_order, making it slightly incomplete for a list 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 0%, and the description does not explain the geo, page, or per_page parameters. It leaves their meaning to inference from names and defaults, which is insufficient for a tool with this many parameters.
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 customer orders with progress by links, using a specific verb ('returns') and resource ('customer orders'), distinguishing it from get_order or mutation tools like submit_order.
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 usage for retrieving a list of customer orders, but it does not explicitly discuss when to prefer this over alternatives like get_order, nor does it mention any exclusions or prerequisites. No explicit guidance is provided.
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 distinct purposes, but a few pairs (check_registration vs get_account_profile, submit_link vs submit_order) overlap in scope. Descriptions clarify the differences (single vs bulk, status vs full profile), so confusion is limited.
All tool names follow a consistent verb_noun pattern in snake_case (begin_registration, list_orders, update_link_draft). The only slight deviation is 'bulk_update_order_draft' with the 'bulk' modifier, but it still fits the overall pattern.
14 tools is well within the ideal range for a specialized service covering registration, orders, links, tariffs, and checkout. Each tool serves a distinct function without unnecessary bloat.
The core workflow (register, browse tariffs, manage order drafts, submit links) is well covered. Minor gaps like lack of explicit order creation or deletion are likely handled externally or via the checkout process, so agents can still complete key tasks.