list_ordersList this agent's recent UFP orders (newest first): order id, what was made, vendor, status, total, ETA, created date. Use it to resolve vague references — "my sticker order", "that order from 6 days ago" — to a concrete order_id before calling get_order_status or leave_review.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 ordering behavior (newest first) and the returned fields. It does not explicitly state it is read-only, but the action of listing without side effects is implied. This is sufficient for a list tool, though a note about no state change would have been more transparent.
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 long, front-loaded with the main purpose, and includes a concrete example of usage. Every sentence provides value without redundancy.
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 no input parameters and no output schema, the description covers the essential return fields, ordering, and a practical use case. It also differentiates from sibling tools, making the context complete for an agent to select and invoke correctly.
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 tool has zero parameters, and the schema coverage is 100% by default. Per rubric, a baseline of 4 is appropriate since there are no parameters to explain and the description adds no param-specific semantics needed.
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 lists the agent's recent UFP orders with a specific set of fields and sorting (newest first). It also explicitly distinguishes its role from siblings by noting it resolves vague references before calling get_order_status or leave_review.
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 gives explicit when-to-use guidance: to resolve vague order references into a concrete order_id. It also names specific alternative tools (get_order_status, leave_review) and indicates this tool should be used before them, providing clear context and 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.
Each tool targets a distinct action in the fabrication workflow: quoting, refining, checkout, order status, reviews, etc. Even similar concepts like get_fabrication_quote and refine_quote are clearly separated by purpose (initial quote vs. modifying an existing one). Internal tools like poll_quote are explicitly marked and never confused with user-facing actions.
All tool names follow a consistent verb_noun pattern in snake_case (create_checkout, get_fabrication_quote, refine_quote, etc.). The verbs are specific and predictable (create, get, list, refine, reply), making the API easy to navigate.
With 10 tools, the server provides a focused but complete set for its purpose—ordering and managing physical fabrications. No tool seems superfluous, and each covers a necessary step in the user journey. This is within the ideal range of 3-15 tools.
The tool surface covers the full lifecycle: quoting, refining, checkout, order tracking, reviews, and vendor communication. Minor gaps exist (e.g., no explicit cancel or return flow), but these are likely handled outside the MCP or via the hosted platform. The main workflows are all supported without dead ends.