poll_quoteINTERNAL — the offer board calls this to refresh itself while vendors finish pricing. Never call this yourself. The board you were given is already final for its turn; do not reach for refine_quote to look at it again either, that paints a duplicate board.
| Name | Required | Description | Default |
|---|---|---|---|
| quote_id | Yes | Quote id the board is watching |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral disclosure. It does reveal that the tool is internal and that the board refreshes itself, and it warns that the board is already final. However, it does not specify what happens if the tool is called manually (errors, side effects, return value), leaving some ambiguity about actual runtime 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 two concise sentences that front-load the INTERNAL warning and immediately give actionable prohibitions. Every clause earns its place, with no filler or repeated schema details.
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 that this is an intentional internal-only tool with a single well-described parameter and no output schema, the description covers the essential context: what the tool is for, who calls it, and why the agent must not call it. It lacks explicit return/error details, but for a tool the agent is explicitly forbidden from invoking, the guardrails are sufficiently complete.
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 input schema fully describes quote_id as 'Quote id the board is watching' with 100% coverage, so the description adds no additional parameter semantics. The baseline of 3 applies because the schema already handles the parameter information.
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 that the tool is an internal callback used by the offer board to refresh itself while vendors finish pricing, which goes beyond the title. It also distinguishes itself from refine_quote, warning not to use that tool for re-inspection because it paints a duplicate board.
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?
It gives explicit, non-ambiguous usage rules: 'Never call this yourself' and explicitly warns against using refine_quote to look at the board again. It explains that the provided board is already final for its turn, so calling this tool is unnecessary.
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.