Submit a task description and receive dollar quotes from multiple frontier AI models before any work is done. Returns the request id, quotes with prices in cents, and a recommendation.
| Name | Required | Description | Default |
|---|---|---|---|
| description | Yes | The task to be quoted |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are sparse (no read-only or destructive hints), so the description carries the burden. It discloses that the tool returns a request id, quotes, and recommendation, and that no work is done. However, it does not mention potential side effects like reserving resources or whether quotes are binding, leaving some transparency gaps.
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 efficient sentences, front-loaded with the core action and outcome. Every word earns its place with no redundancy or fluff.
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 the tool's simplicity (single parameter, no output schema, sparse annotations), the description adequately covers what it does and its return. It could optionally mention whether quotes are binding or how prices are determined, but the current completeness suffices for basic usage.
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 coverage is 100% with a single parameter 'description' documented as 'The task to be quoted'. The description adds no additional meaning beyond the schema, thus baseline 3 is appropriate.
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 submits a task description and receives dollar quotes from multiple frontier AI models. It specifies the return includes request id, quotes in cents, and a recommendation, making the purpose distinct from siblings like 'estimate_cost' or 'accept'.
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 indicates this is used 'before any work is done' to get quotes, implying a preparatory step. It does not explicitly exclude scenarios or mention alternatives, but the context of sibling tools and the precise description provide adequate guidance for when to use it.
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 serves a distinct purpose in the quote-to-deliverable workflow: initiating quotes, accepting, estimating costs, polling results, rerunning with quality guarantee, and checking spending. No overlapping functionality.
All tool names use lowercase with underscores for compound names (e.g., get_deliverable, estimate_cost). Single verbs like quote and spend are also consistent with the pattern.
With 6 tools, the server covers the essential operations for managing AI model quotes and deliverables without unnecessary complexity. The count is well-scoped for a focused service.
The lifecycle from quote to deliverable is well-covered, but a cancellation or rejection tool is missing, which could be needed if a user changes their mind after accepting a quote.