Use when the user asks about likely cost, ballpark pricing, or whether a task needs a custom quote.
| Name | Required | Description | Default |
|---|---|---|---|
| brand | No | ||
| postcode | No | ||
| item_type | No | ||
| wall_type | No | ||
| item_count | No | ||
| service_type | Yes | ||
| task_summary | Yes | ||
| preferred_date | No | ||
| photos_provided | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only says 'use when' and does not reveal what the tool actually does—whether it calculates an estimate, returns a range, requires any prerequisites, or is read-only. The agent must infer behavior from the name.
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, front-loaded sentence with no fluff. It is concise and easy to scan, but it sacrifices substance; however, as a brief usage trigger it is appropriately sized.
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 9 parameters, no output schema, and no annotations, a one-sentence usage trigger is wholly inadequate. The description omits critical context such as return format, required parameter semantics, and any side effects, making the tool poorly specified for an agent to 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?
Schema description coverage is 0%, and the description provides no information about any of the 9 parameters. It does not explain service_type, task_summary, or optional fields like wall_type or photos_provided, leaving the agent to guess based on parameter names only.
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 indicates the tool's intended use case, tying it to cost questions and ballpark pricing. It effectively distinguishes it from sibling tools like find_services or create_booking_request by focusing on quote estimates, though it relies on the tool name for the action verb.
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?
Explicitly states when to use: when the user asks about likely cost, ballpark pricing, or whether a task needs a custom quote. It does not mention alternatives or exclusions, but the trigger scenarios are clear enough to guide tool selection.
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.
Some tools overlap in purpose, such as check_availability vs get_customer_availability and the various verification entry points. Descriptions clarify the context of each, but an agent could still misselect without careful reading.
All tools follow a consistent verb_noun pattern with snake_case, e.g., check_availability, create_booking_request, get_booking_status. This makes the naming predictable and easy to navigate.
With 20 tools, the server is on the heavier side but the scope is broad, covering booking, customer verification, payments, and provider matching. The count feels slightly high but each tool serves a distinct step in the workflow.
The tool surface covers the full booking lifecycle from availability and quote to payment and final creation. Notable gaps include lack of update or cancel booking operations, but the core flow is well covered.