Find businesses that offer a specific service type. Useful when you know the exact service rather than the category — e.g. "Drain cleaning" rather than "plumbing".
| Name | Required | Description | Default |
|---|---|---|---|
| location | No | Optional city or state to filter by | |
| service_type | Yes | Exact service offered (matched against the services array) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden, but it only restates the basic behavior. It does not disclose whether matching is exact, case-sensitive, or partial, nor what happens with no results or multiple results. The schema notes matching against the services array, but the description adds no additional behavioral context.
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 only two sentences and includes a clarifying example. Every word earns its place, and the structure is front-loaded with the core action before the usage hint.
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 low complexity (2 simple parameters, no output schema, no annotations), the description is largely complete for tool selection and invocation. It could mention that 'location' is optional, but the schema already covers that, so no major gap exists.
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% and the description adds meaningful nuance by explaining the difference between an exact service and a category, with a clear example. This helps agents understand how to populate 'service_type' beyond the schema's generic phrasing.
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 it finds businesses offering a specific service type, using a specific verb ('Find') and resource ('businesses'). It also distinguishes itself from siblings via the 'exact service rather than the category' contrast and a concrete example.
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 explicitly specifies when to use the tool ('when you know the exact service') and contrasts with category-based search. It does not name alternative sibling tools or state when not to use it, but the context is clear enough for 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.
The three legacy aliases (get_provider_details, search_providers, submit_quote_request) are exact duplicates of existing tools, creating direct ambiguity. The three search tools (search_businesses, search_by_city, search_by_service) overlap somewhat, though descriptions help clarify their focus.
Most tools follow a clear verb_noun snake_case pattern (e.g., get_business, list_categories, compare_businesses). Minor deviations include the 'search_by_*' variants and legacy aliases with different noun choices (provider vs. business), but these are still readable and consistent in style.
13 tools is within a reasonable range for a business intelligence server, but 3 are redundant legacy aliases, reducing the effective unique tool count to 10. The count is appropriate but slightly inflated by the aliases.
The server covers core workflows: searching, retrieving profiles, reviews, services, availability, comparisons, and service requests. Minor gaps exist, such as no booking capability and limited filtering options, but the primary use case is well served.
