get_serviceGet full details for a specific residential service by its slug. If not found, returns a list of available slugs to help you pick the right one.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The URL slug, e.g. "kitchen-remodeling" |
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| slug | No | ||
| title | No | ||
| attribution | Yes | ||
| description | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish readOnly, idempotent, and non-destructive behavior. The description adds a valuable behavioral detail beyond annotations: on not-found it returns a list of available slugs to help the caller choose the correct one. This enriches the agent's understanding of the tool's failure mode.
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 sentences, no filler, and the main action is stated first. The fallback behavior is a natural second sentence, making the description efficient and well structured.
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?
For a single-parameter read-only lookup with an output schema, annotations, and a clear fallback behavior, the description is complete. Nothing an agent needs to call the tool correctly is missing.
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 schema has 100% description coverage for the single 'slug' parameter, including a concrete example. The description only reinforces 'by its slug' without adding new parameter-level semantics, so the baseline of 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 uses a specific verb ('Get full details') and a specific resource ('residential service') keyed by slug, clearly distinguishing this from list_services and list_commercial_services. The phrase 'full details' also separates it from lighter summary tools.
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 clearly conveys when to use the tool: when you need details for a specific residential service identified by slug. The not-found fallback also provides practical guidance for a wrong/invalid slug. However, it does not explicitly name alternatives like list_services for browsing, though that is strongly implied.
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.
Most tools have clearly distinct purposes, but get_neighborhood_project_activity and get_neighbor_permit_activity are easy to confuse, and get_material_catalog vs get_material_options have overlapping scope. The detailed descriptions mostly resolve ambiguity, so it stays above average.
Naming follows a mostly consistent get_/list_ verb-noun pattern, with a small set of other descriptive verbs like check_, estimate_, and resolve_. The neighbor/neighborhood naming collision and a few multi-word exceptions are the main inconsistencies.
At 36 tools, the surface is heavy and exceeds the 25+ threshold, spanning permits, estimates, content, reviews, financing, weather, and procurement. While each cluster has plausible value, the overall count feels bloated for an agent to navigate efficiently.
The set covers a broad lifecycle: permit research, estimates, licensing, financing, ROI, weather, project/services content, reviews, and lead submission. Minor gaps like scheduling/booking and direct estimate persistence prevent a perfect score.