BestRobotMower MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_robot_mowersA | List and filter robot lawn mowers from the BestRobotMower.co dataset. Filter by brand, navigation type, price, lawn coverage, slope handling and minimum score; sort by score, price, coverage or value. |
| get_robot_mowerA | Get the full spec sheet, sub-scores and cited sources for one robot lawn mower by model name or slug. |
| compare_robot_mowersA | Compare 2 to 4 robot lawn mowers side by side on price, navigation, coverage, slope, cutting specs and scores. |
| recommend_robot_mowerB | Recommend the best-fitting robot lawn mowers for a specific yard: give the lawn size and optionally slope, budget and whether obstacle avoidance is needed. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: listing/filtering, retrieving details, comparing, and recommending. There is no overlap in what an agent would use each tool for.
All tool names follow a consistent verb_noun pattern: list_robot_mowers, get_robot_mower, compare_robot_mowers, recommend_robot_mower. The pattern is predictable and uniform.
Four tools is well-scoped for a niche product dataset: browse, inspect, compare, and recommend. Each tool earns its place without redundancy or bloat.
The surface covers the full consumer workflow: discover, detail, compare, and get personalized recommendations. Minor gaps like explicit review retrieval or accessory filtering exist, but they are not essential to the core purpose.