Robot lawn mower dataset from BestRobotMower.co: specs, prices, 0-5 scores, buying recommendations.
Scored across 4 tools
Each tool serves a clearly distinct purpose: fetching a single mower, listing/filtering the dataset, comparing multiple mowers side-by-side, and generating personalized recommendations. An agent would not struggle to choose between them.
All four tools follow a consistent verb_noun pattern: compare_robot_mowers, get_robot_mower, list_robot_mowers, recommend_robot_mower. The verbs are specific and the noun stem is uniform, making the API predictable.
With only four tools, the set is tightly scoped for a read-only product dataset. Every tool earns its place and the count is well within the ideal 3–15 range for a focused server.
The domain is a robot lawn mower reference dataset, and the four operations cover all realistic read-side needs: retrieve individual details, browse/list with filters, compare selected models, and get advice for a specific yard. No create/update/delete is needed since the server is informational, so there are no obvious gaps.
Compare 2 to 5 robot lawn mowers side by side across navigation, coverage, slope, cutting specs, price and the 0-5 BestRobotMower Score. Pass each model by name or slug.
| Name | Required | Description | Default |
|---|---|---|---|
| models | Yes | 2 to 5 model names or slugs to compare. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It states the operation, the required input of 2-5 models by name/slug, and the comparison dimensions, but it does not describe the response format, error behavior, or how invalid/duplicate models are handled.
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?
One compact sentence that leads with the action, states the accepted count and input format, and enumerates comparison dimensions without redundancy.
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 comparison tool, the description covers purpose, input arity, accepted identifiers, and the fields included in the comparison. It does not spell out the response shape, but the listed dimensions effectively indicate what the comparison returns.
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 input schema already documents the models parameter at 100% coverage, including min/max count and 'names or slugs.' The description repeats this ('2 to 5', 'name or slug') without adding new semantic detail, so it stays at the baseline.
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 names a specific verb ('Compare'), a concrete resource ('robot lawn mowers'), and a clear scope: 2 to 5 models, compared across named attributes. This distinguishes it from sibling tools like get_robot_mower, list_robot_mowers, and recommend_robot_mower.
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 establishes when to use it: when a side-by-side comparison of multiple mowers across specific dimensions is needed. It does not explicitly name alternatives or exclusion conditions, but the comparison focus provides clear context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the full specs, dated price, 0-5 score and sub-scores for a single robot lawn mower by name or slug (e.g. 'Mammotion Luba 2 AWD', 'Segway Navimow i110').
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Model name, brand + model, or slug to look up. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral disclosure burden. It does disclose the response contents (specs, dated price, score, sub-scores), which is useful, but it does not address behaviors such as not-found handling, name ambiguity, normalization, or whether the operation is purely reads with no side effects.
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, well-structured sentence that front-loads the return value and then states the input format with examples. Every word earns its place, with no filler or redundancy.
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 simple one-parameter lookup tool, the description covers the essential invocation details: what to pass, the acceptable input formats, and what data to expect back. It does not detail the exact response shape, and there is no output schema, but the described output fields are sufficient for an agent to use the tool confidently.
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 already fully documents the single 'query' parameter, so the baseline is 3. The description adds concrete examples ('Mammotion Luba 2 AWD', 'Segway Navimow i110') and clarifies that name, brand + model, or slug are all acceptable, which helps an agent phrase queries correctly beyond the schema text.
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') and a specific resource ('a single robot lawn mower'), and clearly enumerates what is returned: full specs, dated price, 0-5 score, and sub-scores. The phrase 'by name or slug' with concrete examples makes the purpose unmistakable and distinguishes it from list/compare/recommend siblings.
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 implies when to use the tool by focusing on 'a single robot lawn mower by name or slug', so an agent can infer it is for known-model lookups rather than browsing or comparing. However, it never explicitly names sibling alternatives or states when not to use them, leaving some routing to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List and filter the robot lawn mowers in the BestRobotMower.co dataset by brand, navigation type, price, lawn-area coverage, slope handling and score. Returns models ranked by the 0-5 BestRobotMower Score (highest first).
| Name | Required | Description | Default |
|---|---|---|---|
| brand | No | Filter by brand, case-insensitive substring (e.g. 'Mammotion', 'Segway', 'Husqvarna'). | |
| limit | No | Max models to return (default 10, max 23). | |
| min_score | No | Only models with a BestRobotMower Score at or above this value (0-5). | |
| navigation | No | Filter by navigation type, case-insensitive substring (e.g. 'RTK', 'satellite', 'vision', 'wire'). | |
| max_price_usd | No | Only models at or below this USD price. | |
| min_slope_pct | No | Only models rated for at least this slope, in percent grade. | |
| min_lawn_area_m2 | No | Only models that cover at least this lawn area in square metres. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the transparency burden. It discloses a key behavioral trait: results are ranked by the 0-5 BestRobotMower Score, highest first, and 'List' implies a read-only operation. It does not mention edge behaviors like empty results or default limit handling, but the schema covers the limit default.
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, front-loaded with the action and resource, with no filler. The ranking behavior is stated compactly in the second sentence, and every clause earns its place.
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 read-only list tool with 7 optional filters and no output schema, the description covers the core purpose and the result ordering. It does not detail returned model fields or pagination, but the 100% schema coverage and simple list semantics make the description sufficient; a mention of the default/max limit would be a minor improvement.
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 100%, so the baseline applies. The description names the filter dimensions (brand, navigation, price, lawn area, slope, score) but adds no semantic detail beyond what the parameter descriptions already provide.
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 opens with a specific verb ('List and filter') and a named resource ('robot lawn mowers in the BestRobotMower.co dataset'), which clearly distinguishes it from get, compare, and recommend siblings. The ranking clause further defines the output as a browse/filter operation rather than a single-item or recommendation tool.
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 conveys the intended use case—listing and filtering a dataset—but it never names sibling tools or states when to prefer them. Usage is implied through 'List and filter' rather than explicitly guided with alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recommend the best robot lawn mowers for a specific yard: give the lawn area in square metres and optionally the steepest slope (percent grade) and a budget in USD. Returns up to 3 models that fit, ranked by the 0-5 BestRobotMower Score, with the reason each was chosen.
| Name | Required | Description | Default |
|---|---|---|---|
| budget_usd | No | Maximum budget in USD (optional). | |
| lawn_area_m2 | Yes | Lawn area to mow, in square metres. | |
| max_slope_pct | No | Steepest slope on the lawn, in percent grade (optional). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the output behavior (up to 3 models, ranked by score, with reasons) and the optionality of inputs. However, it doesn't disclose what happens if no model fits the budget/slope constraints, whether the score is computed internally or from a database, or any rate limits or data freshness. The description adds some behavioral context but not comprehensive.
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, well-structured sentence that front-loads the core purpose, then lists inputs and outputs in a natural order. Every clause earns its place: the input specification, the output count, the ranking criterion, and the reason. No wasted words.
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 recommendation tool with 3 simple parameters and no output schema, the description covers the essential inputs and output shape. It doesn't explain edge cases (e.g., no matching models, invalid slope values) or the meaning of the BestRobotMower Score, but these are minor gaps given the tool's simplicity. The description is complete enough for an agent to call it 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 100%, so the schema already documents all three parameters. The description adds context by explaining how the parameters are used (lawn area is required, slope and budget are optional constraints) and that the output is ranked by a score. This is slightly above baseline but doesn't add significant new meaning beyond the schema.
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 states a specific verb ('Recommend'), a resource ('robot lawn mowers'), and the exact inputs (lawn area, slope, budget) and output (up to 3 models ranked by BestRobotMower Score with reasons). This clearly distinguishes it from siblings like list_robot_mowers (which likely lists all) and compare_robot_mowers (which likely compares specific models).
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 implies when to use this tool: when you need recommendations for a specific yard, with area required and slope/budget optional. It doesn't explicitly state when not to use it or name alternatives, but the context signals and sibling names make the use case clear. A small gap: it doesn't explicitly say 'use list_robot_mowers to see all models' or 'use compare_robot_mowers to compare specific models.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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