Search motorhomes, RVs and campervans worldwide. Get instant results from 300+ rental companies across AU, NZ, US, CA, UK and more. No auth required.
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Usage analytics
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Average 4.3/5 across 2 of 2 tools scored.
Both tools have clearly distinct purposes: list_locations returns valid city names, and search_campervans searches for rentals. There is no overlap.
Both tools follow a consistent snake_case verb_noun pattern: list_locations and search_campervans.
With only 2 tools, the server is quite minimal. While it covers the basic search workflow, it lacks additional tools like retrieving results or managing bookings, making it borderline for its domain.
The server only covers the initial search step. After search, there is no tool to retrieve results or book, relying on the user to interact with a web page. This creates a significant gap for automated workflows.
list_locationsAList all searchable pickup cities by country. Call this to find valid city names before searching.
| Name | Required | Description | Default |
|---|---|---|---|
| country | No | Country code filter (e.g. AU, NZ, US). Omit to return all supported countries. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds value by specifying that the list contains 'searchable pickup cities' and that it's for finding 'valid city names before searching,' which gives context beyond the annotations. No contradiction.
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 two sentences: the first states the core function, the second gives a direct usage hint. No redundant words or vague filler. It is appropriately front-loaded and easy to parse.
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 tool with one optional parameter and no output schema, the description is complete. It explains what the tool returns (searchable pickup cities), how to filter (by country), and when to use it (before searching). No additional information is necessary.
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%: the 'country' parameter is fully documented with examples ('e.g. AU, NZ, US') and behavior ('Omit to return all supported countries'). The description's mention of 'by country' aligns with the schema but doesn't add extra semantic detail beyond what's already in the parameter description.
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 the tool's purpose: 'List all searchable pickup cities by country.' This uses a specific verb (list) and resource (searchable pickup cities), and explicitly distinguishes it from the sibling tool 'search_campervans' by focusing on locations rather than campervans.
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 provides clear usage context: 'Call this to find valid city names before searching.' This tells the agent when to use the tool, though it doesn't explicitly mention alternatives or exclusions. The implied workflow (get cities, then search) is helpful.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_campervansASearch campervan and motorhome rentals. Returns a URL that pre-fills the search form with your trip details. Click Search on the page to see live results with pricing, availability, and booking options from 160+ rental companies.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | Pickup city name, e.g. "Sydney", "Auckland", "Los Angeles". Required. | |
| country | Yes | Country code: AU, NZ, US, CA, GB, DE, FR, IT, ES, NL. Required. | |
| pickup_date | Yes | Pickup date YYYY-MM-DD. Required. | |
| dropoff_city | No | Return city if different from pickup (one-way). Optional. | |
| dropoff_date | Yes | Return date YYYY-MM-DD. Required. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds critical behavioral context beyond these hints: the tool returns a URL that pre-fills a form rather than returning results directly, requiring the user to click Search to see live pricing and availability. This is meaningful disclosure of how the tool operates.
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 two sentences, front-loaded with the core purpose, and contains no fluff. It efficiently conveys the tool's function, return value, and required next action.
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?
The description is complete for this simple tool: it explains the tool's output (a URL), the follow-up action needed (click Search), and the scope (160+ rental companies). With comprehensive parameter schemas and safety annotations, the description covers the essential context without needing to document return values since there is no output schema.
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 all parameters are already documented with types and examples. The description adds a general framing—'pre-fills the search form with your trip details'—but does not add per-parameter semantics beyond what the schema already provides. Given the high schema coverage, a baseline score 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 clearly states the tool's purpose: 'Search campervan and motorhome rentals.' It also explains the unique return behavior—a URL that pre-fills a search form—which distinguishes it from the sibling tool list_locations.
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 provides clear context for when to use the tool (to search campervan and motorhome rentals) and includes an actionable workflow step: 'Click Search on the page to see live results.' It does not explicitly mention alternatives or exclusions, but the usage context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
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For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
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Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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