version
Retrieves the version of the connected LAVA server to confirm compatibility and identify the server release.
Instructions
Return the version of the connected LAVA server.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Retrieves the version of the connected LAVA server to confirm compatibility and identify the server release.
Return the version of the connected LAVA server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description accurately conveys that the tool performs a read-only operation to retrieve version information. It explicitly states the return value, which is sufficient transparency given the tool's simplicity.
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, complete sentence with no redundant words. Every word contributes to the purpose, making it highly concise.
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 tool has no parameters, no output schema, and a straightforward function, the description is largely complete. It could optionally mention use cases like verifying connectivity, but this is not essential.
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 tool has no parameters, and the schema coverage is 100%. The description does not need to add parameter information, and the baseline for zero parameters is 4.
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 ('Return') and resource ('version of the connected LAVA server'), clearly stating the tool's function. It is distinct from sibling tools like get_lab_health or get_qdl_info, which serve different purposes.
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?
No guidance is provided on when to use this tool versus alternatives. While the tool is simple, the description does not indicate scenarios or prerequisites, leaving the agent to infer its use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/mattface/lava-mcp'
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