List the 12 AI continents (tracks) with node counts and top nodes.
This tool declares no required parameters. Add optional parameters from the input schema as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {},
"name": "list_tracks"
}
}| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
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 output contents (node counts and top nodes) and the fixed count (12), but it does not mention return format, ordering, potential errors, or whether the operation is read-only, though 'List' strongly implies a read operation.
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 action and resource, then adds the key output details. Every word contributes meaning, and there is no redundant or vague phrasing.
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 zero-parameter list tool, the description is nearly complete: it names the resource, the expected count, and the included data. It does not specify the exact response structure, but the absence of an output schema makes the description's high-level outline acceptable for basic invocation.
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 zero parameters, so parameter descriptions are not needed. The description's focus on output contents is sufficient; no parameter semantics are required for an agent to invoke this tool correctly.
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 ('List') and a specific resource ('AI continents (tracks)'), and it states exactly what is returned: node counts and top nodes. This clearly distinguishes it from sibling tools like list_entities, which presumably lists entities rather than tracks.
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 this tool is for retrieving the fixed set of 12 tracks, but it does not explicitly state when to choose it over alternatives or when not to use it. No exclusions or sibling comparisons are given, leaving usage context largely implicit.
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.