List the expert personas available to sit on a Perspect AI panel (id, name, discipline).
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_personas"
}
}| 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?
With no annotations provided, the description carries the full burden. The verb 'List' clearly indicates a read-only operation with no side effects. It does not mention error conditions or empty results, but for a simple list operation this is sufficient.
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, concise sentence that imparts all necessary information without redundant words. It is well-structured and directly to the point.
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 that there is no output schema, the description adequately specifies the returned fields (id, name, discipline). It fully communicates what the tool returns and its purpose, leaving no critical gaps for a simple list operation.
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?
There are no parameters, so no explanation is required. The baseline score of 4 is appropriate since the description does not need to elaborate on any inputs.
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 function: listing expert personas, and specifies the exact fields returned (id, name, discipline). The verb 'List' is unambiguous, and the scope ('available to sit on a Perspect AI panel') is well-defined. It also distinguishes itself from the only sibling tool 'run_debate' by its purpose.
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 usage (to see available personas before running a debate) but does not explicitly say when to use it versus 'run_debate'. There is no direct contrast or conditional guidance, so the agent must infer the appropriate context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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