list_agent_modelsList OpenAI, Claude, and Z.AI models, current server pricing, capabilities, BYOK support, and sample Point quotes.
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_agent_models"
}
}| 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?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the description does not need to restate safety. It adds behavioral context by specifying exactly what data is returned (models, pricing, capabilities, BYOK support, quotes). However, it does not disclose output format, pagination, or rate limits, which are not covered by annotations but could be relevant for a listing tool.
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, efficient sentence that front-loads the main purpose and lists the specific data returned. There is zero waste or redundancy. It is appropriately sized for a simple list operation.
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 tool with no parameters, no output schema, and a clear read-only purpose, the description is complete. It covers what is listed and the specific aspects included. The low complexity means nothing essential is missing.
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 the description carries no parameter burden. Per the rubric, a baseline of 4 is appropriate when there are no parameters to document. The description adds no parameter semantics, but none are needed.
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 verb 'List' and the resource 'agent models' from specific providers (OpenAI, Claude, Z.AI), along with additional details (pricing, capabilities, BYOK support, Point quotes). This is distinct from sibling tools, which all relate to AI packs, not model listings. The purpose is unambiguous.
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?
While there is no explicit 'use when' statement, the description makes it evident that this tool is for retrieving model information. Sibling tools are all about AI packs, so no confusion exists. The context is clear, and no exclusions are needed since there are no overlapping alternatives.
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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