List AI agents in your organization to retrieve their voice, LLM, language settings, and call statistics.
List AI agents in your organization. Returns agent configuration including voice, LLM model, language settings, and call statistics.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results to return (default 20, max 50) | |
| agent_name | No | Filter by agent name (partial match, case-insensitive) | |
| include_archived | No | Include archived agents |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool lists agents and returns configuration fields, which implies a read-only operation. It does not disclose pagination behavior, rate limits, or whether the result includes non-archived agents by default, though the schema covers defaults.
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 sentence that front-loads the primary action and resource, then lists the useful return fields. Every word contributes meaning, and there is no redundant or filler content.
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 list tool with fully documented optional parameters, the description covers the core purpose and return content. It does not fully replace the lack of an output schema, but it names the key return fields and is sufficient for an agent to decide when to call it.
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 input schema already provides 100% description coverage for all three parameters (limit, agent_name, include_archived). The tool description adds no parameter-specific meaning beyond saying the result includes configuration and call statistics, so the baseline of 3 applies.
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 lists AI agents in the organization and specifies the resource type, distinguishing it from sibling tools dealing with campaigns, call logs, or phone numbers. It does not explicitly name any sibling alternative, but the resource itself 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?
The intended use case is implied: use this when you need to list agents and view their configuration. However, there is no explicit guidance about when not to use it or how it compares with related tools like get_campaigns or get_call_logs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.