hail

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by hail-hq

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Python

Hybrid

List agents

list_agents

Read-onlyIdempotent

List the workspace's saved agents: the brains that answer calls and texts on its numbers, and that place_call can use via agent_id.

Instructions

List the workspace's saved agents: the brains that answer calls and texts on its numbers, and that place_call can use via agent_id.

    Returns ``{"items": [{"id", "name", "system_prompt", "first_message",
    "ai_disclosure", "ai_disclosure_line", "voice_config", "tools",
    "max_duration_seconds", "handover_max_duration_seconds", "voice_enabled",
    "sms_enabled", "status", "handover_contacts"}, ...]}``.

Tool call template

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_agents"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.29.0Oct 21, 2026

TDQS

A4.3/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare read-only, idempotent, non-destructive behavior; the description adds workspace scope, the semantic role of agents, and the returned fields. It does not mention pagination, rate limits, or empty-list behavior, but the annotation coverage lowers the burden.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The first sentence front-loads purpose effectively, but the second sentence reproduces a long return field list even though an output schema exists. That makes the description more verbose and redundant than necessary.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter, read-only list tool with rich annotations and an output schema, the description covers purpose, workspace scope, the semantic role of agents, and return shape. Nothing critical for correct invocation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero input parameters and schema coverage is 100%, so there are no parameter semantics to explain. Baseline for a zero-parameter tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('List ... saved agents'), scopes it to the workspace, and explains what an agent is (answers calls/texts, usable by place_call via agent_id). This clearly distinguishes it from get_agent, create_agent, and other siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear context for discovering saved agents and notes their relationship to place_call via agent_id. It does not explicitly say when to prefer get_agent for a single agent or mention ordering/exclusions, so it stops short of full when/when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.