RunWhen Platform MCP

List Assistants

list_assistants

List AI assistants configured in a workspace to discover existing personas before creating new ones or to retrieve persona_name values for workspace_chat.

Instructions

List AI assistants (personas) configured in a workspace.

An assistant is a persona — its shortName is the value you pass as persona_name to workspace_chat. Use this to discover which assistants already exist before creating a new one.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "workspace_name": "<workspace_name>"
    },
    "name": "list_assistants"
  }
}

Input Schema

Name	Required	Description	Default
`workspace_name`	Yes	The workspace to query (e.g. 't-oncall').

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 7, 2026

TDQS

A4/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. "List" implies a read-only, non-destructive operation, and the shortName-to-persona_name explanation is useful semantic context. However, it says nothing about result volume, pagination, or ordering, which matters for a list tool with a large potential result set.

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

Conciseness5/5

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

Two tight sentences, front-loaded with the core action, and every clause earns its place by defining the resource and linking the output to a downstream tool.

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

Completeness4/5

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

An output schema exists, so return values need no explanation, and the single parameter is fully documented. The description supplies the key cross-tool semantic (shortName → persona_name) and a usage trigger. Minor omission: no mention of result scale or pagination for a list operation.

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

Parameters3/5

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

Schema description coverage is 100% with a single, well-documented workspace_name parameter, so the schema already does the work. The description adds no syntax or format detail for the parameter itself; its shortName note concerns a sibling tool's input, not this one. Baseline 3 is correct.

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 ("List") and resource ("AI assistants (personas) configured in a workspace"), then clarifies with a definition of what an assistant is. The plural "List" clearly distinguishes it from get_assistant, create_assistant, update_assistant, and delete_assistant among the 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?

Gives a concrete when-to-use: "Use this to discover which assistants already exist before creating a new one," routing toward create_assistant. It also cross-references workspace_chat by explaining the persona_name linkage. No explicit when-not or exclusion is stated, so it falls short of a 5.

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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