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easyonthesauce

elevenlabs-agents-mcp-server

List ElevenLabs Agents

elevenlabs_list_agents
Read-onlyIdempotent

List conversational AI agents in your ElevenLabs workspace with optional name filtering and pagination. Retrieve agent IDs needed for making outbound calls.

Instructions

List conversational AI agents in the ElevenLabs workspace.

Use this first to discover the agent_id needed by elevenlabs_make_outbound_call.

Args:

  • search (string, optional): filter by agent name

  • page_size (number): 1-100, default 30

  • cursor (string, optional): pagination cursor

Returns JSON: { agents: [{ agent_id, name, created_at_unix_secs }], has_more, next_cursor }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cursorNoPagination cursor from a previous response's next_cursor
searchNoOptional name filter, e.g. 'Jack'
page_sizeNoMax agents to return (1-100, default 30)
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds pagination behavior and return format, which is helpful but not critical beyond annotations. No contradictions.

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?

The description is three concise blocks: purpose/usage, parameter list, return format. Every sentence adds value with no fluff.

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?

Given the moderate complexity, full schema coverage, and no output schema, the description explains the return format, pagination, and relationship to outbound calls completely. No gaps remain.

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%, so the schema already explains all parameters. The description summarizes them concisely but adds no new semantic meaning beyond what the schema provides.

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?

The description explicitly states the verb 'list', the resource 'conversational AI agents in the ElevenLabs workspace', and connects to the sibling tool by noting it discovers agent_id for elevenlabs_make_outbound_call. This distinguishes it clearly from other listed tools.

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?

The description includes a direct usage guideline: 'Use this first to discover the agent_id needed by elevenlabs_make_outbound_call.' While it doesn't explicitly state when not to use it, the context is clear and the sibling list provides alternative tool names.

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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