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ElevenLabs MCP Server

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by elevenlabs

list_conversations

Read-only

Retrieve paginated conversation history from agents with metadata. Filter by agent ID or date range. Ideal for reviewing previous interactions and analyzing conversation patterns.

Instructions

Lists agent conversations. Returns: conversation list with metadata. Use when: asked about conversation history.

Args:
    agent_id (str, optional): Filter conversations by specific agent ID
    cursor (str, optional): Pagination cursor for retrieving next page of results
    call_start_before_unix (int, optional): Filter conversations that started before this Unix timestamp
    call_start_after_unix (int, optional): Filter conversations that started after this Unix timestamp
    page_size (int, optional): Number of conversations to return per page (1-100, defaults to 30)
    max_length (int, optional): Maximum character length of the response text (defaults to 10000)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idNo
cursorNo
call_start_before_unixNo
call_start_after_unixNo
page_sizeNo
max_lengthNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYes
textYes
annotationsNo
_metaNo
Behavior3/5

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

Annotations already provide readOnlyHint (safe read) and openWorldHint. The description adds return type info but does not disclose pagination behavior or potential rate limits.

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

Conciseness4/5

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

Starts with a clear purpose, then returns and usage hint, then parameter list. No unnecessary words, though parameter descriptions could be integrated more concisely.

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?

Covers all 6 parameters with defaults and filtering logic. Output schema exists, so details on return format are not required. Pagination cursor is mentioned but behavior not elaborated.

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?

With 0% schema description coverage, the description lists each parameter with a brief functional description. These are minimal but add essential meaning beyond schema field names.

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 clearly states the tool 'Lists agent conversations' with a specific verb and resource. It distinguishes from siblings like 'get_conversation' which retrieves a single conversation.

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

Usage Guidelines3/5

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

Provides a usage cue 'Use when: asked about conversation history' but lacks explicit when-not-to-use or alternatives such as 'get_conversation' for single conversations.

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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