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AIWerk

@aiwerk/mcp-server-elevenlabs

by AIWerk

get_agent_topics_route

Read-onlyIdempotent

Fetch and rank an agent's conversation topics by sentiment, frustration, success rate, or volume, with optional time windows and pagination for analysis.

Instructions

Get Agent Conversation Topics

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cursorNoUsed for fetching next page. Cursor is returned in the response.
sort_byNoColumn to rank topics by. Use conversations for volume, sentiment with sort_direction=asc for the most negative topics, and frustration with sort_direction=desc for the most frustrated ones. Topics with no score are always ranked last.
agent_idYesID of the agent
page_sizeNoNumber of top-level topic groups to return.
to_unix_secsNoEnd of the window to view topics for.
from_unix_secsNoStart of the window to view topics for. When set with to_unix_secs, the completed daily topic-discovery runs in the range are aggregated together, so the window scopes the metrics as well as the topic set. Floored to the start of its UTC day because runs cover whole UTC days; aggregated_run_count re
sort_directionNoDirection to sort topics.
include_evaluation_criteriaNoInclude the per-criteria evaluation breakdown on each topic's metrics. Pass false to drop it: it dominates the payload and the weighted success_rate is returned either way.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.4/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds nothing beyond them, such as that results depend on completed daily topic-discovery runs, that windows snap to UTC day boundaries, or that the criteria breakdown can dominate payload size.

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

Conciseness2/5

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

At four words it is terse rather than concise: for an 8-parameter tool with non-obvious windowing and sorting behavior, this is under-specification, not efficient front-loading. There is nothing to front-load because no substantive content is present.

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

Completeness2/5

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

The tool takes 8 parameters, paginates via cursor, and aggregates across UTC-day topic-discovery runs, yet the description explains none of this. There is no output schema, so return-value context also falls to the description, and none is provided; only the unusually detailed input schema compensates.

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%, and the schema itself is unusually rich on the non-trivial parameters (sort_by directions, window aggregation behavior, include_evaluation_criteria payload impact). The description contributes no parameter semantics of its own, so the baseline of 3 applies.

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

Purpose3/5

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

The description gives a verb (Get) and a resource (Agent Conversation Topics), so it is not a pure tautology of the name, and the added word 'Conversation' narrows the kind of topics. However, it never explains what a 'topic' actually is, what the topic set represents, or that this returns ranked/grouped topic analytics, so an agent gets only a vague sense of purpose.

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

Usage Guidelines2/5

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

There is no when-to-use guidance, no exclusions, and no mention of how this differs from adjacent tools like get_agent_summaries_route, run_conversation_analysis, or get_agent_route. The agent must infer the appropriate context entirely from the name.

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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