Skip to main content
Glama
Sign In
@aiwerk/mcp-server-elevenlabs

@aiwerk/mcp-server-elevenlabs

by AIWerk
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

get_agent_llm_expected_cost_calculation

Estimate an ElevenLabs agent's expected LLM usage and cost before running it. Provide the agent ID plus optional RAG, prompt length, and page count to project token spend.

Instructions

Calculate Expected Llm Usage For An Agent

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": {
      "agent_id": "<agent_id>"
    },
    "name": "get_agent_llm_expected_cost_calculation"
  }
}

Input Schema

NameRequiredDescriptionDefault
agent_idYes
rag_enabledNo
prompt_lengthNo
number_of_pagesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.3/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=false, openWorldHint=true, idempotentHint=false, and destructiveHint=false, so the safety profile is partly covered. The description adds nothing on top of that: it does not say whether the result is an estimate, whether it requires prior usage data, or whether it consumes billable resources. For a calculation tool this is a meaningful gap.

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?

A single short sentence with no filler, and the purpose is front-loaded. It is efficient, though efficiency here borders on under-specification rather than true conciseness.

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?

With four undocumented parameters, no output schema, and a non-obvious calculation whose semantics matter (what the estimate covers, what units, what happens when optional params are null), the description is far too thin. An agent has almost nothing to act on beyond the name.

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

Parameters1/5

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

Four parameters exist with 0% schema description coverage, so the description carries the full burden and fails it. rag_enabled, prompt_length, and number_of_pages are critical inputs for a cost estimate and none are explained or even mentioned. The phrase 'for an agent' only loosely gestures at agent_id.

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 states a verb (Calculate) and a resource (Expected LLM usage for an agent), so the basic purpose is derivable. However, it is essentially a title-cased restatement of the tool name and offers no differentiation from the very close sibling get_public_llm_expected_cost_calculation. An agent cannot tell the two apart from the text alone.

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 prerequisites, and no mention of the closely related public cost-calculation sibling. The agent is left to infer entirely from the name which of the two cost tools to call.

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

Deploy Server

Other Tools