Calculate expected LLM usage costs from prompt length, knowledge base pages, and RAG status to estimate credit spend before running ElevenLabs calls.
Calculate Expected Llm Usage
| Name | Required | Description | Default |
|---|---|---|---|
| rag_enabled | Yes | Whether RAG is enabled. | |
| prompt_length | Yes | Length of the prompt in characters. | |
| number_of_pages | Yes | Pages of content in PDF documents or URLs in the agent's knowledge base. |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=false, openWorldHint=true, idempotentHint=false, and destructiveHint=false. The description adds nothing beyond those structured hints: it does not say what side effects may occur, what authentication or rate limits apply, or whether the calculation is cached. It does not contradict the annotations, but it is uninformative.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is only four words, but it is drastically undersized for a calculation tool with three required inputs and no output schema. Brevity here reflects missing information rather than efficient structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description should explain what the calculation returns and what distinguishes it from the agent-scoped sibling. Instead it provides only a vague phrase, leaving the agent without enough context to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the three required parameters are already fully documented in the schema. The description adds no additional meaning such as valid ranges, units, or interaction between prompt_length, number_of_pages, and rag_enabled. Baseline 3 is appropriate when the schema carries the full parameter burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description restates the title as a bare imperative phrase and does not distinguish this public calculation tool from the sibling get_agent_llm_expected_cost_calculation. It also drops the word 'cost' from the tool name, replacing it with the vaguer 'usage', so an agent cannot confidently tell what is being calculated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool, when not to use it, or which alternatives exist. The sibling get_agent_llm_expected_cost_calculation is never mentioned, leaving the agent with no routing signal.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.