Aggregate local LLM inference ledger totals for requests, tokens, average TTFT, energy, local and cloud-equivalent costs, and percent savings to answer home GPU economics questions.
Aggregate totals from the local LLM inference ledger: request count, prompt/completion tokens, average TTFT, energy (kWh), local cost ($), cloud-equivalent cost ($), and percent saved vs cloud.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full behavioral burden. It implicitly indicates a read-only aggregation and discloses exactly which values are returned (request count, tokens, TTFT, energy, costs, percent saved), which is genuinely useful given there is no output schema. It does not state the aggregation window (all-time? per session?), whether auth or a populated ledger is required, or what happens with an empty ledger.
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?
A single front-loaded sentence with the core purpose first and the metric list following. The list is long but each item is a distinct output field, so it earns its space.
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?
For a parameterless stats tool with no output schema, enumerating the returned metrics in the description is the right compensating move and it is done thoroughly. The main residual gap is the scope/window of the aggregation and its relationship to the overlapping cloud-savings sibling.
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?
Zero parameters, so the schema baseline of 4 applies; there is nothing parameter-related for the description to clarify or omit.
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?
States a specific verb and resource: aggregate totals from the local LLM inference ledger, with an explicit enumeration of the metrics produced. It does not, however, differentiate itself from siblings like tokledger_cloud_savings, which by name appears to cover the same cloud-equivalent/percent-saved territory.
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?
No guidance on when to call this versus tokledger_recent, tokledger_model_breakdown, or tokledger_cloud_savings. The overlap with tokledger_cloud_savings in particular is left for the agent to guess at.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.