llmledger

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Local Or Cloud

local_or_cloud

Read-onlyIdempotent

Compare running an open model locally against paying per call for cloud models, including monthly cost, hardware break-even, electricity, and speed.

Instructions

Pay a cloud model per call, or buy a computer and run an open model at home: the cost a month each way, the month buying pays off, and whether the computer keeps up. The local model's speed comes from your own timed runs (log_run); electricity from EIA or your price; the computer's price is yours.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`state`	No	A US state, for EIA's average there, for example Texas
`watts`	No	Power the computer draws while it answers, in watts
`months`	No	How many months to compare over
`thinking`	No	Thinking turned on for the local model
`local_way`	No	How the local model is called	ollama
`local_model`	Yes	The open model you would run at home, as Ollama names it, for example llama3.1:8b
`cloud_models`	Yes	1 to 5 cloud models to compare with
`input_tokens`	Yes	Prompt tokens per call
`cached_tokens`	No	Prompt tokens read from the cloud model's cache
`output_tokens`	Yes	Answer tokens per call, including hidden thinking
`hardware_price`	Yes	What the computer costs you, in dollars
`calls_per_month`	Yes	Calls a month
`electricity_price`	No	Your electricity price, dollars per kWh (default: EIA's average for homes)

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.7Oct 20, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, closed-world), so the description is free to add provenance detail: local speed comes from your own timed runs logged via log_run, electricity from EIA or a supplied price, hardware price is yours. That is useful behavioral context about data dependencies. Minor tension: it cites EIA as a source while openWorldHint=false, though this may be a bundled dataset and is not a clear contradiction.

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?

Two sentences with no filler; the core comparison is front-loaded. The second sentence packs three source notes efficiently, though it is slightly dense and could be split for readability.

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?

An output schema exists, so return values need no explanation, and all 13 parameters are documented in the schema. The description supplies enough context (comparison scope, data provenance) for an agent to invoke this correctly, leaving only sibling routing unaddressed.

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% across 13 parameters, so the schema already carries parameter meaning (including Ollama naming and the local_way enum). The description adds only marginal parameter context, such as the dependency of local speed on logged runs. Baseline 3 is appropriate.

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

Purpose4/5

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

The description states a concrete comparison: per-call cloud cost versus buying hardware and running an open model locally, and names the outputs (monthly cost each way, break-even month, whether the computer keeps up). It is clearly distinguishable from generic siblings like estimate_cost, though it never names compare_models or estimate_cost as alternatives.

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?

Usage is only implied – the agent can infer this is for local-vs-cloud decisions. The mention of log_run is framed as a data source for speed, not as an alternative tool or a precondition for calling this one. No explicit when-to-use vs compare_models/estimate_cost guidance.

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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