# ai-economics [Health: Active]

**Category:** 🗣️ Conversational AI  
**Repository:** https://github.com/pich/ai-economics-mcp  
**GitHub Stars:** 1  
**npm Downloads (last month):** 149  
**Views:** 0  
**Installs:** 0  
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**Directory Page:** https://allmcps.com/mcp/ai-economics

## Description
12 calculators for AI cost, energy and agent verification. Free API, no key.

## Tools
Capabilities this server exposes over MCP:

- **token_cost** — What do 1M tokens cost per month across GPT, Claude, Gemini, DeepSeek?
- **context_window** — How many tokens is 50 pages, does it fit, what does carrying it cost?
- **agent_hour** — What does one hour of an AI agent cost, fully loaded with verification?
- **model_routing** — How much does routing to a cheaper tier save per year?
- **llm_energy** — How much electricity does an AI query use?
- **joules_per_verified_task** — Which model is most efficient per task that actually passes?
- **token_burn** — What does org-wide token burn cost in money, kWh, CO₂?
- **humanoid_energy** — How long can a humanoid robot run per charge?
- **proof_adjusted_autonomy** — How autonomous is a "90% autonomous" agent once proof is required?
- **revocation_exposure** — How long does a revoked token keep working across your stack?
- **verification_bottleneck** — How many AI agents can a human review team absorb?
- **proof_debt** — What does unverified AI work cost over time?

## Claude Desktop Quick Installation
Install path detected from listing signals. Uses `npx` (confidence: high):

```json
"mcpServers": {
  "ai-economics": {
    "command": "npx",
    "args": ["-y","@michalpiszczek/ai-economics-mcp"],
    "env": {
      "AI_ECONOMICS_API": ""
    }
  }
}
```

**Requires environment variables:** `AI_ECONOMICS_API` — the values above are empty placeholders; fill in real credentials before running (see the repository for what each one is for).

## Documentation

## What ai-economics does

The ai-economics MCP server exposes twelve focused calculators for assessing the operational and economic consequences of AI systems. It covers token pricing across GPT, Claude, Gemini, and DeepSeek; context-window sizing; hourly agent costs; model-tier routing; query energy use; and organization-wide token consumption measured in money, electricity, and carbon dioxide.

The calculators also address whether AI work can be trusted or supervised. They estimate energy per verified task, autonomy after evidence and independent validation are required, the time revoked credentials may remain usable, the number of agents a human review team can handle, and the accumulated cost of unverified work. A separate calculator estimates how long a humanoid robot can operate on a charge.

## How it works

The MCP process wraps the free piszczek.pl/tools API. Each tool accepts optional parameters; when values are omitted, the server uses defaults corresponding to the interactive calculators.

Responses include four fields: a calculated result, the formula used, an interpretation, and a `cite_as` sentence suitable for quoting. This makes the output useful both for exploratory questions and for documents that need to explain how a figure was derived. The project is listed in the official MCP Registry as `pl.piszczek/ai-economics`.

## Setup and configuration

For Claude Desktop or Claude Code, run the package through `npx`:

```json
{
  "mcpServers": {
    "ai-economics": {
      "command": "npx",
      "args": ["-y", "@michalpiszczek/ai-economics-mcp"]
    }
  }
}
```

Claude Code also supports `claude mcp add ai-economics -- npx -y @michalpiszczek/ai-economics-mcp`. The package is plain JavaScript and does not need a compilation step. For local stdio execution, it can be built into a Docker image and run interactively without exposing a port.

`AI_ECONOMICS_API` is optional. Set it to replace the default `https://piszczek.pl/tools/api` endpoint, such as when pointing the client at a self-hosted instance. No credentials are required by the documented setup.

## Tools and capabilities

The ai-economics MCP server provides these tools:

- `token_cost` compares monthly costs for one million tokens across listed model families.
- `agent_hour` calculates a fully loaded hourly agent cost that includes verification.
- `model_routing` estimates annual savings from sending work to a cheaper model tier.
- `llm_energy` and `token_burn` estimate electricity use, with `token_burn` also covering financial cost and CO₂.
- `joules_per_verified_task` compares energy efficiency for tasks that pass verification.
- `proof_adjusted_autonomy`, `verification_bottleneck`, and `proof_debt` model evidence, human review capacity, and the cost of unchecked work.
- `humanoid_energy` estimates robot runtime per charge, while `revocation_exposure` estimates how long revoked authority may continue working.

## Limitations and notes

Results depend on the assumptions supplied to each tool. The server connects to the configured calculator API rather than storing calculation inputs locally. Concepts and calculators are licensed separately from the MIT-licensed code: the README identifies the concepts and calculators as CC BY 4.0.

The ai-economics MCP server is best suited to budgeting, comparative analysis, and discussions of verification or operational exposure. It is not documented as a monitoring system, an access-control mechanism, or a replacement for independent energy, cost, or security measurements.

_Full upstream README: https://allmcps.com/mcp/ai-economics/readme_