Agent Cost MCP Server

Name: Agent Cost MCP Server
Author: karmendra8386

by karmendra8386version 0.1.1i★ –iRuns on your computer

Registry nameio.github.karmendra8386/agent-cost-mcp-server

Install

Overview

Description from the maker (original English)

Prices LLM/agent runs per successful task, with spend recording and budget status.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiVery new↓ 282/wk · ★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Accounting, finance & tax

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: AGENT_COST_LEDGER_PATH

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "agent-cost": {
      "command": "uvx",
      "args": [
        "agent-cost-mcp-server"
      ],
      "env": {
        "AGENT_COST_LEDGER_PATH": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add agent-cost --env AGENT_COST_LEDGER_PATH=your-value -- uvx agent-cost-mcp-server

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "agent-cost": {
      "command": "uvx",
      "args": [
        "agent-cost-mcp-server"
      ],
      "env": {
        "AGENT_COST_LEDGER_PATH": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "agent-cost": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "agent-cost-mcp-server"
      ],
      "env": {
        "AGENT_COST_LEDGER_PATH": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

AGENT_COST_LEDGER_PATH env
Optional path for the local spend-ledger file used by record_spend / budget_status.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account karmendra8386, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Accounting, finance & taxi

More stars than 0% of the other 2932 servers in this category
Published to the registry more recently than 84% of the other 2932 servers in this category
Basic checks equal to or better than 100% of the other 2932 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Agent Cost MCP Server safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.1) reached the registry 5 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Finance Toolkit, SolidInvoice, Korea Tax Calculator

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Agent Cost MCP Server

An MCP server that gives agents and developers tools to estimate, budget, and compare the cost of LLM work before and during a run — the model-spend counterpart to AWS infrastructure cost tooling.

Agent frameworks decide for themselves how many model calls a task takes, so the cost of a run is usually only visible after the bill arrives. This server answers, from inside your tooling:

*What will this task cost on model A vs model B?*
*Which model is cheapest per successful task, not per token?*
*Has this run already burned its budget?*

The server is self-contained: a maintained pricing snapshot ships with it, no AWS account or API key is needed, all state is a local JSON ledger, and it makes no network calls at runtime.

Tools

Tool	Arguments	Returns
`price_usage`	`model_id`, `input_tokens`, `output_tokens`	Input/output/total cost in USD for one concrete usage record
`estimate_run_cost`	`model_id`, `input_tokens`, `output_tokens`	The model's cost for one run, plus the same workload priced on every other model, cheapest first
`compare_models_for_task`	`input_tokens`, `output_tokens`, `success_rates?`	All models ranked by expected cost per successful task (cost per attempt ÷ success rate)
`record_spend`	`task_id`, `model_id`, `input_tokens`, `output_tokens`	The recorded ledger entry, priced at list rates
`budget_status`	`task_id`, `budget_usd`	`{spent_usd, remaining_usd, over_budget, calls, models_used, by_model}` for the task. `by_model` maps each model_id to `{calls, input_tokens, output_tokens, spent_usd}`

Cost per token is the wrong ranking for agent work: a cheap model that fails half its tasks costs more per success than a pricier, reliable one. Pass measured success rates to compare_models_for_task if you have them; models without a supplied rate are ranked on raw cost.

This server supplies the numbers. *Enforcing* a budget (stopping a run) belongs to the agent framework — see BudgetedModel in strands-agents/samples for that pattern.

Installation

The server is published on PyPI (current release 0.1.1) and listed on the Official MCP Registry as io.github.karmendra8386/agent-cost-mcp-server. Requires Python 3.10+; the uvx path needs uv.

Claude Code

claude mcp add agent-cost -- uvx agent-cost-mcp-server

Claude Desktop and Cursor

Add to the client's MCP configuration (Claude Desktop: claude_desktop_config.json; Cursor: .cursor/mcp.json):

"mcpServers": { "agent-cost": { "command": "uvx", "args": [ "agent-cost-mcp-server" ] } } }

VS Code

Add to .vscode/mcp.json (VS Code uses a servers key):

"servers": { "agent-cost": { "type": "stdio", "command": "uvx", "args": [ "agent-cost-mcp-server" ] } } }

pip

pip install agent-cost-mcp-server

agent-cost-mcp-server # serves over stdio

From source (GitHub)

If you need the unreleased tip of master rather than the PyPI release:

pip install git+https://github.com/karmendra8386/agent-cost-mcp-server

agent-cost-mcp-server # serves over stdio

Example: what a tool call returns

compare_models_for_task for one attempt at a task using 10,000 input and 2,000 output tokens, with example success rates supplied for a few models (substitute your own measured rates; models without a supplied rate are ranked on raw cost):

"input_tokens": 10000, "output_tokens": 2000, "success_rates": { "amazon.nova-micro": 0.2, "amazon.nova-lite": 0.6, "openai.gpt-5-nano": 0.4, "openai.gpt-4o-mini": 0.9, "anthropic.claude-haiku-3-5": 0.85, "anthropic.claude-sonnet-4-5": 0.95 } }

Response, captured from a live run of the server (pricing snapshot v1, 2026-10-20; first 5 of 18 ranked models):

"input_tokens": 10000, "output_tokens": 2000, "ranked": [ { "model": "amazon.nova-lite", "display_name": "Amazon Nova Lite", "cost_per_run_usd": 0.00108, "success_rate": 0.6, "cost_per_success_usd": 0.0018 }, { "model": "google.gemini-2.5-flash-lite", "display_name": "Gemini 2.5 Flash-Lite", "cost_per_run_usd": 0.0018, "success_rate": 1.0, "cost_per_success_usd": 0.0018 }, { "model": "openai.gpt-4o-mini", "display_name": "GPT-4o mini", "cost_per_run_usd": 0.0027, "success_rate": 0.9, "cost_per_success_usd": 0.003 }, { "model": "amazon.nova-micro", "display_name": "Amazon Nova Micro", "cost_per_run_usd": 0.00063, "success_rate": 0.2, "cost_per_success_usd": 0.00315 }, { "model": "openai.gpt-5-nano", "display_name": "GPT-5 nano", "cost_per_run_usd": 0.0013, "success_rate": 0.4, "cost_per_success_usd": 0.00325 } ], "pricing_snapshot": "v1 (2026-10-20)" }

Note the flip: Amazon Nova Micro is the cheapest model per run ($0.00063), but at a 20% success rate it ranks fourth per *successful* task ($0.00315 — roughly five attempts per success). Nova Lite costs 1.7× more per run and is the cheapest per success.

Pricing snapshot

Prices live in src/agent_cost_mcp_server/pricing.json — a versioned snapshot of list prices (USD per 1M tokens) for major Bedrock and OpenAI-compatible models, dated in the file. Prices change; verify against the provider's pricing page before making spending decisions. Updating the snapshot is a one-file change, and the server reads it at startup.

Ledger

record_spend appends to a local JSON ledger at ~/.agent-cost-mcp-server/ledger.json. Set AGENT_COST_LEDGER_PATH to use a different file. Nothing leaves the machine.

Background

The pricing and cost-per-success model are a port of tokenecon, an open-source library for per-task agent cost accounting (paper: doi.org/10.5281/zenodo.23195511), including a Strands integration that prices live agent runs. This server exposes the same math over MCP so any agent or IDE can call it.

Proposed for the awslabs/mcp collection in RFC #4772.

Development

uv sync --extra dev   # or: pip install -e ".[dev]"

pytest

Cite

If you use this software in your work, please cite the software itself — and, for the cost model and methods behind it, the companion preprint.

Software

@software{pandey_agent_cost_mcp_server_2026,

author = {Pandey, Karmendra}, title = {agent-cost-mcp-server}, version = {0.1.1}, doi = {10.5281/zenodo.23680824}, url = {https://github.com/karmendra8386/agent-cost-mcp-server}, date = {2026-10-26} }

Companion preprint

K. Pandey, "Token-Economics for Agentic AI: A Cost Model and Tiered Routing

Reference," Zenodo, DOI 10.5281/zenodo.23195511, Oct. 2026.

@misc{pandey_tokeneconomics_2026,

author = {Pandey, Karmendra}, title = {Token-Economics for Agentic AI: A Cost Model and Tiered Routing Reference}, doi = {10.5281/zenodo.23195511}, url = {https://zenodo.org/records/23195511}, publisher = {Zenodo}, date = {2026-10} }

A CITATION.cff is included for automated citation tooling.

License

Apache-2.0 — see LICENSE.

Agent Cost MCP Server

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Accounting, finance & taxi

Common questions

Alternatives

Finance Toolkit

SolidInvoice

Korea Tax Calculator

README from the maker

Agent Cost MCP Server

Tools

Installation

Claude Code

Claude Desktop and Cursor

VS Code

pip

From source (GitHub)

Example: what a tool call returns

Pricing snapshot

Ledger

Background

Development

Cite

License