Agent Cost MCP Server

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

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.

Copyright 2026 Karmendra Pandey.