About agent-costcenter
agent-costcenter is an MCP server for attributing AI agent costs and managing budgets across an agent fleet. It combines call logging, model pricing, scoped reports, spending alerts, and anomaly detection.
Key features
- 01Track LLM and tool-call costs by agent
- 02Built-in pricing for 11 models
- 03Set budgets and warn or block on limits
- 04Reports by agent, team, or project
- 05Detect spending spikes
- 06Compare model cost efficiency
Use cases
- 01Find the highest-cost agents
- 02Monitor budgets across teams and projects
- 03Investigate unusual spending increases
- 04Compare costs across models
MCP support
Exposes MCP tools for registering agents, recording LLM and tool-call costs, checking budgets, generating reports, detecting anomalies, and comparing models. It also provides summary and pricing resources.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEagent-costcenter-mcp
MCP server for per-agent cost attribution and budget management. Answer "which agent costs the most?" across your entire AI agent fleet.
Features
- Per-agent cost tracking — register agents, log LLM and tool calls, get per-agent spend
- Built-in pricing table — 11 models (Claude, GPT, Gemini, DeepSeek) with fuzzy name matching
- Budget management — set caps and alerts, check remaining budget, warn or block on overspend
- Cost reports — breakdown by agent, team, project, or all; includes model-level detail and percentages
- Anomaly detection — automatically flag agents with 2x+ spending spikes
- Model comparison — compare cost-per-1K-tokens across all models used
Quick Start
npx agent-costcenter-mcp
Claude Desktop
{
"mcpServers": {
"agent-costcenter": {
"command": "npx",
"args": ["agent-costcenter-mcp"]
}
}
}
Tools
| Tool | Description |
|---|---|
register_agent | Register agent with team, project, model, and budget cap |
log_llm_call | Log an LLM call — auto-calculates cost from pricing table |
log_tool_call | Log a tool/API call cost |
get_cost_report | Cost breakdown by agent, team, project, or all |
set_budget_alert | Set spending threshold (warn or block) |
check_budget | Check if agent is within budget |
get_cost_anomalies | Find agents with unusual spending spikes |
compare_models | Compare cost efficiency across models |
Resources
| URI | Description |
|---|---|
costcenter://summary | Current total spend across all agents |
costcenter://pricing | Built-in model pricing table |
Built-in Pricing (per 1M tokens)
| Model | Input | Output |
|---|---|---|
| claude-opus-4 | $15.00 | $75.00 |
| claude-sonnet-4 | $3.00 | $15.00 |
| claude-haiku-4 | $0.80 | $4.00 |
| gpt-4o | $2.50 | $10.00 |
| gpt-4o-mini | $0.15 | $0.60 |
| gpt-4.1 | $2.00 | $8.00 |
| gpt-4.1-mini | $0.40 | $1.60 |
| gemini-2.5-pro | $1.25 | $10.00 |
| gemini-2.5-flash | $0.15 | $0.60 |
| deepseek-v3 | $0.27 | $1.10 |
| deepseek-r1 | $0.55 | $2.19 |
Model names are fuzzy-matched (case-insensitive, strips version suffixes).
Example Workflow
1. register_agent(agent_id="researcher", team="discovery", model="claude-sonnet-4", budget_cap_usd=10)
2. log_llm_call(agent_id="researcher", model="claude-sonnet-4", input_tokens=5000, output_tokens=2000)
3. log_tool_call(agent_id="researcher", tool_name="web_search", duration_ms=1200, cost_usd=0.01)
4. check_budget(agent_id="researcher")
5. get_cost_report(scope="all")
6. get_cost_anomalies()
7. compare_models()
Development
npm install
npm test
npm run dev # watch mode
License
MIT
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.