Waste Audit
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
Waste Audit
A hosted MCP server that audits AI agent logs for wasted tokens and returns a quantified savings report.
What it does
Reads an agent's tool-call log and finds three patterns that waste context:
Redundant calls — the same tool with identical arguments, over and over. Usually a missing memoization layer.
Retry loops — the same tool called again immediately after an error. Usually a retry policy with no backoff and no cap.
Duplicate reasoning steps — the agent re-thinking a step it already resolved because the context window dropped it.
For each pattern it returns a count and a quantified estimate of wasted tokens and dollars, so you can rank fixes by ROI instead of guessing.
Related MCP server: AgentCost
Status
This is a stub. The analyze_waste tool returns a fixed example report. The payment layer uses MockPaymentProvider — it simulates the payment flow so the deployment can be tested end to end, but it does not process real charges.
The real analysis logic lives in agent-cost-auditor. Porting it into analyze_waste is the next step.
Tools
Tool | Price | What it does |
| Free | Returns the price for the audit |
| $0.50 (mocked) | Returns a quantified waste report for a log ID |
Deployment
The server runs on FastMCP Cloud (https://waste-audit.fastmcp.app/mcp) and is also listed on mcprush as a hosted endpoint.
Requires a running MCP server with fastmcp, paymcp, and mcp<2.0.0.
License
MIT
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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This server cannot be deployed
Maintenance
Related MCP Connectors
See, price, and control every tool call your AI agents make: policy checks, cost, and audit tools.
Cost per run, tool, model and day for AI agents, from one call or an OpenTelemetry export.
See AI agent token spend per project and model, and stop agents that loop or burn tokens.
1Track, analyze, and act on your streaming and SaaS subscriptions from any AI agent.
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