Verified cloud cost forecasting for AI agents. AWS, GCP, Azure pricing matrix.
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Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"io-github-maryadawson-code-openclaw-finops": {
"command": "<see-readme>",
"args": []
}
}
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Stop your AI agents from hallucinating cloud costs. Get real pricing forecasts inside the conversation.
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Stop your AI agents from hallucinating cloud costs. Get real pricing forecasts inside the conversation.
IntegrityPulse FinOps is a remote MCP server that gives AI coding agents accurate, real-time cloud deployment cost forecasts. Instead of your agent guessing that "an EC2 instance costs around $50/month," it calls a tool backed by a verified pricing matrix and returns a line-item breakdown.
One tool. Three providers. Zero hallucinations.
User: "What would it cost to run our API on AWS with an m5.large, a managed Postgres, and Redis?"
Agent (via IntegrityPulse FinOps):
| Service | Category | Hours | Est. Cost |
|----------------------------|----------|-------|-----------|
| m5.large | Compute | 730 | $70.08 |
| rds.postgres.db.m5.large | Database | 730 | $204.40 |
| elasticache.redis.t3.micro | Cache | 730 | $11.68 |
Total Estimated Monthly Cost: $286.16
Add this to your claude_desktop_config.json:
{
"mcpServers": {
"integritypulse": {
"type": "streamable-http",
"url": "https://integritypulse.marywomack.workers.dev/mcp",
"headers": {
"x-api-key": "YOUR_API_KEY"
}
}
}
}
Add to your .cursor/mcp.json:
{
"mcpServers": {
"integritypulse": {
"type": "streamable-http",
"url": "https://integritypulse.marywomack.workers.dev/mcp",
"headers": {
"x-api-key": "YOUR_API_KEY"
}
}
}
}
curl -X POST https://integritypulse.marywomack.workers.dev/mcp \
-H "Content-Type: application/json" \
-H "Accept: application/json, text/event-stream" \
-H "x-api-key: YOUR_API_KEY" \
-d '{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "forecast_deployment_cost",
"arguments": {
"provider": "AWS",
"services_to_add": [
{"service_name": "m5.large", "estimated_usage_hours": 730}
]
}
}
}'
forecast_deployment_cost| Parameter | Type | Description |
|---|---|---|
provider | "AWS" | "GCP" | "AZURE" | Cloud provider to price against |
services_to_add | Array<{ service_name, estimated_usage_hours }> | Services to include in the forecast |
Supported services:
| AWS | GCP | Azure |
|---|---|---|
| t3.micro, t3.medium, m5.large | e2-micro, e2-medium, n2-standard-2 | B1s, B2s, D2s_v3 |
| rds.postgres.db.t3.micro, rds.postgres.db.m5.large | cloudsql.postgres.db-custom-1-3840, cloudsql.postgres.db-custom-4-15360 | postgresql.flexible.b1ms |
| elasticache.redis.t3.micro | memorystore.redis.1gb | |
| s3.standard.1tb |
AI agents are moving from "write me code" to "deploy this for me." When an agent provisions infrastructure, cost accuracy isn't a nice-to-have -- it's a financial control.
The problem: LLMs hallucinate pricing. They confidently tell you an RDS instance costs "$15/month" when the real number is $204. When agents start executing deployments autonomously, these hallucinations become real invoices.
The solution: IntegrityPulse FinOps is a grounded pricing oracle that agents call as a tool. The pricing matrix is maintained, versioned, and deterministic. No generation, no guessing.
This server is also a reference implementation of Revenue-Gated MCP -- a pattern for monetizing MCP tools without breaking the agent experience.
Here's how it works: