// Analyze cloud costs, find optimization opportunities, and track anomalies using Harness CCM via MCP. Use when user says "cloud costs", "analyze costs", "cost optimization", "reduce spending", "cost report", or asks about cloud bills.
Create and update Harness AI agent instances for automated code and infrastructure tasks. Supports multi-stage execution, MCP server integration, LLM connector configuration, runtime inputs, repository cloning, and task/rules-based instruction. Use when asked to create an agent, update agent spec, modify agent configuration, automate tasks, perform agentic workflows, build autonomous systems, or work with AI agents. Trigger phrases: create agent, update agent, modify agent spec, AI agent, autonomous agent, agentic pipeline, automation task, automate workflow, Harness agent, code coverage agent, review agent, agentic task.
Execute and monitor Harness pipeline runs via MCP tools. Find pipelines, provide runtime inputs, trigger executions, monitor progress, handle approvals, retry failures, and abort running or stuck executions. Use when asked to run a pipeline, execute a deployment, trigger a build, start a pipeline, deploy a service, check execution status, approve a pipeline, or abort/stop/interrupt executions. Trigger phrases: run pipeline, execute pipeline, deploy, start build, trigger pipeline, check execution, approve deployment, retry failed pipeline, abort execution, stop pipeline, interrupt execution, kill stuck pipeline.
Create OPA governance policies for Harness via MCP. Define policies that enforce compliance rules on pipelines, services, environments, feature flags, artifacts, code repositories, templates, SBOM, security tests, Terraform, GitOps, connectors, secrets, and more. Use when asked to create, write, fix, or explain an OPA policy, Rego rule, deny rule, governance policy, compliance rule, or policy-as-code for any Harness entity. Trigger phrases: create policy, OPA policy, governance policy, compliance rule, rego policy, deny rule, enforce policy, security policy, supply chain governance.
Generate DORA metrics and engineering performance reports using Harness SEI via MCP. Track deployment frequency, lead time, change failure rate, and MTTR. Use when user says "DORA metrics", "deployment frequency", "lead time", "engineering metrics", or asks about team performance.
Manage Harness Feature Flags via MCP. Create boolean or multivariate flags, list flags by project or workspace, get flag details with environment state, toggle flags on/off per environment, and delete flags. Use when asked to create a feature flag, toggle a flag, list flags, check flag status, enable or disable a feature, or manage feature rollouts. Trigger phrases: feature flag, toggle flag, create flag, enable feature, disable feature, flag status, feature rollout, kill switch.
| name | analyze-costs |
| description | Analyze cloud costs, find optimization opportunities, and track anomalies using Harness CCM via MCP. Use when user says "cloud costs", "analyze costs", "cost optimization", "reduce spending", "cost report", or asks about cloud bills. |
| metadata | {"author":"Harness","version":"2.0.0","mcp-server":"harness-mcp-v2"} |
| license | Apache-2.0 |
| compatibility | Requires Harness MCP v2 server (harness-mcp-v2) |
Analyze cloud costs and identify savings using Harness Cloud Cost Management (CCM) via MCP.
Call MCP tool: harness_get
Parameters:
resource_type: "cost_overview"
Returns total spend, provider breakdown, and trend vs previous period.
Call MCP tool: harness_get
Parameters:
resource_type: "cost_breakdown"
Multi-dimensional breakdown by service, environment, team, region.
Call MCP tool: harness_list
Parameters:
resource_type: "cost_perspective"
Then get detailed data for a specific perspective:
Call MCP tool: harness_get
Parameters:
resource_type: "cost_perspective"
resource_id: "<perspective_id>"
Call MCP tool: harness_get
Parameters:
resource_type: "cost_timeseries"
Call MCP tool: harness_list
Parameters:
resource_type: "cost_recommendation"
For detailed recommendation info:
Call MCP tool: harness_get
Parameters:
resource_type: "cost_recommendation_detail"
resource_id: "<recommendation_id>"
Summary statistics:
Call MCP tool: harness_get
Parameters:
resource_type: "cost_recommendation_stats"
Call MCP tool: harness_list
Parameters:
resource_type: "cost_anomaly"
To dismiss a known anomaly:
Call MCP tool: harness_execute
Parameters:
resource_type: "cost_anomaly"
action: "ignore"
resource_id: "<anomaly_id>"
Call MCP tool: harness_get
Parameters:
resource_type: "cost_commitment_analysis"
Also: cost_commitment_coverage, cost_commitment_utilisation, cost_commitment_savings
## Cloud Cost Analysis
**Period:** Last 30 Days
### Summary
| Provider | Spend | vs Previous |
|----------|-------|-------------|
| AWS | $X | +X% |
| GCP | $X | +X% |
| Azure | $X | -X% |
### Top Recommendations
1. **Rightsize <resource>** - Save $X/month (95% confidence)
2. **Convert to Reserved** - Save $X/month
3. **Delete unused resources** - Save $X/month
### Anomalies
- <date>: <service> spiked $X above expected
### Actions
- [ ] Rightsize instance X
- [ ] Purchase reserved capacity
- [ ] Clean up unused volumes
| Resource Type | Operations | Description |
|---|---|---|
cost_overview | get | Total spend summary |
cost_summary | get | Filtered cost summary |
cost_timeseries | get | Cost trends over time |
cost_breakdown | get | Multi-dimensional breakdown |
cost_perspective | list, get | Custom cost views |
cost_recommendation | list | Optimization suggestions |
cost_recommendation_detail | get | Detailed recommendation |
cost_recommendation_stats | get | Summary statistics |
cost_estimated_savings | get | Savings projections |
cost_anomaly | list, get | Cost spikes |
cost_category | list, get | Cost allocation |
cost_filter_value | get | Available filter values |