cloud_cost_ri_optimizer
Analyzes AWS and Azure cloud pricing data alongside RIPE regional demand trends to generate Reserved Instance purchase recommendations for CTOs. Inputs include target cloud provider, instance family, region, and desired commitment term. Outputs include cost savings percentage, optimal RI quantity...
This record as markdown: /tools/io-github-getgapup-mcp-knowledge/cloud-cost-ri-optimizer.md
What cloud_cost_ri_optimizer does on Mcp Knowledge
AI agents use cloud_cost_ri_optimizer to create or update resources in Mcp Knowledge, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Mcp Knowledge environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
term | string | — | |
async | boolean | — | If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client ti |
region | string | Yes | |
utilization | number | — | |
cloud_provider | string | Yes | |
instance_family | string | Yes |
Parameters from the server's own tool schema.
Why cloud_cost_ri_optimizer is rated Medium
Reserved Instance purchases are binding financial commitments that lock in cloud spending, making this Write (creates/modifies financial records and billing obligations). This is less severe than Financial (no direct money transfer) but creates enforceable spending commitments.
From the tool's definition Tool generates 'Reserved Instance purchase recommendations' which commits to financial obligations (reserved instances are prepaid commitments). Description explicitly states it outputs 'optimal RI quantity' for purchase decisions.
Attacks that exploit this kind of access
The rule that runs cloud_cost_ri_optimizer safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Mcp Knowledge, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For cloud_cost_ri_optimizer, this is the rule to start with:
cloud_cost_ri_optimizer stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Mcp Knowledge, apply this rule, and every cloud_cost_ri_optimizer call is checked against it from then on.
Questions about cloud_cost_ri_optimizer
Analyzes AWS and Azure cloud pricing data alongside RIPE regional demand trends to generate Reserved Instance purchase recommendations for CTOs. Inputs include target cloud provider, instance family, region, and desired commitment term. Outputs include cost savings percentage, optimal RI quantity, and regional demand insights. Ideal for reducing cloud spend with data-driven decisions. Keywords: cloud cost optimization, reserved instances, AWS pricing, Azure pricing, RIPE demand trends. It is categorised as a Write tool in the Mcp Knowledge MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
cloud_cost_ri_optimizer accepts 6 parameters: term, async, region, utilization, cloud_provider, instance_family. Required: region, cloud_provider, instance_family. The full parameter table on this page comes from the server's own tool schema.
Register the Mcp Knowledge MCP server in PolicyLayer and add a rule for cloud_cost_ri_optimizer: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Mcp Knowledge. Nothing to install.
cloud_cost_ri_optimizer is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the cloud_cost_ri_optimizer rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for cloud_cost_ri_optimizer. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
cloud_cost_ri_optimizer is provided by the Mcp Knowledge MCP server (https://mcp.gapup.io). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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