mcp server
io.github.alexpota/cloudscope
Azure + GCP cost management: spending, forecasts, anomalies, budgets, idle resources, and tags.
Description as published by the maintainer. Source
- version 0.3.3
- active
active — Most recent push to the repository was 2026-08-04.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| GitHub stars | 1 | Number of GitHub accounts that bookmarked this repository since it was created. It is a bookmark count, not installs, not active users and not quality. | cumulative, all time | GitHub | |
| Last commit | 2026-08-04 | Date of the most recent push to any branch. This is the strongest cheap indicator of whether the project is still maintained. | point in time | GitHub | |
| Open issues | 11 | Open issues plus open pull requests, as GitHub counts them together. A high number can mean an active project or an abandoned one. | as of fetch | GitHub | |
| Latest published version | 0.3.3 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-05-29 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| License | MIT | Licence GitHub detected in the repository. Detection can be wrong; the LICENSE file is authoritative. | as of fetch | GitHub | |
| First listed in the MCP Registry | 2026-05-29 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| repository status | active | The repository exists on GitHub and is not archived. This says nothing about how recently it was worked on. | as of fetch | GitHub |
Where to get it
Related, by what their authors tagged them
-
nable
— last commit 2026-08-06, shares anomaly-detection, azure, cloud-cost
Local-first FinOps copilot: ask AWS, Azure, GCP, Kubernetes and SaaS cost questions in your editor.
-
Cloudprice
— last commit 2026-08-02, shares azure, cloud-cost, cost-management
Compare on-demand compute + storage pricing across AWS, Azure, and GCP. Bulk workload compare.
-
Jitsu
— last commit 2026-08-06, shares bigquery
Manage Jitsu data pipelines: destinations, streams, connections, functions, live events.
-
Control Plane
— last commit 2026-08-05, shares multi-cloud
Control Plane (controlplane.com): deploy and operate workloads across AWS, GCP, Azure, and more.
-
Boyce
— last commit 2026-04-28, shares bigquery
Deterministic SQL compiler for AI agents. Your agent stops guessing SQL.
-
com.fahaliai/fahali
— last commit 2026-07-24, shares anomaly-detection
Market-risk for AI agents: verified lead time, signed receipts, judged record. Not advice.
-
com.mnemopay/sdk
— last commit 2026-06-26, shares anomaly-detection
Memory + wallet for AI agents. Real payment rails, Agent FICO 300-850, Merkle audit, identity.
-
ThinkNEO Control Plane
— last commit 2026-07-06, shares finops
Enterprise AI Control Plane: governance, guardrails, spend tracking, compliance & smart routing.
-
Aiqbee Brain
— last commit 2026-03-07, shares azure
AI knowledge graph for architecture, portfolio, and digital strategy management.
-
Azure Boards MCP
— last commit 2026-06-25, shares azure
Azure DevOps Boards work items over MCP (stdio). Requires Bun, org, project, and PAT.
These share tags the maintainers applied themselves, such as anomaly-detection, azure, cloud-cost, cost-management. Common tags like "mcp" or "ai" are ignored for this: agreeing with six hundred other projects is not a similarity.
This is not a recommendation and not a test result. It is a map of what the authors said their work is about.
How the author describes it
Topics the maintainer set on GitHub: anomaly-detection, azure, azure-advisor, bigquery, budget-monitoring, claude, claude-code, cloud-billing, cloud-cost, cost-management, cost-optimization, cursor, finops, gcp, gcp-recommender, google-cloud, mcp, mcp-server, model-context-protocol, multi-cloud.
Bring your own setup
We take apart real AI setups every week and show what broke, what cost too much, and what the trace actually said. If you run agents on real work, that is where the useful conversation is.
Join ZBS AI Practice Lab