Cloudaware MCP - Overview
This page provides an overview of Cloudaware MCP server, covering its purpose, core capabilities, and supported use cases.
About
Cloudaware MCP (Metadata & Query Proxy) Server provides a secure AI data layer on top of your Cloudaware CMDB and Google BigQuery Export Project. It enables LLM-based tools (ChatGPT, Claude, GitHub Copilot, and others) to:
Understand the Cloudaware CMDB as a structured data model
Explore object types, fields, and relationships
Execute SQL queries against your BigQuery export
This allows AI assistants to navigate your infrastructure data, answer questions about resources and dependencies, and generate code and reports based on real Cloudaware data.
Key Capabilities
CMDB as a Self-Describing Graph
MCP exposes the Cloudaware CMDB as a self-describing graph of objects, fields, and relationships. Any compatible LLM can:
Discover object types and their API names
Inspect fields, labels, and data types
Understand how objects are related (relationship graph)
Reason about infrastructure without navigating the UI
Under the hood, tools such as search_types, search_fields, get_relationship_graph, analyze_field, and execute_query enable AI agents to follow a “discover first, query later” pattern.
Direct SQL Access to BigQuery Export
MCP provides AI with controlled SQL execution against your Cloudaware BigQuery Export Project:
Explore datasets and tables
Join infrastructure and cost/usage data
Build custom reports and dashboards
Power engineering and analytics workflows
Queries are executed using the execute_query tool against your existing Export Project and SObjects dataset.
Bring Your Own LLM
MCP is model-agnostic and integrates with:
ChatGPT
Claude Code and Claude.ai
GitHub Copilot
Gemini CLI
LM Studio and local models
Internal chatbots and AI assistants
Other MCP-enabled tools
Your teams retain their existing toolchains (IDEs, CLIs, and internal assistants), while MCP makes Cloudaware data available within those tools.
Enterprise-Grade Security
MCP is designed as a read-only execution layer operating under the authenticated identity.
AI can read metadata and execute queries only within that identity’s permissions. MCP does not introduce additional privileges
Access is scoped to the selected Export Project and SObjects dataset
MCP inherits the same permissions as the authenticated user (HTTP via Google OAuth 2.0) or the configured service account (local execution)
All access respects existing BigQuery IAM permissions
If a user does not have access to a dataset in BigQuery, MCP will not expose it to AI tools.
Use Cases
AI Resource Finder
MCP enables AI-driven resource discovery without manual UI searches. Engineers, FinOps analysts, and architects can ask an AI assistant to:
“Find all EBS volumes matching this pattern.”
“List S3 buckets in these accounts with their key properties.”
“Show all resources that match these filters.”
AI Navigator for CMDB
MCP makes CMDB self-explanatory for onboarding, troubleshooting, and in-product AI assistance scenarios:
“Explain what a specific object or field represents”
“Show where a given CI is stored”
“Find objects related to a service, account, VPC, or environment”
This is the backend capability for any AI helper embedded into the Cloudaware UI.
Inventory Research and Data Exploration
With MCP, an LLM can explore the CMDB structure, explain objects and values, and guide engineers through the data – without requiring them to first build complex list views or reports. Examples:
“Analyze my inventory and show what exists for this application”
“Compare these sets of resources under the given conditions”
“Suggest where to look for this CI or attribute”
Developer Automation Accelerator
Before MCP, developers typically needed to:
Study CMDB SObjects and their relationships
Build ETL jobs or many API calls
Manually discover which fields to use in scripts
With MCP, an AI assistant can:
Generate Python scripts that interact with Cloudaware APIs or BigQuery
Select the correct objects and fields based on metadata
Compose queries and joins that follow the actual relationship graph
This reduces the time to build automation from weeks or months of discovery to minutes of interaction with an AI tool.
Auto-documentation and CMDB Intelligence
MCP enables AI-driven documentation of your data model:
Document structures of CMDB objects
Describe field purpose and semantics
Build dependency graphs between services and resources
This is especially valuable for complex, long-running environments and for onboarding new team members.
! Next step – check Cloudaware MCP - Configuration for setup instructions, supported HTTP transport modes, and system requirements