FOCUS MCP Server
Provides DuckDB-powered analytics engine for querying and analyzing FOCUS cloud billing data, supporting both local Parquet files and S3 storage with fast SQL query execution.
Enables analysis of Google Cloud cost and usage data through FOCUS billing format, allowing standardized cost queries and optimization insights across cloud providers.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@FOCUS MCP Servershow me the top 5 most expensive services this month"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
FOCUS MCP Server
Ask your AI assistant what your cloud actually costs. This MCP (Model Context Protocol) server connects Claude, or any other MCP client, to your FOCUS billing data, so questions like these become one-line prompts instead of hand-written SQL:
"What are my highest-cost services by region this month?"
"Show me commitment discount utilization trends."
"Which accounts have unusual spending patterns?"
"Explain the difference between BilledCost and EffectiveCost."
Under the hood, DuckDB queries your Parquet exports directly, whether they sit on local disk, S3, or GCS. There is no data warehouse to stand up. The server bundles 254 queries curated from the official FOCUS use-case catalog, as a separate collection per specification version (36 for v1.0, 41 for v1.1, 53 for v1.2, 58 for v1.3, 66 for v1.4), and each query cites the page it came from.
What is FOCUS?
FOCUS (FinOps Open Cost & Usage Specification) is the open standard for cloud billing data. AWS, Microsoft, and Google Cloud export it natively, so one schema and one set of queries work across providers.
Related MCP server: MCP Cloud Services Server
Quick start
1. Get FOCUS data
Each provider has an official export path:
AWS: FOCUS setup guide for AWS (Data Exports → FOCUS 1.0 or 1.2)
Microsoft Azure: FOCUS setup guide for Microsoft
Google Cloud: FOCUS setup guide for Google Cloud, or see GCS + BigQuery below
Other providers: all FOCUS setup guides
The server reads Parquet with Hive partitioning:
/path/to/your/focus/data/
├── billing_period=2025-05/
│ ├── file1.parquet
│ └── file2.parquet
├── billing_period=2025-06/
│ └── ...2. Run the server
The quickest path needs no container. uvx fetches and runs the published
package in one step:
claude mcp add focus -e FOCUS_DATA_LOCATION=/path/to/your/focus/data -- uvx focus-mcpFor Claude Desktop, add this to claude_desktop_config.json:
{
"mcpServers": {
"focus": {
"command": "uvx",
"args": ["focus-mcp"],
"env": {
"FOCUS_DATA_LOCATION": "/path/to/your/focus/data",
"FOCUS_VERSION": "1.0"
}
}
}
}Reading GCS with Application Default Credentials needs the gcs extra, which
uvx installs when you name it:
claude mcp add focus -e FOCUS_DATA_LOCATION=gs://your-bucket/focus \
-- uvx --from 'focus-mcp[gcs]' focus-mcpDocker remains available and is the better fit when you want a pinned, attested image:
Images are published to Docker Hub and GitHub Container Registry on every release:
docker pull glassity/focus-mcp:latest # Docker Hub
docker pull ghcr.io/glassity/focus-mcp:latest # GHCRFor Claude Code, one command:
claude mcp add focus -- docker run -i --rm \
-v /path/to/your/focus/data:/data:ro \
-e FOCUS_DATA_LOCATION=/data \
glassity/focus-mcp:latestFor Claude Desktop, add this to claude_desktop_config.json:
{
"mcpServers": {
"focus": {
"command": "docker",
"args": [
"run", "-i", "--rm",
"-v", "/path/to/your/focus/data:/data:ro",
"-e", "FOCUS_DATA_LOCATION=/data",
"-e", "FOCUS_VERSION=1.0",
"glassity/focus-mcp:latest"
]
}
}
}For S3 or GCS data, drop the volume mount and point FOCUS_DATA_LOCATION at
the bucket. See Data locations for the credential options.
3. Ask something
Start your client and try:
Show me what FOCUS data is loaded.The assistant calls get_data_info and reports row counts, date ranges, and
providers. From there, ask in plain language:
Run the service costs by region analysis for the last 3 months.
Show me the top 10 most expensive services across all accounts.
Find unused capacity reservations I can optimize.
Compare costs across providers and regions.
What columns are available in FOCUS v1.4?Tools
Eight tools, in two groups.
Data and query:
Tool | What it does |
| Inspect the loaded data: row counts, date ranges, providers |
| Browse the predefined analysis queries for your FOCUS version |
| One query in detail: SQL, parameters, citation to the spec |
| Run a predefined query or custom SQL against your data |
Schema and specification:
Tool | What it does |
| All FOCUS columns with type and requirement level |
| Full definition of one column |
| FOCUS formatting standards and conventions |
| Full requirements of one attribute |
The schema tools answer from the FOCUS specification itself, so the assistant can explain what a column means as well as query it.
Query library
Every query is extracted from the official FOCUS use-case catalog and carries a citation back to its source page:
FOCUS v1.0: 36 queries
FOCUS v1.1: 41 queries
FOCUS v1.2: 53 queries
FOCUS v1.3: 58 queries
FOCUS v1.4: 66 queries
Coverage includes cost allocation, commitment discount tracking, anomaly
detection, budget reconciliation, and provider comparison. FOCUS_VERSION
selects which set is active; match it to what your provider exports. The
specification runs ahead of the exports - AWS Data Exports currently
delivers FOCUS 1.0 and 1.2 - so the 1.3 and 1.4 collections are ready for
the day a provider ships them.
Data locations
Local files
export FOCUS_DATA_LOCATION="/path/to/your/focus/data"A downloaded copy of an AWS Data Export (aws s3 sync, a mounted volume)
is loaded through the manifests it was copied with, exactly as the bucket
itself would be — see below.
Amazon S3
export FOCUS_DATA_LOCATION="s3://your-bucket/focus-exports"
export AWS_REGION="us-west-2" # defaults to us-east-1Point the location at the export root, the prefix holding both data/ and
metadata/. AWS Data Exports list every file of their latest delivery in a
per-billing-period manifest under metadata/, and the server loads those
files when the manifests are there: superseded chunks and re-delivered
periods left behind on the prefix are ignored instead of double-counted.
Locations without manifests — a BigQuery export, a directory of Parquet
files — are read by globbing every Parquet file underneath them.
get_data_info reports which of the two was used, and the file list is
refreshed every few minutes so new deliveries need no restart.
Authentication uses the standard AWS credential chain, in order: IAM role
(automatic on EC2/ECS/Lambda), AWS_ACCESS_KEY_ID/AWS_SECRET_ACCESS_KEY
environment variables, AWS_PROFILE, then ~/.aws/credentials.
export AWS_PROFILE="billing-reader" # use a specific profileSome buckets store keys with a leading slash; if listing fails, try a double
slash after the bucket name: s3://your-bucket//focus/path.
When running in Docker, pass credentials as -e variables or mount the
profile read-only:
docker run -i --rm \
-v "$HOME/.aws:/home/mcp/.aws:ro" \
-e FOCUS_DATA_LOCATION="s3://your-bucket/focus-exports" \
-e AWS_REGION="us-west-2" \
-e AWS_PROFILE="billing-reader" \
glassity/focus-mcp:latestGoogle Cloud (GCS + BigQuery FOCUS export)
Google Cloud exports billing natively in FOCUS format: Billing → Billing export → FOCUS usage cost (Preview) writes a FOCUS table to BigQuery. Export it to Parquet in a GCS bucket:
EXPORT DATA OPTIONS (
uri = 'gs://your-bucket/focus-export/*.parquet',
format = 'PARQUET',
overwrite = true
) AS
SELECT * FROM `your-project.your_focus_dataset.your_focus_table`;Schedule that statement as a BigQuery scheduled query to keep the bucket fresh; this also archives your data past the FOCUS export's 2-year TTL. Then:
export FOCUS_DATA_LOCATION="gs://your-bucket/focus-export"Authentication is tried in this order:
HMAC keys: set
GCS_HMAC_KEY_IDandGCS_HMAC_SECRET(create withgcloud storage hmac create <service-account-email>). This path uses DuckDB's native GCS support over the S3-interoperability API and needs no extra dependencies.Application Default Credentials: included in the Docker image; from a source checkout, install the extra with
uv sync --extra gcs. Then authenticate however you normally do:gcloud auth application-default login, a service-account JSON viaGOOGLE_APPLICATION_CREDENTIALS, or workload identity on GCE/GKE.No credentials: public buckets only.
Two methods exist because DuckDB's built-in GCS support only speaks HMAC; ADC
comes from the optional gcsfs dependency. Credentials are only ever read
inside the server process and are never exposed to MCP clients.
Configuration
Variable | Default | Description |
|
| Where the Parquet lives: a local path, |
|
| FOCUS specification version: |
|
| Region for S3 access |
| (unset) | AWS profile for S3 authentication |
| (unset) | Static AWS credentials, if not using a role or profile |
| (unset) | HMAC credentials for GCS |
| (unset) | Service-account JSON for GCS via ADC |
Development
git clone https://github.com/glassity/focus-mcp.git
cd focus-mcp
uv sync # install, including dev tools
uv sync --extra gcs # + GCS ADC support
export FOCUS_DATA_LOCATION="/path/to/your/focus/data"
uv run focus-mcpPoint a client at the source checkout instead of the Docker image:
{
"mcpServers": {
"focus": {
"command": "uv",
"args": ["run", "--directory", "/path/to/focus-mcp", "focus-mcp"],
"env": {
"FOCUS_DATA_LOCATION": "/path/to/your/focus/data",
"FOCUS_VERSION": "1.0"
}
}
}
}Build and run your own image:
docker build -t focus-mcp:custom .
docker run -i --rm \
-v "/path/to/your/focus/data:/data:ro" \
-e FOCUS_DATA_LOCATION=/data \
focus-mcp:customSee CONTRIBUTING.md for conventions and checks before opening a pull request.
Roadmap
Automated query synchronization from the FOCUS specification, so new use-case pages land here without manual extraction
Richer response formatting: citations and educational context inline in query results
Validation of every use-case query against real provider exports (AWS ships FOCUS 1.0 and 1.2 today; 1.3+ as providers adopt them)
Evaluate moving column and attribute definitions to MCP resources
Surface conformance-gap notes from the spec in tool responses
Security
Report vulnerabilities to the address in SECURITY.md, not the
issue tracker. Know the trust model: execute_query runs SQL that the AI
assistant writes, inside the server process. Your Parquet files are a query
source, not a writable database, and every example here mounts them :ro;
keep that. Run the Docker image rather than a bare process if you want a hard
boundary, and give the server only the object-store credentials it needs,
scoped to the billing bucket.
License
Apache-2.0. Copyright Glassity. See LICENSE.
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