bq-guard-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| BQ_GUARD_POLICY | No | Path to the bq-guard policy YAML file. If not set, the policy is looked up via --policy, $BQ_GUARD_POLICY, or ./bq-guard.yaml. Defaults to the built-in policy. | |
| BQ_GUARD_AUDIT_LOG | No | Path to the audit log file. Overrides the audit_log setting in the policy file. Set to null to disable audit logging. | |
| GOOGLE_APPLICATION_CREDENTIALS | No | Path to a Google Cloud service account key JSON file. Used for Application Default Credentials on servers. Not needed locally if gcloud auth application-default login has been run. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_datasetsA | List datasets the policy allows. project defaults to the configured project. |
| list_tablesB | List tables in a dataset, given as |
| describe_tableA | Schema, row count, size, partitioning, and clustering for |
| dry_runA | Estimate bytes scanned for one statement without running it, and report whether run_query would allow it and why not. |
| run_queryA | Run one statement after the policy checks and a dry run. Returns at most the row cap;
|
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Each tool targets a distinct action: listing datasets, listing tables, describing a table, dry-running a query, and running a query. There is no overlap or ambiguity between them.
All tool names follow a consistent verb_noun snake_case pattern (list_datasets, list_tables, describe_table, dry_run, run_query). The naming is predictable and uniform.
Five tools is well-scoped for a BigQuery guard server, covering metadata exploration and query execution without excess or redundancy. Each tool earns its place.
The surface covers the core workflow: discover datasets, browse tables, inspect schema, estimate cost, and run queries. Minor gaps like dataset-level metadata or query cancellation are not essential for the stated purpose.