Install mcp-bigquery from PyPI
Install exact version 0.7.1. The executable name has not been verified, so it is intentionally not guessed.
python -m pip install 'mcp-bigquery==0.7.1'MCP server intelligence profile
BigQuery Validator MCP Server
Enables validation and dry-run analysis of BigQuery SQL queries without execution. Provides cost estimates, schema previews, and syntax validation for BigQuery queries
1Distribution channel
9Independently observed tools
0Linked remote endpoints
AvailableVersion intelligence
Detailed security scan evidence is not public for this MCP yet. Public identity, registry metadata, and independently observed protocol inventory remain available.
Install and connect
Installation and connection instructions are shown only when supported by retained package, repository, or endpoint evidence.
Identity
| Canonical slug | bigquery-validator-4aecc9e9 | Deployment | Local Only |
|---|---|---|---|
| Canonical package | pypi:mcp-bigquery | Repository | caron14/mcp-bigquery |
| First published | Aug 17, 2026 | Latest release | Aug 17, 2026 |
| Last security verification | — | Classification confidence | 90% |
| Publication | Draft | Official distribution | Not verified |
Distributions
| Channel | Identifier | Current version | Versions | Source |
|---|---|---|---|---|
| pypi | mcp-bigquery | 0.7.1 | 1 | Repository |
Current release
| Package | Version | Published / observed | Inventory | Security scan |
|---|---|---|---|---|
| pypimcp-bigquery | 0.7.1Current | Aug 17, 2026 | 9 toolsSucceeded · 0 resources · 0 prompts | Evidence restricted |
Enterprise protection
Continuously monitor this MCP for security risk
Independently scan the exact version your agents use, receive alerts when its risk changes, and investigate every finding with retained version evidence.
- Independent exact-version security scans
- Continuous release and vulnerability monitoring
- Risk-change alerts with capability context
- Historical evidence and API exports
Custom pricingContact salesTailored to your organization, integrations, data needs, and support requirements.
Current version evidence
No public current-version evidence is available yet.
Current protocol inventory
2025-06-18Negotiated protocol
mcp-bigqueryServer-reported name
2Capability groups
Aug 23, 2026Observed
Tools 9
| Tool | Category | Annotations | Risk |
|---|---|---|---|
bq_describe_tableGet table schema, metadata, and statisticsInput schema{
"type": "object",
"properties": {
"table_id": {
"type": "string",
"description": "The table ID"
},
"dataset_id": {
"type": "string",
"description": "The dataset ID"
},
"project_id": {
"type": "string",
"description": "GCP project ID (uses default if not provided)"
},
"format_output": {
"type": "boolean",
"description": "Whether to format schema as table string"
}
},
"required": [
"table_id",
"dataset_id"
]
} | — | — | |
bq_dry_run_sqlPerform a dry-run of a BigQuery SQL query to get cost estimates and metadataInput schema{
"type": "object",
"properties": {
"sql": {
"type": "string",
"description": "The SQL query to dry-run"
},
"params": {
"type": "object",
"description": "Optional query parameters (key-value pairs)",
"additionalProperties": true
},
"pricePerTiB": {
"type": "number",
"description": "Price per TiB for cost estimation (defaults to env var or 5.0)"
}
},
"required": [
"sql"
]
} | — | — | |
bq_extract_dependenciesExtract table and column dependencies from BigQuery SQLInput schema{
"type": "object",
"properties": {
"sql": {
"type": "string",
"description": "The SQL query to analyze"
},
"params": {
"type": "object",
"description": "Optional query parameters (key-value pairs)",
"additionalProperties": true
}
},
"required": [
"sql"
]
} | — | — | |
bq_get_table_infoGet comprehensive table information including partitioning and clusteringInput schema{
"type": "object",
"properties": {
"table_id": {
"type": "string",
"description": "The table ID"
},
"dataset_id": {
"type": "string",
"description": "The dataset ID"
},
"project_id": {
"type": "string",
"description": "GCP project ID (uses default if not provided)"
}
},
"required": [
"table_id",
"dataset_id"
]
} | — | — | |
bq_list_datasetsList all datasets in the BigQuery projectInput schema{
"type": "object",
"properties": {
"project_id": {
"type": "string",
"description": "GCP project ID (uses default if not provided)"
},
"max_results": {
"type": "integer",
"description": "Maximum number of datasets to return"
}
}
} | — | — | |
bq_list_tablesList all tables in a BigQuery dataset with metadataInput schema{
"type": "object",
"properties": {
"dataset_id": {
"type": "string",
"description": "The dataset ID"
},
"project_id": {
"type": "string",
"description": "GCP project ID (uses default if not provided)"
},
"max_results": {
"type": "integer",
"description": "Maximum number of tables"
},
"table_type_filter": {
"type": "array",
"items": {
"type": "string"
},
"description": "Filter by table types (TABLE, VIEW, EXTERNAL, MATERIALIZED_VIEW)"
}
},
"required": [
"dataset_id"
]
} | — | — | |
bq_preview_tableGet a preview of table data without running a query job (cost-free)Input schema{
"type": "object",
"properties": {
"dataset_id": {
"type": "string",
"description": "The dataset ID"
},
"table_id": {
"type": "string",
"description": "The table ID"
},
"project_id": {
"type": "string",
"description": "GCP project ID (uses default if not provided)"
},
"max_results": {
"type": "integer",
"description": "Maximum number of rows to preview (default: 5, hard limit: 10)"
}
},
"required": [
"dataset_id",
"table_id"
]
} | — | — | |
bq_validate_query_syntaxEnhanced syntax validation with detailed error reportingInput schema{
"type": "object",
"properties": {
"sql": {
"type": "string",
"description": "The SQL query to validate"
},
"params": {
"type": "object",
"description": "Optional query parameters (key-value pairs)",
"additionalProperties": true
}
},
"required": [
"sql"
]
} | — | — | |
bq_validate_sqlValidate BigQuery SQL syntax without executing the queryInput schema{
"type": "object",
"properties": {
"sql": {
"type": "string",
"description": "The SQL query to validate"
},
"params": {
"type": "object",
"description": "Optional query parameters (key-value pairs)",
"additionalProperties": true
}
},
"required": [
"sql"
]
} | — | — |
Resources 0
- None observed.
Resource templates 0
- None observed.
Prompts 0
- None observed.
Remote endpoints
| Endpoint | Transport | Authentication | Health | Observed |
|---|---|---|---|---|
| No verified remote endpoint is linked. | ||||
BigQuery Validator MCP Server questions
How do I install BigQuery Validator MCP Server?
Install the selected package version with: python -m pip install 'mcp-bigquery==0.7.1'
What tools does BigQuery Validator MCP Server provide?
BigQuery Validator MCP Server exposed 9 tools during independent protocol observation, including bq_describe_table, bq_dry_run_sql, bq_extract_dependencies, bq_get_table_info, bq_list_datasets, bq_list_tables, bq_preview_table, bq_validate_query_syntax, and others.
Is BigQuery Validator MCP Server secure?
The selected current version does not yet have completed public verification. Unknown does not mean clean or vulnerable.
Company and product intelligence
These internal links are derived from strong identity fields such as the implementation name, package, repository, vendor, and listing name—not generic description prose.
Associated company landscape
Google intelligence →
Association is based on retained identity fields; it does not by itself prove first-party publication.
Explore related MCP server guides
Curated product and capability guides containing this catalog record.