BigQuery MCP Server (pvoo/bigquery-mcp) is an MCP server listed on the M8ven Trust Index. It scores 74 out of 100, grade C. It declares 5 tools. No publisher has claimed this listing.

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C
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74/100

BigQuery MCP Server

Enables LLMs to explore BigQuery datasets and tables, run safe read-only queries, and optionally perform vector search using BigQuery embeddings.

→ pvoo/bigquery-mcp· listed on glama

Limited view. Automated analysis covers part of this stack. Findings reflect what we verified. Grades reflect the full trust pyramid: code, verification depth, and reputation. New projects cap at C until adoption is earned.

Limited view: static analysis for Python is partially covered.

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Who stands behind it

pvoo

Source: Glama

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Install from

Glamanot scored here

The grade above is for the source repository. Registries can serve a different version, so we mark the ones we were not able to read.

// key findings
✅
No credential exfiltration, no sensitive file access, no obfuscation
Static analysis found nothing flowing your secrets to unexpected places.
✅
Open source with a license and README
Anyone can audit the code, the license is declared, and the publisher documents what it does.
// environment variables
To run this server yourself, you supply these values. They go in your own MCP client configuration and stay on your machine. The secret label means the value is sensitive, not that the server mishandles it.
configGCP_PROJECT_ID"": "your-project-id",
configBIGQUERY_LOCATIONexport =US
configBIGQUERY_LIST_MAX_RESULTSexport =500
configBIGQUERY_LIST_MAX_RESULTS_DETAILEDexport =25
configBIGQUERY_SAMPLE_ROWS
configBIGQUERY_SAMPLE_ROWS_FOR_STATSexport =500
configBIGQUERY_MAX_RECOMMENDED_RESULTS
configBIGQUERY_MAX_BYTES_BILLEDexport =109951162777
configBIGQUERY_VECTOR_SEARCH_ENABLEDno-vector-search =false enabled Disable vector search tools
configBIGQUERY_EMBEDDING_MODELembedding-model - Required. Full path to embedding model (project.dataset.model). Validated on startup.
configBIGQUERY_EMBEDDING_COLUMN_CONTAINSvector-column-contains embedding Pattern for finding embedding columns (column name must contain this)
configBIGQUERY_EMBEDDING_TABLESembedding-tables - Tables with embedding columns (skips auto-discovery)
configBIGQUERY_ALLOWED_DATASETSexport =dataset1,dataset2
configBIGQUERY_DISTANCE_TYPEdistance-type COSINE Distance metric: COSINE, EUCLIDEAN, DOT_PRODUCT
// quality suggestions

Tool annotations

No tools have read-only/destructive annotations

Add readOnlyHint or destructiveHint annotations to every tool so hosts can warn users before invoking.

All four hints declared on every tool

5/5 tools missing one or more hints — run_query (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); list_datasets_in_project (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); list_tables_in_dataset (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint), +2 more. OpenAI's directory rejects tools where any of the four hints are missing or non-boolean.

For every tool, set all four hints (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) to explicit true/false values that match the handler’s actual behaviour.

Tests exist

No test files found

Add tests that exercise each declared tool.

Claim the listing to review these findings one by one and send us a correction where you disagree, straight to the team. Claiming also means we tell you when the grade moves, and reach you first if we find anything urgent.

// full audit trail
The findings above are the summary. The full trail, every check we ran, each deduction, the network hosts observed and the dependency advisories, goes to verified publishers, along with an alert whenever a new one lands. Verified publishers can also review each finding and dispute it in one click. Publisher corrections have sharpened several of our checks this month, because the maintainer knows the codebase better than any scanner.
// improvement guidance — verified publishers only
We have 3 concrete improvements we can share with the publisher of this MCP. Each comes with specific guidance to raise the trust score.
// embed badge in your README
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commit: 5fddf15964eafa3b5c95c945b7f9e03cfce380fc
code hash: 138df283f3897ddef99c81e8998d122ad921071d641ec26940c93587b8c655a4
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