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Bigquery MCP Server

answering

Bigquery is answering right now. Last checked moments ago. It exposes 8 tools.

Uptime history 47 days of history
47 days agonow
100.0%
Uptime 24h
92 of 92 checks
8
Tools
read from the server
49 ms
Response time
average over 24h
open, no key
Access
streamable-http

What changed 208

Every tool that appeared, vanished or quietly changed what it asks for. Recorded since 27 August 2026. No other catalogue keeps this.

17 Sep 6 tool descriptions were rewritten12 times that day execute_sql, execute_sql_readonly, get_dataset_info and 3 more
17 Sep 2 tools changed the parameters they ask for6 times that day execute_sql, execute_sql_readonly
17 Sep 2 tools appeared4 times that day cancel_job, get_query_results
17 Sep 2 tools disappeared cancel_job, get_query_results
14 Sep 6 tool descriptions were rewritten50 times that day execute_sql, execute_sql_readonly, get_dataset_info and 3 more
14 Sep 2 tools changed the parameters they ask for46 times that day execute_sql, execute_sql_readonly
14 Sep 2 tools disappeared18 times that day cancel_job, get_query_results
14 Sep 2 tools appeared16 times that day cancel_job, get_query_results
13 Sep 6 tool descriptions were rewritten10 times that day execute_sql, execute_sql_readonly, get_dataset_info and 3 more
13 Sep 2 tools changed the parameters they ask for6 times that day execute_sql, execute_sql_readonly
and 176 more, back to 27 August 2026

Nothing serious here today

Today is the operative word: we check Bigquery every 15 minutes and re-read its code on every release. Watch it and you find out the day that stops being true.

Three servers free · no card

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 0 min ago.

run in your terminal
claude mcp add mcp --transport http https://bigquery.googleapis.com/mcp
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "mcp": {
      "url": "https://bigquery.googleapis.com/mcp"
    }
  }
}
~/.codex/config.toml
[mcp_servers.mcp]
url = "https://bigquery.googleapis.com/mcp"
.cursor/mcp.json
{
  "mcpServers": {
    "mcp": {
      "url": "https://bigquery.googleapis.com/mcp"
    }
  }
}
.vscode/mcp.json
{
  "mcpServers": {
    "mcp": {
      "url": "https://bigquery.googleapis.com/mcp"
    }
  }
}

Available tools 8

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

dataset
get_dataset_info
Get metadata information about a BigQuery dataset or BigLake namespace.
list_dataset_ids
List BigQuery dataset IDs and BigLake namespaces in a Google Cloud project. Supports pagination. Use `page_size` to limit results and `page_token` to retrieve next page.
execute
execute_sql
Run a SQL query in the project and return the result. Prefer the `execute_sql_readonly` tool if possible. This tool can execute any query that bigquery supports including: * SQL Queries (`SELECT`, `INSERT`, `UPDATE`, `DELETE`, `CREATE`, etc.) * AI/ML functions like `AI.FORECAST`, `ML.EVALUATE`, `ML.PREDICT` * Any other query that bigquery supports. Example Queries: ```sql -- Insert data into a table. INSERT INTO `my_project.my_dataset`.my_table (name, age) VALUES ('Alice', 30); -- Create a table. CREATE TABLE `my_project.my_dataset`.my_table ( name STRING, age INT64); -- DELETE data from a table. DELETE FROM `my_project.my_dataset`.my_table WHERE name = 'Alice'; -- Create Dataset CREATE SCHEMA `my_project.my_dataset` OPTIONS (location = 'US'); -- Drop table DROP TABLE `my_project.my_dataset`.my_table; -- Drop dataset DROP SCHEMA `my_project.my_dataset`; -- Create Model CREATE OR REPLACE MODEL `my_project.my_dataset.my_model` OPTIONS ( model_type = 'LINEAR_REG' LS_INIT_LEARN_RATE=0.15, L1_REG=1, MAX_ITERATIONS=5, DATA_SPLIT_METHOD='SEQ', DATA_SPLIT_EVAL_FRACTION=0.3, DATA_SPLIT_COL='timestamp') AS SELECT col1, col2, timestamp, label FROM `my_project.my_dataset.my_table`; ``` Queries executed using the `execute_sql` tool will always have the default job label `goog-mcp-server: true` automatically set in addition to any custom `labels` provided in the request. Queries are charged to the project specified in the `project_id` field. Query Execution Behavior: * If the query completes within the synchronous timeout (default 20 seconds or custom `timeout_ms`), the tool returns `job_complete: true` and the initial result rows directly. For fast queries, `job_id` may be omitted as no persistent background job is created; no further action or polling is needed. * If the query takes longer than `timeout_ms`, the tool returns `job_complete: false` and a `job_id`. In this case, use the `get_query_results` tool with `job_id` to poll until `job_complete: true`, or use `cancel_job` to abort the running query. * You can optionally specify `timeout_ms` to configure the maximum synchronous wait time in milliseconds (defaults to 20,000 ms), and `job_timeout_ms` to enforce a hard server-side timeout after which BigQuery automatically terminates the job.
execute_sql_readonly
Run a read-only SQL query in the project and return the result. Prefer this tool over `execute_sql` if possible. This tool is restricted to only `SELECT` statements. `INSERT`, `UPDATE`, and `DELETE` statements and stored procedures aren't allowed. If the query doesn't include a `SELECT` statement, an error is returned. For information on creating queries, see the [GoogleSQL documentation](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax). Example Queries: ```sql -- Count the number of penguins in each island. SELECT island, COUNT(*) AS population FROM bigquery-public-data.ml_datasets.penguins GROUP BY island -- Evaluate a bigquery ML Model. SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`) -- Evaluate BigQuery ML model on custom data SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Predict using BigQuery ML model: SELECT * FROM ML.PREDICT(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Forecast data using AI.FORECAST SELECT * FROM AI.FORECAST(TABLE `project.dataset.my_table`, data_col => 'num_trips', timestamp_col => 'date', id_cols => ['usertype'], horizon => 30) ``` Queries executed using the `execute_sql_readonly` tool will always have the job label `goog-mcp-server: true` automatically set in addition to any custom `labels` provided in the request. Queries are charged to the project specified in the `project_id` field. Query Execution Behavior: * If the query completes within the synchronous timeout (default 20 seconds or custom `timeout_ms`), the tool returns `job_complete: true` and the result rows directly. For fast queries, `job_id` may be omitted as no persistent background job is created; no further action or polling is needed. * If the query takes longer than `timeout_ms`, the tool returns `job_complete: false` and a `job_id`. In this case, use the `get_query_results` tool with `job_id` to poll until `job_complete: true`, or use `cancel_job` to abort the running query. * You can optionally specify `timeout_ms` to configure the maximum synchronous wait time in milliseconds (defaults to 20,000 ms), and `job_timeout_ms` to enforce a hard server-side timeout after which BigQuery automatically terminates the job.
table
get_table_info
Get metadata information about a BigQuery table or BigLake table.
list_table_ids
List table ids in a BigQuery dataset or BigLake namespace. Supports pagination. Use `page_size` to limit results and `page_token` to retrieve next page.
cancel
cancel_job
Cancel a running BigQuery job. Use this tool to cancel a query job that is currently executing (i.e. returned `job_complete: false` with a `job_id` from `execute_sql` or `execute_sql_readonly`). Specify the `job_id` to abort.
results
get_query_results
Get the results of a BigQuery SQL query job. Use this tool ONLY when: 1. A previous `execute_sql` or `execute_sql_readonly` call returned `job_complete: false` with a `job_id` (poll with this tool until `job_complete: true`), OR 2. You need to paginate through additional rows using `page_token` or `start_index` for a previously completed job. Do NOT call this tool if the query already returned `job_complete: true` with all rows. Supports pagination. Use `max_results` to limit results and `page_token` to retrieve the next page of results.

Endpoints

URLTransportStateLatencyChecked
https://bigquery.googleapis.com/mcp streamable-http answering 50 ms 0 min ago

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Bigquery — questions

Answers built from our own checks of this server.

What can Bigquery do?
It exposes 8 tools, read directly from the server on our last check. Among them: cancel_job, execute_sql, execute_sql_readonly, get_dataset_info, get_query_results, get_table_info and 2 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →
What is Bigquery mostly used for?
Its tools cluster around execute, dataset and table. That is what this server is built to work with — the grouping comes from the actual tool names, not from a category we assigned.
Is Bigquery working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 92 of 92 checks got a reply (100.0%), average response time 49 ms. The bar chart above shows every period we have measured.
How do I connect Bigquery?
Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does Bigquery need an API key?
No. Bigquery completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 8 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is Bigquery?
It answers our handshake in 49 ms on average, which is faster than 88% of all working MCP servers we measure. That puts it in the quick quarter of the ecosystem. The comparison comes from our own checks across the whole registry, every 15 minutes.