# How to connect Google BigQuery to Muse

```json
{
  "title": "How to connect Google BigQuery to Muse",
  "toolkit": "Google BigQuery",
  "toolkit_slug": "googlebigquery",
  "framework": "Muse",
  "framework_slug": "muse",
  "url": "https://composio.dev/toolkits/googlebigquery/framework/muse",
  "markdown_url": "https://composio.dev/toolkits/googlebigquery/framework/muse.md",
  "updated_at": "2026-10-05T05:41:39.775Z"
}
```

## Introduction

Muse is Meta's AI assistant. It can connect to outside tools through custom connectors, which use the Model Context Protocol (MCP).
This guide explains how to connect Google BigQuery to Muse using Composio Connect, which handles OAuth, token refresh, and reliability so you do not have to. You add Composio as a custom connector once, then ask Muse to work with Google BigQuery in plain language.

## Also integrate Google BigQuery with

- [Claude Code](https://composio.dev/toolkits/googlebigquery/framework/claude-code)
- [Cursor](https://composio.dev/toolkits/googlebigquery/framework/cursor)
- [ChatGPT](https://composio.dev/toolkits/googlebigquery/framework/chatgpt)
- [Claude Cowork](https://composio.dev/toolkits/googlebigquery/framework/claude-cowork)
- [Antigravity](https://composio.dev/toolkits/googlebigquery/framework/antigravity)
- [OpenAI Agents SDK](https://composio.dev/toolkits/googlebigquery/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/googlebigquery/framework/claude-agents-sdk)
- [Codex](https://composio.dev/toolkits/googlebigquery/framework/codex)
- [Kimi Code](https://composio.dev/toolkits/googlebigquery/framework/kimi)
- [Grok Build](https://composio.dev/toolkits/googlebigquery/framework/grok-build)
- [VS Code](https://composio.dev/toolkits/googlebigquery/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/googlebigquery/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/googlebigquery/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/googlebigquery/framework/hermes-agent)
- [Atomic Agent](https://composio.dev/toolkits/googlebigquery/framework/atomic-agent)
- [CLI](https://composio.dev/toolkits/googlebigquery/framework/cli)
- [Google ADK](https://composio.dev/toolkits/googlebigquery/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/googlebigquery/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/googlebigquery/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/googlebigquery/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/googlebigquery/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/googlebigquery/framework/crew-ai)
- [Pydantic AI](https://composio.dev/toolkits/googlebigquery/framework/pydantic-ai)
- [AutoGen](https://composio.dev/toolkits/googlebigquery/framework/autogen)

## TL;DR

- Cross-app workflows across 1,500+ apps from a single plugin layer.
- On-demand tool loading so Muse only loads the tools needed for the current task.
- Programmatic tool calling through a remote workbench for complex, multi-step workflows.
- Multi-account support so users can connect and switch between multiple accounts for the same app.
- Shared account access so teams or agents can work through common business accounts and service connections.
- Managed authentication with OAuth handling, credential storage, and token refresh managed by Composio.
- Safer credential handling so app credentials stay outside the model context and tool responses.
- User-level account isolation so actions run against the correct connected account instead of mixing user access.

## Connect Google BigQuery to Muse

### Connecting Google BigQuery to Muse
You add Composio to Muse once as a custom connector, then connect Google BigQuery from the chat.

## What is the Google BigQuery MCP server, and what's possible with it?

The Google BigQuery MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Google BigQuery account. It provides structured and secure access to your projects, datasets, tables, jobs, routines, and machine learning models, so your agent can organize data, inspect schemas, load records, run query jobs, and retrieve results on your behalf.
- Dataset and table management: Have your agent create, inspect, update, list, or delete datasets and tables, and restore deleted datasets within their recovery window.
- Schema and row inspection: Let the agent fetch table schemas, metadata, and stored rows to understand the structure and contents of your data.
- Data loading and ingestion: Direct your agent to stream individual records into tables or start load jobs with uploaded files.
- Query and job operations: Instruct your agent to start query, load, extract, or copy jobs, check their status, list recent jobs, and retrieve query results.
- Routines, models, and data listings: Have your agent create routines and Analytics Hub listings, or list, update, and delete BigQuery ML models.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `GOOGLEBIGQUERY_CANCEL_JOB` | Cancel BigQuery Job | Tool to cancel a running BigQuery job. This call returns immediately, and you need to poll for the job status to see if the cancel completed successfully. Note that cancelled jobs may still incur costs. |
| `GOOGLEBIGQUERY_CREATE_CAPACITY_COMMITMENT` | Create Capacity Commitment | Tool to create a new capacity commitment resource in BigQuery Reservation. Use when you need to purchase compute capacity (slots) with a committed period of usage for BigQuery jobs. Supports various commitment plans (FLEX, MONTHLY, ANNUAL, THREE_YEAR) and editions (STANDARD, ENTERPRISE, ENTERPRISE_PLUS). |
| `GOOGLEBIGQUERY_CREATE_CONNECTION` | Create BigQuery Connection | Tool to create a new BigQuery connection to external data sources using the BigQuery Connection API. Use when setting up connections to AWS, Azure, Cloud Spanner, Cloud SQL, Salesforce DataCloud, or Apache Spark. |
| `GOOGLEBIGQUERY_CREATE_DATA_EXCHANGE` | Create Analytics Hub Data Exchange | Tool to create a new Analytics Hub data exchange for sharing BigQuery datasets. Use when you need to set up a container for data sharing with descriptive information and listings. |
| `GOOGLEBIGQUERY_CREATE_DATAEXCHANGES_LISTINGS` | Create Analytics Hub Listing | Tool to create a new listing in a BigQuery Analytics Hub data exchange. Use when you need to share a BigQuery dataset with specific subscribers or make it available for discovery. The dataset must exist and be in the same region as the data exchange. |
| `GOOGLEBIGQUERY_CREATE_DATASET` | Create BigQuery Dataset | Tool to create a new BigQuery dataset with explicit location, labels, and description using the BigQuery Datasets API. Use when the workflow needs to set up a staging/warehouse dataset and correctness of region is critical to avoid downstream job location mismatches. Surfaces 409 Already Exists errors cleanly without retrying. |
| `GOOGLEBIGQUERY_CREATE_LISTING` | Create Analytics Hub Listing | Tool to create a new listing in a data exchange using Analytics Hub API. Use when publishing a BigQuery dataset to make it available for subscription by other users or organizations. |
| `GOOGLEBIGQUERY_CREATE_LOCATIONS_DATAPOLICIES` | Create BigQuery Data Policy (v2beta1) | Tool to create a new data policy under a project with specified location using the v2beta1 BigQuery Data Policy API. Use when you need to set up data masking rules or column-level security for sensitive data. The v2beta1 endpoint uses a nested request structure. |
| `GOOGLEBIGQUERY_CREATE_QUERY_TEMPLATE` | Create Analytics Hub Query Template | Tool to create a new query template in a BigQuery Analytics Hub Data Clean Room (DCR) data exchange. Use when you need to define predefined and approved queries for data clean room use cases. Query templates must be created in DCR data exchanges only. |
| `GOOGLEBIGQUERY_CREATE_RESERVATION` | Create BigQuery Reservation | Tool to create a new BigQuery reservation resource to guarantee compute capacity (slots) for query and pipeline jobs. Use when you need to reserve dedicated compute resources for predictable performance and cost management. Reservations can be configured with autoscaling, concurrency limits, and edition-based features. |
| `GOOGLEBIGQUERY_CREATE_RESERVATION_ASSIGNMENT` | Create BigQuery Reservation Assignment | Tool to create a BigQuery reservation assignment that allows a project, folder, or organization to submit jobs using slots from a specified reservation. Use when setting up resource allocation for BigQuery workloads. Note: A resource can only have one assignment per (job_type, location) combination. |
| `GOOGLEBIGQUERY_CREATE_ROUTINE` | Create BigQuery Routine | Tool to create a new user-defined routine (function or procedure) in a BigQuery dataset. Use when you need to define SQL, JavaScript, Python, Java, or Scala functions/procedures for reusable logic, data transformations, or custom masking. Supports scalar functions, table-valued functions, procedures, and aggregate functions with comprehensive type definitions. |
| `GOOGLEBIGQUERY_CREATE_TABLE` | Create BigQuery Table | Tool to create a new, empty table in a BigQuery dataset. Use when setting up data infrastructure for standard tables, external tables, views, or materialized views. Supports partitioning, clustering, and encryption configuration. |
| `GOOGLEBIGQUERY_DELETE_DATASET` | Delete BigQuery Dataset | Tool to delete a BigQuery dataset specified by datasetId via the datasets.delete API. Before deletion, you must delete all tables unless deleteContents=True is specified. Use when cleaning up test datasets or removing unused data warehouses. Immediately after deletion, you can create another dataset with the same name. |
| `GOOGLEBIGQUERY_DELETE_JOB_METADATA` | Delete BigQuery Job Metadata | Tool to delete the metadata of a BigQuery job. Use when you need to remove job metadata from the system. If this is a parent job with child jobs, metadata from all child jobs will be deleted as well. |
| `GOOGLEBIGQUERY_DELETE_MODEL` | Delete BigQuery ML Model | Tool to delete a BigQuery ML model from a dataset. Use when you need to remove a trained machine learning model permanently. The operation deletes the model and cannot be undone. |
| `GOOGLEBIGQUERY_DELETE_ROUTINE` | Delete BigQuery Routine | Tool to delete a BigQuery routine by its ID. Use when you need to remove a stored procedure, user-defined function, or table function from a dataset. This operation is irreversible. |
| `GOOGLEBIGQUERY_DELETE_TABLE` | Delete BigQuery Table | Tool to delete a BigQuery table from a dataset. Use when you need to remove a table and all its data permanently. The operation deletes all data in the table and cannot be undone. |
| `GOOGLEBIGQUERY_GET_BIGQUERY_MODEL` | Get BigQuery ML Model | Tool to retrieve a specific BigQuery ML model resource by model ID. Use when you need detailed information about a trained machine learning model including its configuration, training runs, hyperparameters, and evaluation metrics. |
| `GOOGLEBIGQUERY_GET_CONNECTION_IAM_POLICY` | Get BigQuery Connection IAM Policy | Tool to get the IAM access control policy for a BigQuery connection resource. Returns an empty policy if the resource exists but has no policy set. Use this to check who has access to a specific connection before modifying permissions. |
| `GOOGLEBIGQUERY_GET_DATASET` | Get BigQuery Dataset Metadata | Tool to retrieve BigQuery dataset metadata including location via the datasets.get API. Use this before creating jobs/queries if the workflow has been failing with location mismatch to confirm the dataset's region and correct the job location accordingly. |
| `GOOGLEBIGQUERY_GET_JOB` | Get BigQuery Job | Tool to retrieve information about a specific BigQuery job. Returns job configuration, status, and statistics. Use this to check job status after running queries or to get details about job execution. |
| `GOOGLEBIGQUERY_GET_QUERY_RESULTS` | Get BigQuery Query Results | Tool to get the results of a BigQuery query job via RPC. Use this to retrieve results after running a query, or to check job completion status and fetch paginated results. |
| `GOOGLEBIGQUERY_GET_ROUTINE` | Get BigQuery Routine | Tool to retrieve a BigQuery routine (user-defined function or stored procedure) by its ID. Use to inspect routine definitions, arguments, return types, and metadata. |
| `GOOGLEBIGQUERY_GET_ROUTINE_IAM_POLICY` | Get BigQuery Routine IAM Policy | Tool to retrieve the IAM access control policy for a BigQuery routine resource. Returns an empty policy if the routine exists but has no policy set. Use this to check current access permissions before modifying them. |
| `GOOGLEBIGQUERY_GET_SERVICE_ACCOUNT` | Get BigQuery Service Account | Tool to get the service account for a project used for interactions with Google Cloud KMS. Use when you need to retrieve the BigQuery service account email for KMS encryption configuration or key access permissions. |
| `GOOGLEBIGQUERY_GET_TABLE_IAM_POLICY` | Get BigQuery Table IAM Policy | Tool to retrieve the IAM access control policy for a BigQuery table resource. Returns an empty policy if the resource exists but has no policy set. Use this to check current access permissions before modifying them. |
| `GOOGLEBIGQUERY_GET_TABLE_SCHEMA` | Get BigQuery Table Schema | Tool to fetch a BigQuery table's schema and metadata without querying row data. Use before generating SQL queries to avoid column name typos and confirm field types and nullable modes. This is especially useful when INFORMATION_SCHEMA access is restricted. |
| `GOOGLEBIGQUERY_INSERT_ALL` | Insert Data into BigQuery Table | Tool to stream data into BigQuery one record at a time without running a load job. Use when you need immediate data availability or inserting small batches. Supports row-level deduplication via insertId and error handling via skipInvalidRows. |
| `GOOGLEBIGQUERY_INSERT_JOB` | Insert BigQuery Job | Tool to start a new asynchronous BigQuery job (query, load, extract, or copy). Use when you need to run a query as a job, load data from Cloud Storage, extract table data to GCS, or copy tables. For dry-run validation without execution, set dryRun to true in configuration. |
| `GOOGLEBIGQUERY_INSERT_JOB_WITH_UPLOAD` | Insert BigQuery Job with Upload | Tool to start a new BigQuery load job with file upload. Uploads a file (CSV, JSON, etc.) and loads it into a BigQuery table in a single operation. Use when you need to upload data from a local file directly to BigQuery rather than loading from Cloud Storage. |
| `GOOGLEBIGQUERY_LIST_ANALYTICS_HUB_LISTINGS` | List Analytics Hub Listings | Tool to list all listings in a given Analytics Hub data exchange. Use when you need to discover available data listings within a specific data exchange that can be subscribed to for data sharing. |
| `GOOGLEBIGQUERY_LIST_BIG_QUERY_CONNECTIONS` | List BigQuery Connections | Tool to list BigQuery connections in a given project and location. Use when you need to discover available external data source connections (Cloud SQL, AWS, Azure, Spark, etc.) configured for BigQuery. |
| `GOOGLEBIGQUERY_LIST_CAPACITY_COMMITMENTS` | List BigQuery Capacity Commitments | Tool to list all capacity commitments for the admin project. Use when you need to view purchased compute capacity slots and their commitment details (plan, state, duration). |
| `GOOGLEBIGQUERY_LIST_DATAEXCHANGES_LISTINGS` | List Data Exchange Listings | Tool to list all listings in a given Analytics Hub data exchange using the v1beta1 API. Use when you need to discover available data listings within a specific data exchange that can be subscribed to for data sharing. |
| `GOOGLEBIGQUERY_LIST_DATASETS` | List BigQuery Datasets | Tool to list datasets in a specific BigQuery project, including dataset locations. Use after identifying an accessible project to discover available datasets and their locations before querying. The dataset location is critical for avoiding location-related query/job errors. |
| `GOOGLEBIGQUERY_LIST_JOBS` | List BigQuery Jobs | Tool to list all jobs that you started in a BigQuery project. Job information is available for a six month period after creation. Jobs are sorted in reverse chronological order by creation time. Use to monitor query execution, track job statuses, and retrieve job history. |
| `GOOGLEBIGQUERY_LIST_LOCATIONS` | List BigQuery Data Transfer Locations | Tool to list information about supported locations for BigQuery Data Transfer Service. Use when you need to discover available regions/locations where BigQuery Data Transfer operations can be performed. |
| `GOOGLEBIGQUERY_LIST_LOCATIONS_CONNECTIONS` | List Connections in Location | Tool to list BigQuery connections in a given project and location using the v1beta1 API. Use when you need to discover available external data source connections (Cloud SQL, AWS, Azure, Spark, etc.) configured for BigQuery in a specific location. |
| `GOOGLEBIGQUERY_LIST_LOCATIONS_DATAPOLICIES` | List BigQuery Location Data Policies | Tool to list all data policies in a specified parent project and location using the v2beta1 API. Use when you need to discover data masking policies and column-level security policies configured for BigQuery datasets. |
| `GOOGLEBIGQUERY_LIST_MODELS` | List BigQuery Models | Tool to list all BigQuery ML models in a specified dataset. Requires READER dataset role. Use this to discover available models before getting detailed information via models.get method. |
| `GOOGLEBIGQUERY_LIST_ORGANIZATION_DATA_EXCHANGES` | List Organization Data Exchanges | Tool to list all data exchanges from projects in a given organization and location using Analytics Hub API. Use when you need to discover available data exchanges within an organization that can be used for data sharing. |
| `GOOGLEBIGQUERY_LIST_PROJECTS` | List BigQuery Projects | Tool to list BigQuery projects to which the user has been granted any project role. Returns projects with at least READ access. For enhanced capabilities, consider using the Resource Manager API. |
| `GOOGLEBIGQUERY_LIST_QUERY_TEMPLATES` | List Analytics Hub Query Templates | Tool to list all query templates in a given Analytics Hub data exchange. Use when you need to discover available query templates that define predefined and approved queries for data clean room use cases. |
| `GOOGLEBIGQUERY_LIST_RESERVATION_ASSIGNMENTS` | List BigQuery Reservation Assignments | Tool to list BigQuery reservation assignments. Only explicitly created assignments will be returned (no expansion or merge happens). Use wildcard "-" in parent path to list assignments across all reservations in a location. |
| `GOOGLEBIGQUERY_LIST_RESERVATION_GROUPS` | List BigQuery Reservation Groups | Tool to list all BigQuery reservation groups for a project in a specified location. Use when you need to discover available reservation groups which serve as containers for reservations. |
| `GOOGLEBIGQUERY_LIST_RESERVATIONS` | List BigQuery Reservations | Tool to list all BigQuery reservations for a project in a specified location. Use when you need to discover available reservations or view reservation details including slot capacity and autoscale configuration. |
| `GOOGLEBIGQUERY_LIST_ROUTINES` | List BigQuery Routines | Tool to list all routines (user-defined functions and stored procedures) in a BigQuery dataset. Requires the READER dataset role. Use this to discover available routines before executing or inspecting them. |
| `GOOGLEBIGQUERY_LIST_ROW_ACCESS_POLICIES` | List BigQuery Row Access Policies | Tool to list all row access policies on a specified BigQuery table. Use when you need to discover which row-level security policies are applied to a table and their filter predicates. |
| `GOOGLEBIGQUERY_LIST_TABLE_DATA` | List BigQuery Table Data | Tool to list the content of a BigQuery table in rows via the REST API. Use this to retrieve actual data from a table without writing SQL queries. Returns paginated results with row data in the native BigQuery format. |
| `GOOGLEBIGQUERY_LIST_TABLES` | List BigQuery Tables | Tool to list tables in a BigQuery dataset via the REST API. Use this early in exploration to discover accessible tables without relying on INFORMATION_SCHEMA, especially when SQL-based metadata queries are blocked or restricted. This provides a deterministic inventory of tables even when dataset-level permissions prevent INFORMATION_SCHEMA access. |
| `GOOGLEBIGQUERY_PATCH_DATASET` | Patch BigQuery Dataset | Tool to update an existing BigQuery dataset using RFC5789 PATCH semantics. Only replaces fields provided in the request, leaving other fields unchanged. Use when you need to modify dataset properties like description, labels, expiration settings, or access controls without affecting other configuration. |
| `GOOGLEBIGQUERY_PATCH_MODEL` | Patch BigQuery ML Model | Tool to update specific fields in an existing BigQuery ML model using PATCH semantics. Use when you need to modify model metadata like description, friendly name, labels, or expiration time without replacing the entire model resource. |
| `GOOGLEBIGQUERY_PATCH_TABLE` | Patch BigQuery Table | Tool to update specific fields in an existing BigQuery table using RFC5789 PATCH semantics. Only the fields provided in the request are updated; unspecified fields remain unchanged. Use when you need to modify table metadata like description, friendly name, labels, or expiration time without replacing the entire table resource. |
| `GOOGLEBIGQUERY_QUERY` | Query | Query Tool runs a SQL query in BigQuery using the REST API. Use proper BigQuery SQL syntax, e.g., SELECT * FROM `project.dataset.table` WHERE column_name = 'value'. Results are returned under data.rows; an empty rows array means no matching data. Large result sets may be returned via remote_file_info instead of inline rows. Verify exact project_id, dataset, table, and column names before running; wrong identifiers trigger invalidQuery or notFound errors. |
| `GOOGLEBIGQUERY_SEARCH_ALL_ASSIGNMENTS` | Search All BigQuery Reservation Assignments | Tool to search all BigQuery reservation assignments for a specified resource in a particular region. Use when you need to find assignments for a project, folder, or organization. Returns assignments created on the resource or its closest ancestor, covering all JobTypes. |
| `GOOGLEBIGQUERY_SET_ROUTINE_IAM_POLICY` | Set BigQuery Routine IAM Policy | Tool to set the IAM access control policy for a BigQuery routine resource. Use this to grant or modify access permissions for users, service accounts, or groups. Include the etag from getIamPolicy to prevent concurrent modifications. |
| `GOOGLEBIGQUERY_TEST_ROUTINE_IAM_PERMISSIONS` | Test BigQuery Routine IAM Permissions | Tool to test which IAM permissions the caller has on a BigQuery routine. Returns the subset of requested permissions that the caller actually has. Use to verify access before performing operations. |
| `GOOGLEBIGQUERY_UNDELETE_DATASET` | Undelete BigQuery Dataset | Tool to undelete a BigQuery dataset within the time travel window. If a deletion time is specified, the dataset version deleted at that time is undeleted; otherwise, the most recently deleted version is restored. |
| `GOOGLEBIGQUERY_UPDATE_CONNECTION` | Update BigQuery Connection | Tool to update a specified BigQuery connection using the BigQuery Connection API. Use when you need to modify connection properties such as friendly name, description, or connection-specific settings. For security reasons, credentials are automatically reset if connection properties are included in the update mask. |
| `GOOGLEBIGQUERY_UPDATE_DATASET` | Update BigQuery Dataset | Tool to update information in an existing BigQuery dataset using the PUT method. The update method replaces the entire dataset resource, whereas the patch method only replaces fields that are provided in the submitted dataset resource. Use when you need to modify dataset properties like description, access controls, or default settings. |
| `GOOGLEBIGQUERY_UPDATE_ROUTINE` | Update BigQuery Routine | Tool to update an existing BigQuery routine (function or stored procedure). This replaces the entire routine resource with the provided definition. Use when modifying routine logic, arguments, return types, or other configuration. Ensure all required fields are provided as this is a full replacement operation. |
| `GOOGLEBIGQUERY_UPDATE_TABLE` | Update BigQuery Table | Tool to update an existing BigQuery table. The update method replaces the entire Table resource, whereas the patch method only replaces fields that are provided. Use when you need to modify table properties like schema, description, labels, partitioning, or clustering configuration. |

## Supported Triggers

None listed.

## Troubleshooting

### 1. Why doesn't Muse show a button to add my API key?

Muse shows the button only when you ask it to open the secure credentials store.
- Start a new chat.
- Send the message from step 1 exactly as written.
- If Muse asks for the key in the chat, do not paste it. Ask Muse to open the secure credentials store.

### 2. Why does the Composio connection fail after I add my API key?

The key or the connector settings are not correct.
- Copy the full key from the Composio dashboard. It starts with ck_.
- Make sure the URL is https://connect.composio.dev/mcp and the header is x-consumer-api-key.
- Remove the Composio connector in Muse settings, then do steps 1 to 3 again.

### 3. Why doesn't Muse use the Composio tools for Google BigQuery?

Muse may not pick the Composio connector for the request.
- Ask Muse to list its Composio tools.
- Name Composio and Google BigQuery in your request, for example: Use Composio to connect to Google BigQuery.
- If it still does not work, start a new chat and try again.

### 4. Why does Muse ask me to connect Google BigQuery again?

Your Google BigQuery connection expired or was removed.
- Open the link Muse gives you and sign in to Google BigQuery again.
- Check that the Google BigQuery connection shows as ACTIVE in the Composio dashboard.

### 5. What do I do if I pasted my API key into the chat?

Treat the key as exposed.
- Create a new API key in the Composio dashboard and delete the old one.
- Add the new key in Muse's secure credentials store, not in the chat.

## Creating MCP Server - Stand-alone vs Composio SDK

Once connected, Muse can access the Google BigQuery MCP server through Composio to run the actions you authorize, using plain language in the chat.

## Complete Code

None listed.

## Conclusion

### Way Forward
Now that Google BigQuery is connected, extend your setup by connecting the other apps you already use every day, so Muse can run true cross-app workflows end to end.
- Connect Calendar to turn threads into scheduled meetings automatically.
- Connect Slack or Teams to post summaries, approvals, and alerts where your team works.
- Connect Notion, Linear, Jira, or Asana to convert requests into tickets, tasks, and docs.
- Connect Drive, Dropbox, or OneDrive to fetch, file, and share attachments without manual steps.
Start with one workflow you do repeatedly, then keep adding apps as you find new handoffs. With everything behind a single connection, Muse can coordinate multiple tools safely and reliably in one conversation.

## How to build Google BigQuery MCP Agent with another framework

- [Claude Code](https://composio.dev/toolkits/googlebigquery/framework/claude-code)
- [Cursor](https://composio.dev/toolkits/googlebigquery/framework/cursor)
- [ChatGPT](https://composio.dev/toolkits/googlebigquery/framework/chatgpt)
- [Claude Cowork](https://composio.dev/toolkits/googlebigquery/framework/claude-cowork)
- [Antigravity](https://composio.dev/toolkits/googlebigquery/framework/antigravity)
- [OpenAI Agents SDK](https://composio.dev/toolkits/googlebigquery/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/googlebigquery/framework/claude-agents-sdk)
- [Codex](https://composio.dev/toolkits/googlebigquery/framework/codex)
- [Kimi Code](https://composio.dev/toolkits/googlebigquery/framework/kimi)
- [Grok Build](https://composio.dev/toolkits/googlebigquery/framework/grok-build)
- [VS Code](https://composio.dev/toolkits/googlebigquery/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/googlebigquery/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/googlebigquery/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/googlebigquery/framework/hermes-agent)
- [Atomic Agent](https://composio.dev/toolkits/googlebigquery/framework/atomic-agent)
- [CLI](https://composio.dev/toolkits/googlebigquery/framework/cli)
- [Google ADK](https://composio.dev/toolkits/googlebigquery/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/googlebigquery/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/googlebigquery/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/googlebigquery/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/googlebigquery/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/googlebigquery/framework/crew-ai)
- [Pydantic AI](https://composio.dev/toolkits/googlebigquery/framework/pydantic-ai)
- [AutoGen](https://composio.dev/toolkits/googlebigquery/framework/autogen)

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- [Peopledatalabs](https://composio.dev/toolkits/peopledatalabs) - Peopledatalabs delivers B2B data enrichment and identity resolution APIs. Supercharge your apps with accurate, up-to-date business and contact data.
- [Snowflake](https://composio.dev/toolkits/snowflake) - Snowflake is a cloud data warehouse built for elastic scaling, secure data sharing, and fast SQL analytics across major clouds.
- [Posthog](https://composio.dev/toolkits/posthog) - PostHog is an open-source analytics platform for tracking user interactions and product metrics. It helps teams refine features, analyze funnels, and reduce churn with actionable insights.
- [Akta](https://composio.dev/toolkits/akta) - Akta is a company intelligence platform providing enrichment, news, and alternative business signals. It helps teams build targeted company lists and enrich data-driven workflows.
- [Amplitude](https://composio.dev/toolkits/amplitude) - Amplitude is a digital analytics platform for product and behavioral data insights. It helps teams analyze user journeys and make data-driven decisions quickly.
- [Amplitude MCP](https://composio.dev/toolkits/amplitude_mcp) - Amplitude MCP is Amplitude's product analytics service for tracking user behavior and product metrics. Use it to centralize product data and manage charts, dashboards, cohorts, experiments, and taxonomy.
- [Baremetrics](https://composio.dev/toolkits/baremetrics) - Baremetrics is a subscription analytics platform for recurring-revenue businesses. It helps teams track MRR, churn, customers, and revenue trends in one place.
- [Bing Webmaster Tools](https://composio.dev/toolkits/bing_webmaster_tools) - Bing Webmaster Tools is Microsoft's search console for site performance, crawling, indexing, URL submission, and verified site management. It helps site owners understand Bing Search visibility and fix issues that affect organic traffic.
- [Bread & Butter](https://composio.dev/toolkits/bread_butter) - Bread & Butter is a lead-intelligence and identity platform for website visitor tracking, user profiles, attribution, authentication, and conversion workflows. It helps teams understand who is visiting, where leads come from, and how users convert.
- [Bright Data MCP](https://composio.dev/toolkits/brightdata_mcp) - Bright Data MCP is an AI-powered web scraping and data collection platform. Instantly access public web data in real time with advanced scraping tools.
- [Browseai](https://composio.dev/toolkits/browseai) - Browseai is a web automation and data extraction platform that turns any website into an API. It's perfect for monitoring websites and retrieving structured data without manual scraping.
- [BSC Designer](https://composio.dev/toolkits/bsc_designer) - BSC Designer is a strategy execution platform for balanced scorecards, KPIs, dashboards, and strategy maps. It helps teams turn goals into measurable performance plans they can track over time.
- [Chameleon](https://composio.dev/toolkits/chameleon) - Chameleon is a product adoption platform for building in-app experiences, managing customer data, and analyzing user engagement. It helps teams improve onboarding, feature discovery, and product adoption with targeted user experiences.
- [ChartMogul](https://composio.dev/toolkits/chartmogul) - ChartMogul is a subscription analytics and revenue data platform. It helps teams monitor MRR, churn, customer segments, and billing metrics.
- [ClickHouse](https://composio.dev/toolkits/clickhouse) - ClickHouse is an open-source, column-oriented database for real-time analytics and big data processing using SQL. Its lightning-fast query performance makes it ideal for handling large datasets and delivering instant insights.
- [Coinmarketcal](https://composio.dev/toolkits/coinmarketcal) - CoinMarketCal is a community-powered crypto calendar for upcoming events, announcements, and releases. It helps traders track market-moving developments and stay ahead in the crypto space.

## Frequently Asked Questions

### Does Muse support MCP?

Yes. Muse supports custom connectors that use the Model Context Protocol (MCP). Composio connects as one custom connector over remote streamable HTTP at https://connect.composio.dev/mcp, and gives Muse access to Google BigQuery and 1,500+ other apps.

### Where do I get the API key for the Composio connector?

Copy it from the Composio dashboard. It starts with ck_. Enter it only in Muse's secure credentials store, never in the chat.

### Can I connect more than one Google BigQuery account to Muse?

Yes. Composio supports multiple connected accounts for the same app. Give each Google BigQuery connection a name, such as work or personal, and tell Muse which one to use.

### How safe is my Google BigQuery data with Composio?

Tokens, keys, and configuration are encrypted at rest and in transit. Your Composio API key stays in Muse's secure credentials store, not in the chat or the model context. Composio is SOC 2 Type 2 compliant.

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