{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-bigquery","slug":"bigquery","name":"BigQuery","type":"mcp","url":"https://github.com/ergut/mcp-bigquery-server","page_url":"https://unfragile.ai/bigquery","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-bigquery__cap_0","uri":"capability://tool.use.integration.natural.language.to.sql.query.translation.via.llm","name":"natural language to sql query translation via llm","description":"Enables Claude or other LLMs to translate natural language questions into executable SQL queries against BigQuery datasets. The server exposes a CallTool handler that accepts natural language input, which the LLM converts to SQL, then validates and executes the query. This bridges the gap between conversational interfaces and structured database access without requiring users to write SQL manually.","intents":["Ask questions about my BigQuery data in plain English without writing SQL","Have Claude generate and execute SQL queries based on my data questions","Enable non-technical stakeholders to query databases through conversational AI"],"best_for":["Data analysts and business users who prefer conversational interfaces over SQL","Teams integrating Claude Desktop with BigQuery data warehouses","Organizations building LLM-powered BI tools on top of BigQuery"],"limitations":["LLM-generated SQL quality depends on Claude's understanding of schema and query complexity — complex multi-table joins may require schema hints","No query optimization or cost estimation before execution — users may incur unexpected BigQuery costs","Limited to read-only queries; cannot perform data modification operations"],"requires":["Claude Desktop or MCP-compatible client","Google Cloud project with BigQuery enabled","Valid Google Cloud authentication (gcloud CLI or service account)"],"input_types":["natural language text (user questions)"],"output_types":["structured query results (JSON/tabular format)","error messages for invalid queries"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bigquery__cap_1","uri":"capability://search.retrieval.bigquery.resource.discovery.and.enumeration","name":"bigquery resource discovery and enumeration","description":"Implements the MCP ListResources handler to dynamically discover and list all available BigQuery datasets, tables, and views within a GCP project. The server queries BigQuery's metadata API to build a hierarchical resource tree that Claude can browse, enabling users to explore their data warehouse structure without manual documentation. This discovery happens on-demand and reflects the current state of the BigQuery project.","intents":["Browse available datasets and tables in my BigQuery project from Claude","Discover what data exists before writing queries","Get a complete inventory of tables and views accessible to the service account"],"best_for":["Data teams exploring unfamiliar BigQuery projects","Developers building discovery features into LLM-powered analytics tools","Organizations with large, complex data warehouses needing dynamic schema browsing"],"limitations":["Discovery latency increases with project size — projects with 1000+ tables may experience multi-second delays","Does not discover external tables or snapshots, only native BigQuery tables and views","Requires BigQuery.datasets.list and BigQuery.tables.list IAM permissions; restricted service accounts may see incomplete results"],"requires":["Google Cloud project with BigQuery API enabled","Service account or gcloud CLI with bigquery.datasets.list permission","Network connectivity to BigQuery API endpoint"],"input_types":["none (server-initiated discovery)"],"output_types":["hierarchical resource list (dataset → table/view URIs)","resource metadata (table names, types)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bigquery__cap_2","uri":"capability://memory.knowledge.schema.inspection.and.table.metadata.retrieval","name":"schema inspection and table metadata retrieval","description":"Implements the MCP ReadResource handler to retrieve detailed schema information (column names, data types, descriptions, nullability) for specific BigQuery tables and views. When Claude requests details about a table, the server queries BigQuery's INFORMATION_SCHEMA to return structured metadata that helps the LLM understand table structure before generating queries. This enables context-aware SQL generation with accurate type information.","intents":["View the schema and column details for a specific BigQuery table","Understand data types and constraints before asking questions about a table","Get column descriptions and metadata to help Claude generate correct queries"],"best_for":["LLM agents that need to understand table structure before generating SQL","Teams building semantic layers on top of BigQuery","Data documentation systems that expose schema through Claude"],"limitations":["Does not retrieve sample data or statistics — only schema structure","Column descriptions depend on manual documentation in BigQuery; undocumented columns return empty descriptions","Large tables with 1000+ columns may return verbose metadata that consumes token budget"],"requires":["BigQuery.tables.get IAM permission","Valid table URI in format 'bigquery://project/dataset/table'","Access to INFORMATION_SCHEMA views in the target dataset"],"input_types":["resource URI (bigquery://project/dataset/table)"],"output_types":["structured schema metadata (JSON with columns, types, descriptions)","table metadata (row count, creation date, last modified)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bigquery__cap_3","uri":"capability://data.processing.analysis.read.only.sql.query.validation.and.execution","name":"read-only sql query validation and execution","description":"Implements query validation logic that parses incoming SQL queries to ensure they are read-only (SELECT only) before executing them against BigQuery. The server uses pattern matching or SQL parsing to block INSERT, UPDATE, DELETE, and DDL operations, then executes validated queries with a configurable 1GB processing limit to control costs. Results are returned in structured format that Claude can interpret and present to users.","intents":["Execute SQL queries safely without risk of accidental data modification","Control BigQuery costs by limiting query processing to 1GB per query","Prevent unauthorized data changes through the LLM interface"],"best_for":["Production environments where data safety is critical","Multi-user systems where different users should have read-only access","Cost-conscious organizations needing to prevent runaway queries"],"limitations":["Query validation uses pattern matching and may not catch all DDL/DML operations in complex nested queries or CTEs","1GB processing limit is enforced per query, not per session — large analytical queries may be blocked","No query optimization or cost estimation before execution — users discover cost overruns only after query runs","Cannot distinguish between intentional and accidental modifications — all writes are blocked equally"],"requires":["BigQuery.jobs.create and BigQuery.datasets.get IAM permissions","Query must be valid BigQuery SQL syntax","Sufficient BigQuery quota for 1GB processing limit"],"input_types":["SQL query string (SELECT statements only)"],"output_types":["query results (rows in JSON/tabular format)","query metadata (execution time, bytes processed, row count)","error messages for invalid or blocked queries"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bigquery__cap_4","uri":"capability://tool.use.integration.mcp.protocol.tool.exposure.and.request.routing","name":"mcp protocol tool exposure and request routing","description":"Implements the MCP ListTools handler to expose BigQuery query execution as a callable tool within the MCP protocol, allowing Claude to discover and invoke the query capability. The server registers the 'execute_query' tool with parameter schemas that Claude uses to understand required inputs (SQL query text). This enables Claude to treat BigQuery querying as a first-class tool alongside other MCP-exposed capabilities.","intents":["Enable Claude to discover that BigQuery querying is available as a tool","Allow Claude to call BigQuery queries with proper parameter validation","Integrate BigQuery querying into Claude's multi-tool reasoning and planning"],"best_for":["Claude Desktop users who want BigQuery as a native tool","Developers building multi-tool LLM agents that include BigQuery","Teams standardizing on MCP for tool integration across multiple data sources"],"limitations":["Tool discovery happens at server startup — new tools added to BigQuery require server restart","Parameter schema is static and cannot be dynamically generated based on available tables","No tool versioning or deprecation mechanism — breaking changes require client updates"],"requires":["MCP SDK for the target language (TypeScript/Node.js)","Claude Desktop or MCP-compatible client that supports tool calling","Server must be running and registered with the client"],"input_types":["none (server-initiated tool registration)"],"output_types":["tool definitions (name, description, parameter schema)","tool invocation responses (query results or errors)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bigquery__cap_5","uri":"capability://safety.moderation.google.cloud.authentication.and.credential.management","name":"google cloud authentication and credential management","description":"Supports two authentication methods: Google Cloud CLI (gcloud) for development and service account JSON keys for production. The server uses the Google Cloud client libraries to authenticate with BigQuery, automatically discovering credentials from the environment (GOOGLE_APPLICATION_CREDENTIALS env var or gcloud default credentials). This abstraction allows the same server code to work in development and production without code changes.","intents":["Authenticate with BigQuery using development credentials (gcloud CLI)","Deploy the server to production with service account credentials","Manage BigQuery access without hardcoding credentials in the server"],"best_for":["Development teams using gcloud CLI for local testing","Production deployments on Google Cloud (Cloud Run, Compute Engine, GKE)","Organizations with existing Google Cloud infrastructure and IAM policies"],"limitations":["Credential discovery follows Google Cloud's standard chain (env var → gcloud → metadata server) — misconfigured environments may fail silently","Service account keys are long-lived and require manual rotation; no built-in key rotation mechanism","gcloud CLI authentication requires local installation and login — not suitable for headless/automated environments","No support for workload identity federation or other advanced GCP auth methods"],"requires":["Google Cloud SDK (gcloud CLI) for development OR service account JSON key for production","GOOGLE_APPLICATION_CREDENTIALS environment variable (for service account) OR gcloud login (for CLI)","Google Cloud project with BigQuery API enabled","Service account with bigquery.jobs.create, bigquery.datasets.list, bigquery.tables.get permissions"],"input_types":["environment variables or credential files"],"output_types":["authenticated BigQuery client connection","authentication errors if credentials are invalid or missing"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bigquery__cap_6","uri":"capability://data.processing.analysis.query.result.formatting.and.structured.output","name":"query result formatting and structured output","description":"Processes BigQuery query results and formats them into structured output (JSON or tabular format) that Claude can parse and present to users. The server handles variable result sizes, converts BigQuery data types to JSON-compatible formats, and includes metadata (row count, execution time, bytes processed). This formatting layer ensures results are human-readable while remaining machine-parseable for downstream processing.","intents":["Get query results in a format Claude can easily interpret and present","Include query metadata (execution time, cost) alongside results","Handle large result sets without overwhelming the LLM context"],"best_for":["LLM interfaces that need to present query results to users","Agents that need to process query results for further analysis","Systems that need to track query costs and performance metrics"],"limitations":["Large result sets (10000+ rows) may exceed Claude's context window — no built-in pagination or sampling","Complex data types (STRUCT, ARRAY) are flattened to JSON, potentially losing nested structure information","No result caching — identical queries executed multiple times incur full BigQuery costs","Timestamp and date formatting depends on BigQuery's default serialization — timezone handling may be ambiguous"],"requires":["BigQuery query execution completed successfully","Result set must fit in memory (limited by server resources)","JSON serialization support for all BigQuery data types"],"input_types":["BigQuery query result objects"],"output_types":["JSON-formatted result rows","query metadata (execution time, bytes processed, row count)","error messages if result formatting fails"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bigquery__cap_7","uri":"capability://tool.use.integration.mcp.resource.uri.parsing.and.hierarchical.resource.mapping","name":"mcp resource uri parsing and hierarchical resource mapping","description":"Implements URI parsing for BigQuery resources using the 'bigquery://' scheme (e.g., 'bigquery://project/dataset/table') to map natural resource identifiers to BigQuery API calls. The server parses these URIs in ReadResource and ListResources handlers to extract project, dataset, and table components, then uses them to construct appropriate BigQuery API requests. This abstraction allows Claude to reference resources by human-readable URIs rather than API-specific identifiers.","intents":["Reference BigQuery tables using standard URIs instead of API-specific identifiers","Enable Claude to navigate the resource hierarchy (project → dataset → table)","Provide a consistent resource naming scheme across the MCP interface"],"best_for":["MCP clients that expect standard resource URI formats","Systems building resource browsers or explorers on top of BigQuery","Teams standardizing on URI-based resource identification across multiple data sources"],"limitations":["URI parsing is simple string-based — malformed URIs may produce cryptic errors","No URI validation before API calls — invalid project/dataset/table combinations fail at query time","URI scheme is custom to this implementation — not standardized across BigQuery tools","No support for complex resource references (snapshots, external tables, views with special characters)"],"requires":["Valid BigQuery resource URI in format 'bigquery://project/dataset/table'","Project, dataset, and table names must be valid BigQuery identifiers"],"input_types":["resource URI strings"],"output_types":["parsed resource components (project, dataset, table)","BigQuery API request parameters"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Claude Desktop or MCP-compatible client","Google Cloud project with BigQuery enabled","Valid Google Cloud authentication (gcloud CLI or service account)","Google Cloud project with BigQuery API enabled","Service account or gcloud CLI with bigquery.datasets.list permission","Network connectivity to BigQuery API endpoint","BigQuery.tables.get IAM permission","Valid table URI in format 'bigquery://project/dataset/table'","Access to INFORMATION_SCHEMA views in the target dataset","BigQuery.jobs.create and BigQuery.datasets.get IAM permissions"],"failure_modes":["LLM-generated SQL quality depends on Claude's understanding of schema and query complexity — complex multi-table joins may require schema hints","No query optimization or cost estimation before execution — users may incur unexpected BigQuery costs","Limited to read-only queries; cannot perform data modification operations","Discovery latency increases with project size — projects with 1000+ tables may experience multi-second delays","Does not discover external tables or snapshots, only native BigQuery tables and views","Requires BigQuery.datasets.list and BigQuery.tables.list IAM permissions; restricted service accounts may see incomplete results","Does not retrieve sample data or statistics — only schema structure","Column descriptions depend on manual documentation in BigQuery; undocumented columns return empty descriptions","Large tables with 1000+ columns may return verbose metadata that consumes token budget","Query validation uses pattern matching and may not catch all DDL/DML operations in complex nested queries or CTEs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=bigquery","compare_url":"https://unfragile.ai/compare?artifact=bigquery"}},"signature":"K3d8LeRg5qURHuWw3Ua7yYkH3rQUu6yFUkcKkXpPubeFZpzke4+B7ZpykDCnwXqFUNrzM7skqY1Q9v55kU6lCw==","signedAt":"2026-06-23T00:54:49.315Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/bigquery","artifact":"https://unfragile.ai/bigquery","verify":"https://unfragile.ai/api/v1/verify?slug=bigquery","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}