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The server implements the Model Context Protocol specification to expose query execution as a callable tool, handling connection pooling, statement preparation, and result serialization back to the client through MCP's structured message format.","intents":["Execute SELECT, INSERT, UPDATE, DELETE queries from an AI agent without direct database access","Run ad-hoc SQL commands from Claude or other MCP-compatible clients against a CockroachDB cluster","Retrieve structured data from the database to feed into LLM reasoning chains"],"best_for":["AI agents and LLM applications needing database query capabilities","Teams building agentic workflows that require data retrieval from CockroachDB","Developers integrating CockroachDB with Claude or other MCP-compatible LLM clients"],"limitations":["Query execution is synchronous — long-running queries will block the MCP server","No built-in query timeout enforcement; relies on database-level statement timeouts","Result sets are fully materialized in memory before serialization, limiting scalability for very large result sets","No query plan optimization or hints — raw SQL is passed directly to CockroachDB"],"requires":["CockroachDB 21.1 or later with PostgreSQL-compatible wire protocol","Network connectivity to CockroachDB cluster (TCP port 26257 by default)","MCP client implementation (e.g., Claude desktop, custom MCP host)","Valid CockroachDB connection credentials (username, password, or certificate-based auth)"],"input_types":["SQL query string","Query parameters (optional, for parameterized queries)"],"output_types":["Structured JSON result set","Row count for DML operations","Error messages with SQL error codes"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cockroachdb__cap_1","uri":"capability://memory.knowledge.database.schema.introspection.and.metadata.retrieval","name":"database schema introspection and metadata retrieval","description":"Exposes CockroachDB schema metadata (tables, columns, indexes, constraints, data types) through MCP tools by querying the information_schema and pg_catalog system tables. This allows LLM agents to discover database structure, understand column types and constraints, and generate contextually-aware SQL queries without requiring hardcoded schema definitions.","intents":["Discover available tables and columns in a CockroachDB database for dynamic query generation","Retrieve column data types and constraints to validate query parameters before execution","Generate SQL queries with proper table/column references without manual schema documentation"],"best_for":["Agentic systems that need to dynamically adapt queries based on runtime schema discovery","LLM applications generating SQL without pre-defined schema context","Multi-tenant systems where schema varies per customer or environment"],"limitations":["Metadata queries add latency to agent reasoning loops (typically 50-200ms per introspection call)","Does not expose view definitions, stored procedures, or custom types in detail","Schema caching is not implemented — each introspection call hits the database","Large schemas (1000+ tables) may return slow or incomplete results"],"requires":["SELECT permissions on information_schema and pg_catalog tables","CockroachDB 21.1 or later","Network connectivity to CockroachDB cluster"],"input_types":["Database name (optional, defaults to current database)","Table name pattern (optional, for filtering)"],"output_types":["JSON array of table definitions with column metadata","Column type information (data type, nullable, default values)","Index and constraint definitions"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cockroachdb__cap_2","uri":"capability://tool.use.integration.connection.pooling.and.session.management.via.mcp","name":"connection pooling and session management via mcp","description":"Manages persistent connections to CockroachDB through a connection pool, reusing database sessions across multiple MCP tool invocations to reduce connection overhead. The server handles connection lifecycle (creation, validation, cleanup) transparently, allowing the MCP client to issue sequential queries without managing connection state explicitly.","intents":["Execute multiple database queries in sequence without reconnecting for each query","Reduce latency for rapid-fire query execution from agentic workflows","Maintain transaction context across multiple tool calls within a single agent step"],"best_for":["High-frequency query patterns where connection overhead is significant","Agentic systems executing multiple database operations per reasoning step","Applications requiring transaction isolation across multiple MCP tool invocations"],"limitations":["Pool size is fixed at server startup — no dynamic scaling based on load","Idle connections are not automatically closed, consuming resources if pool is oversized","No per-client connection isolation — all MCP clients share the same pool","Connection validation (ping) adds ~5-10ms per query if enabled"],"requires":["CockroachDB 21.1 or later","Network connectivity to CockroachDB cluster","Sufficient system resources for the configured pool size"],"input_types":["Pool size configuration (at server startup)","Connection timeout settings"],"output_types":["Active connection count (via monitoring tools)","Query execution results (reusing pooled connections)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cockroachdb__cap_3","uri":"capability://tool.use.integration.transaction.management.with.rollback.and.commit.control","name":"transaction management with rollback and commit control","description":"Provides MCP tools to explicitly control transaction boundaries (BEGIN, COMMIT, ROLLBACK) in CockroachDB, allowing LLM agents to group multiple SQL operations into atomic units. The server tracks transaction state per MCP session and ensures proper cleanup (rollback on error or timeout) to prevent resource leaks and orphaned transactions.","intents":["Execute multiple related database modifications as a single atomic transaction","Rollback changes if an agent detects an error or constraint violation mid-operation","Ensure data consistency when an agent performs multi-step database operations"],"best_for":["Agentic systems performing multi-step data modifications that must succeed or fail atomically","Applications requiring ACID guarantees across multiple database operations","Workflows where partial updates would leave the database in an inconsistent state"],"limitations":["Long-running transactions block other operations and consume resources","No automatic transaction timeout — requires explicit ROLLBACK or connection cleanup","Nested transactions (SAVEPOINTs) are not explicitly exposed as MCP tools","Transaction isolation level is fixed at server configuration time, not per-transaction"],"requires":["CockroachDB 21.1 or later","Network connectivity to CockroachDB cluster","Proper error handling in the MCP client to ensure ROLLBACK is called on failure"],"input_types":["Transaction control command (BEGIN, COMMIT, ROLLBACK)"],"output_types":["Transaction status confirmation","Error messages if transaction control fails"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cockroachdb__cap_4","uri":"capability://tool.use.integration.parameterized.query.execution.with.prepared.statements","name":"parameterized query execution with prepared statements","description":"Supports parameterized SQL queries using prepared statements, where query templates and parameters are sent separately to CockroachDB. This prevents SQL injection attacks, improves query plan caching, and allows the LLM agent to safely construct dynamic queries by binding user-provided values as parameters rather than string concatenation.","intents":["Execute queries with user-provided values safely without SQL injection risk","Improve query performance through prepared statement plan caching","Allow LLM agents to generate parameterized queries dynamically based on user input"],"best_for":["Applications where LLM agents construct queries from untrusted input","High-performance scenarios where query plan caching provides measurable benefit","Security-sensitive applications requiring SQL injection prevention"],"limitations":["Parameter binding adds ~5-10ms overhead per query compared to direct string queries","Prepared statement cache is limited in size — very large numbers of unique statements may cause cache thrashing","Some dynamic SQL patterns (e.g., dynamic column lists) cannot be parameterized","Parameter type inference may fail for complex expressions, requiring explicit type casting"],"requires":["CockroachDB 21.1 or later","MCP client support for passing query parameters separately from the query string"],"input_types":["SQL query template with parameter placeholders ($1, $2, etc.)","Array of parameter values with inferred or explicit types"],"output_types":["Structured JSON result set","Error messages with parameter binding details"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cockroachdb__cap_5","uri":"capability://data.processing.analysis.query.result.pagination.and.streaming","name":"query result pagination and streaming","description":"Implements pagination controls (LIMIT, OFFSET) and result streaming to handle large result sets without materializing the entire dataset in memory. The MCP server returns results in configurable chunks, allowing clients to fetch subsequent pages on demand, reducing memory consumption and improving responsiveness for queries returning thousands or millions of rows.","intents":["Retrieve large result sets from CockroachDB without exhausting server memory","Implement pagination in agent workflows to process data incrementally","Stream results to the client as they become available rather than waiting for the full result set"],"best_for":["Applications querying large tables or aggregations that return many rows","Agentic systems processing data incrementally rather than all at once","Memory-constrained environments where full result materialization is infeasible"],"limitations":["Pagination requires maintaining cursor state on the server, consuming resources for long-lived result sets","Streaming results may be slower than batch retrieval due to per-row serialization overhead","Result ordering must be deterministic (requires ORDER BY) for consistent pagination","No built-in result caching — repeated pagination requests re-execute the query"],"requires":["CockroachDB 21.1 or later","MCP client support for pagination parameters (limit, offset or cursor)"],"input_types":["SQL query","Page size (limit)","Offset or cursor for pagination"],"output_types":["Partial result set (one page)","Metadata indicating total row count and whether more results are available","Cursor or offset for fetching the next page"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cockroachdb__cap_6","uri":"capability://automation.workflow.database.monitoring.and.health.check.tools","name":"database monitoring and health check tools","description":"Exposes MCP tools for monitoring CockroachDB cluster health, including connection status, query performance metrics, and system resource usage. The server queries CockroachDB's built-in monitoring tables (crdb_internal.* and system.* tables) to provide real-time visibility into cluster state, allowing agents to diagnose issues or make decisions based on current system health.","intents":["Check CockroachDB cluster connectivity and availability before executing queries","Monitor query performance and identify slow queries causing bottlenecks","Retrieve system metrics (CPU, memory, disk usage) to inform agent decision-making"],"best_for":["Agentic systems that need to adapt behavior based on database health","Monitoring and observability integrations with CockroachDB","Self-healing systems that can detect and respond to database issues"],"limitations":["Monitoring queries add latency to agent reasoning loops (typically 100-500ms)","Metrics are point-in-time snapshots, not historical trends","Some metrics require elevated privileges (e.g., system table access)","No built-in alerting or thresholding — clients must implement their own logic"],"requires":["CockroachDB 21.1 or later","SELECT permissions on crdb_internal and system tables","Network connectivity to CockroachDB cluster"],"input_types":["Health check type (connectivity, performance, resource usage)"],"output_types":["JSON object with health metrics","Status indicators (healthy, degraded, unhealthy)","Detailed error messages if checks fail"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cockroachdb__cap_7","uri":"capability://safety.moderation.error.handling.and.diagnostic.reporting","name":"error handling and diagnostic reporting","description":"Provides detailed error messages and diagnostic information when queries fail, including SQL error codes, constraint violations, and execution context. The MCP server translates CockroachDB error responses into structured JSON with actionable information, allowing LLM agents to understand failure reasons and potentially retry or adjust queries automatically.","intents":["Understand why a query failed (syntax error, constraint violation, permission denied, etc.)","Provide agents with enough context to retry or modify a failed query intelligently","Log and report errors for debugging and monitoring purposes"],"best_for":["Agentic systems that need to recover from query failures automatically","Applications requiring detailed error diagnostics for debugging","Systems that need to distinguish between transient and permanent failures"],"limitations":["Error messages may expose sensitive information (table names, column names) if not sanitized","Some CockroachDB errors are cryptic and require domain knowledge to interpret","No built-in error recovery strategies — clients must implement retry logic","Error context is lost if the connection is dropped before the error is reported"],"requires":["CockroachDB 21.1 or later","Network connectivity to CockroachDB cluster"],"input_types":["SQL query that may fail"],"output_types":["Structured error object with code, message, and context","Suggestions for recovery (e.g., 'retry with exponential backoff' for transient errors)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cockroachdb__cap_8","uri":"capability://tool.use.integration.multi.database.and.multi.cluster.support","name":"multi-database and multi-cluster support","description":"Allows the MCP server to manage connections to multiple CockroachDB databases or clusters, with tools to switch context between them. The server maintains separate connection pools for each database/cluster and tracks the current context, enabling agents to query across multiple CockroachDB instances or perform cross-database operations.","intents":["Execute queries against different CockroachDB databases or clusters from a single agent","Perform cross-database joins or data synchronization operations","Manage multiple CockroachDB environments (dev, staging, production) from a single MCP server"],"best_for":["Multi-tenant systems with separate databases per customer","Organizations managing multiple CockroachDB clusters across regions","Data migration or synchronization workflows spanning multiple databases"],"limitations":["Context switching between databases adds latency (typically 10-50ms per switch)","Cross-database transactions are not supported — each database has its own transaction context","Connection credentials must be configured for each database at server startup","No automatic failover or load balancing across clusters"],"requires":["CockroachDB 21.1 or later (multiple instances)","Network connectivity to all target CockroachDB clusters","Connection credentials for each database/cluster"],"input_types":["Database or cluster identifier","SQL query to execute in that context"],"output_types":["Query results from the specified database/cluster","Confirmation of context switch"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cockroachdb__cap_9","uri":"capability://data.processing.analysis.bulk.data.import.and.export.operations","name":"bulk data import and export operations","description":"Provides MCP tools for bulk importing data into CockroachDB (via IMPORT or INSERT with batch operations) and exporting query results to external formats (CSV, JSON). The server handles data serialization, chunking, and error recovery to efficiently move large datasets between CockroachDB and external systems without requiring manual ETL pipelines.","intents":["Import CSV or JSON data files into CockroachDB tables in bulk","Export query results to CSV or JSON format for downstream processing","Perform data synchronization between CockroachDB and external data sources"],"best_for":["Data migration and ETL workflows involving CockroachDB","Agentic systems that need to ingest external data into the database","Reporting and analytics systems exporting data from CockroachDB"],"limitations":["Bulk import performance depends on data size and network bandwidth","No built-in data validation or schema mapping — clients must ensure data format matches table schema","Export operations materialize results in memory before serialization, limiting scalability for very large datasets","No support for incremental/delta imports — each import is a full operation"],"requires":["CockroachDB 21.1 or later","INSERT or IMPORT permissions on target tables","Network connectivity to CockroachDB cluster","Sufficient disk space for temporary files during import/export"],"input_types":["CSV or JSON data file (for import)","SQL query (for export)","Target table name (for import)"],"output_types":["Import status (rows inserted, errors)","CSV or JSON export file","Data validation errors"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["CockroachDB 21.1 or later with PostgreSQL-compatible wire protocol","Network connectivity to CockroachDB cluster (TCP port 26257 by default)","MCP client implementation (e.g., Claude desktop, custom MCP host)","Valid CockroachDB connection credentials (username, password, or certificate-based auth)","SELECT permissions on information_schema and pg_catalog tables","CockroachDB 21.1 or later","Network connectivity to CockroachDB cluster","Sufficient system resources for the configured pool size","Proper error handling in the MCP client to ensure ROLLBACK is called on failure","MCP client support for passing query parameters separately from the query string"],"failure_modes":["Query execution is synchronous — long-running queries will block the MCP server","No built-in query timeout enforcement; relies on database-level statement timeouts","Result sets are fully materialized in memory before serialization, limiting scalability for very large result sets","No query plan optimization or hints — raw SQL is passed directly to CockroachDB","Metadata queries add latency to agent reasoning loops (typically 50-200ms per introspection call)","Does not expose view definitions, stored procedures, or custom types in detail","Schema caching is not implemented — each introspection call hits the database","Large schemas (1000+ tables) may return slow or incomplete results","Pool size is fixed at server startup — no dynamic scaling based on load","Idle connections are not automatically closed, consuming resources if pool is oversized","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.6,"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=cockroachdb","compare_url":"https://unfragile.ai/compare?artifact=cockroachdb"}},"signature":"zX1AJJSiKcMuBiRoCucAEKf3FY0OP0ceCY/4Si3AH1jpw3nNV0yxY0/QSNxDdqruNgtx3qPF+uTZqlVICU5iDw==","signedAt":"2026-06-22T13:53:05.249Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cockroachdb","artifact":"https://unfragile.ai/cockroachdb","verify":"https://unfragile.ai/api/v1/verify?slug=cockroachdb","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"}}