CockroachDB

Executes arbitrary SQL queries against CockroachDB instances by translating MCP tool calls into native PostgreSQL wire protocol commands. 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.