{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"postgresql-mcp-server","slug":"postgresql-mcp-server","name":"PostgreSQL MCP Server","type":"mcp","url":"https://github.com/modelcontextprotocol/servers/tree/main/src/postgres","page_url":"https://unfragile.ai/postgresql-mcp-server","categories":["mcp-servers"],"tags":["postgres","database","sql","official","reference"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"postgresql-mcp-server__cap_0","uri":"capability://tool.use.integration.mcp.compliant.postgresql.tool.exposure.via.json.rpc.protocol","name":"mcp-compliant postgresql tool exposure via json-rpc protocol","description":"Exposes PostgreSQL operations as MCP Tools through a standardized JSON-RPC 2.0 transport layer, enabling LLM clients to invoke database queries and schema operations as first-class capabilities. The server implements the MCP protocol primitives (Tools, Resources, Prompts) to bridge SQL execution with LLM reasoning, using the official MCP SDK to handle bidirectional communication and request/response serialization.","intents":["Connect an LLM client to PostgreSQL without writing custom API wrappers","Expose database operations as discoverable tools in an MCP-compatible client","Enable Claude or other MCP clients to query and explore a PostgreSQL database directly"],"best_for":["Teams building LLM agents that need structured database access","Developers integrating PostgreSQL into Claude Desktop or other MCP clients","Organizations standardizing on MCP for tool discovery and capability exposure"],"limitations":["MCP protocol overhead adds ~50-100ms per request due to JSON-RPC serialization","No built-in query optimization — LLM-generated queries may be inefficient without explicit guidance","Requires MCP client support; not compatible with standard PostgreSQL clients or ORMs"],"requires":["PostgreSQL 12+ database instance","Node.js 18+ (TypeScript implementation)","MCP-compatible client (Claude Desktop, custom MCP client, or MCP SDK)","Network connectivity to PostgreSQL server"],"input_types":["MCP Tool invocation requests (JSON-RPC)","SQL query strings","Schema inspection parameters"],"output_types":["Query result sets (JSON)","Schema metadata (JSON)","MCP Tool definitions (JSON Schema)"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"postgresql-mcp-server__cap_1","uri":"capability://data.processing.analysis.read.only.sql.query.execution.with.safety.defaults","name":"read-only sql query execution with safety defaults","description":"Executes SQL SELECT queries against PostgreSQL with enforced read-only constraints, preventing accidental or malicious write operations (INSERT, UPDATE, DELETE, DROP). The server validates query syntax and intent before execution, using connection pooling to manage database resources efficiently and returning results as structured JSON with row-level metadata.","intents":["Run SELECT queries safely from an LLM without risk of data modification","Prevent LLM-generated queries from accidentally mutating the database","Execute analytical queries and data exploration with confidence in data integrity"],"best_for":["Read-heavy analytics and reporting workflows","LLM agents that should never modify production data","Teams requiring strict separation between query and mutation capabilities"],"limitations":["Cannot execute stored procedures, transactions, or multi-statement queries","Write operations (INSERT, UPDATE, DELETE) are blocked at the server level, not database role level","No query timeout enforcement — long-running queries may consume connection pool resources","Result sets are fully materialized in memory before JSON serialization, limiting scalability for very large result sets (>100k rows)"],"requires":["PostgreSQL user account with SELECT privileges on target tables","Connection pooling configured (default: 10 connections)","Query must be valid SQL SELECT syntax"],"input_types":["SQL SELECT query string","Query parameters (optional, for parameterized queries)"],"output_types":["JSON array of result rows","Row count metadata","Column type information"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"postgresql-mcp-server__cap_10","uri":"capability://automation.workflow.logging.and.observability.for.query.execution.and.errors","name":"logging and observability for query execution and errors","description":"Logs all query executions, errors, and connection events to stdout or structured log files, enabling debugging and monitoring of MCP server behavior. The server records query text, execution time, result row count, and error details with timestamps. Supports structured logging (JSON format) for integration with log aggregation systems like ELK or Datadog.","intents":["Debug query failures and understand why queries are slow","Monitor MCP server health and database connectivity","Audit which queries are executed and by which clients"],"best_for":["Production deployments requiring observability","Debugging query performance issues","Security auditing and compliance requirements"],"limitations":["Logging may expose sensitive data (query text, parameter values) — requires careful log retention policies","Structured logging adds ~5-10ms overhead per query","No built-in log rotation — requires external log management","Query text logging may reveal schema structure or business logic"],"requires":["Writable filesystem or log aggregation endpoint","Log retention and privacy policies in place"],"input_types":["Log level configuration (DEBUG, INFO, WARN, ERROR)"],"output_types":["Log entries (text or JSON): query text, execution time, error details, timestamps"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"postgresql-mcp-server__cap_2","uri":"capability://data.processing.analysis.schema.introspection.and.metadata.discovery","name":"schema introspection and metadata discovery","description":"Inspects PostgreSQL schema to expose table structures, column definitions, indexes, and relationships as queryable metadata. The server implements schema discovery tools that retrieve information_schema data and format it as structured JSON, enabling LLMs to understand database structure before generating queries. Supports filtering by schema, table, or column patterns.","intents":["Understand database structure before writing queries","Discover available tables, columns, and their data types","Retrieve table relationships and constraints for query planning"],"best_for":["LLM agents that need to explore unfamiliar databases","Interactive query builders that require schema context","Documentation generation and data lineage tools"],"limitations":["Does not expose row-level security policies or column-level permissions","Metadata queries may be slow on databases with thousands of tables","Does not include statistics (row counts, index usage) — only structural metadata","Custom types and domains are returned but not fully resolved"],"requires":["PostgreSQL information_schema access (standard on all PostgreSQL installations)","SELECT privilege on information_schema tables"],"input_types":["Schema name (optional filter)","Table name (optional filter)","Column name pattern (optional filter)"],"output_types":["Table metadata (JSON): name, schema, column definitions, constraints","Column metadata (JSON): name, type, nullable, default, constraints","Index metadata (JSON): name, columns, uniqueness"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"postgresql-mcp-server__cap_3","uri":"capability://automation.workflow.connection.pooling.with.configurable.pool.size.and.timeout.management","name":"connection pooling with configurable pool size and timeout management","description":"Manages PostgreSQL connections using a connection pool (default 10 connections) to reuse database connections across multiple queries, reducing connection overhead and improving throughput. The server configures pool parameters (min/max connections, idle timeout, connection timeout) and handles connection lifecycle (acquire, release, error recovery) transparently. Implements connection validation and automatic reconnection on failure.","intents":["Reduce latency by reusing database connections across multiple LLM queries","Handle concurrent requests from multiple MCP clients without exhausting database connections","Gracefully recover from connection failures and network interruptions"],"best_for":["High-frequency query workloads with multiple concurrent LLM agents","Long-running MCP servers that need stable database connectivity","Production deployments with strict connection limits"],"limitations":["Pool size is fixed at startup — cannot dynamically adjust based on load","No built-in monitoring or metrics for pool utilization","Connection leaks are possible if queries hang indefinitely (requires external timeout enforcement)","Pool exhaustion will cause subsequent queries to queue or fail with 'no available connections' error"],"requires":["PostgreSQL connection parameters (host, port, database, user, password)","Network connectivity to PostgreSQL server","Sufficient PostgreSQL max_connections setting to accommodate pool size"],"input_types":["Pool configuration (size, timeout, idle timeout) — typically set at server startup"],"output_types":["Connection status (implicit — reflected in query execution success/failure)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"postgresql-mcp-server__cap_4","uri":"capability://safety.moderation.parameterized.query.execution.with.sql.injection.prevention","name":"parameterized query execution with sql injection prevention","description":"Supports parameterized SQL queries using PostgreSQL prepared statements, separating query structure from data values to prevent SQL injection attacks. The server accepts query templates with placeholder parameters and binds user-supplied values safely using the pg library's parameterization mechanism. Parameters are type-checked and escaped by the PostgreSQL driver before execution.","intents":["Execute queries with dynamic values without risk of SQL injection","Allow LLM-generated queries to safely incorporate user input or data from other sources","Reuse query plans for repeated queries with different parameters"],"best_for":["LLM agents that construct queries with user-supplied filters or values","Applications requiring strict SQL injection prevention","High-frequency queries with varying parameters (query plan caching benefit)"],"limitations":["Parameters can only substitute values, not table/column names or SQL keywords","LLM must be explicitly instructed to use parameterized queries; no automatic conversion from string interpolation","Parameter binding adds ~5-10ms overhead per query due to prepared statement compilation","Prepared statement cache is per-connection, not shared across pool"],"requires":["Query string with $1, $2, etc. placeholders (PostgreSQL syntax)","Parameter values array matching placeholder count and types"],"input_types":["SQL query template with $N placeholders","Array of parameter values (strings, numbers, booleans, dates, etc.)"],"output_types":["Query result set (JSON)","Parameter binding confirmation (implicit)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"postgresql-mcp-server__cap_5","uri":"capability://data.processing.analysis.multi.table.join.and.aggregation.query.support","name":"multi-table join and aggregation query support","description":"Executes complex SQL queries including JOINs, GROUP BY, aggregations (SUM, COUNT, AVG, MAX, MIN), subqueries, and window functions. The server parses and validates query structure to ensure read-only compliance while allowing sophisticated analytical queries. Results are returned as nested JSON structures that preserve column aliases and aggregation results.","intents":["Run analytical queries that combine data from multiple tables","Compute aggregations and statistics across large datasets","Support complex business logic queries without requiring application-level post-processing"],"best_for":["Analytics and reporting workflows","LLM agents performing data analysis and summarization","Exploratory data analysis with ad-hoc queries"],"limitations":["No query optimization hints — complex JOINs may be slow without proper indexing","Subqueries and CTEs (WITH clauses) are supported but may consume significant memory","Window functions are supported but require PostgreSQL 8.4+","Query complexity is not limited — malformed or inefficient queries may hang or timeout"],"requires":["Valid SQL SELECT syntax with JOIN, GROUP BY, or aggregate functions","Appropriate indexes on joined columns for performance","PostgreSQL 8.4+ for window function support"],"input_types":["SQL SELECT query with JOINs, GROUP BY, aggregations, or window functions"],"output_types":["Aggregated result set (JSON)","Column aliases preserved in output","NULL values for missing aggregations"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"postgresql-mcp-server__cap_6","uri":"capability://safety.moderation.error.handling.and.query.validation.with.detailed.error.reporting","name":"error handling and query validation with detailed error reporting","description":"Validates SQL syntax and execution errors, returning detailed error messages that help LLM clients understand and correct query failures. The server catches PostgreSQL errors (syntax errors, constraint violations, type mismatches) and formats them as structured JSON responses with error codes, messages, and context. Distinguishes between client errors (invalid SQL) and server errors (connection failures) for appropriate retry logic.","intents":["Understand why a query failed and how to fix it","Provide LLM agents with actionable error feedback for query refinement","Distinguish between transient failures (retry) and permanent errors (abandon)"],"best_for":["Interactive query debugging workflows","LLM agents that refine queries based on error feedback","Development and testing environments"],"limitations":["Error messages may expose schema details or data patterns that should be hidden","PostgreSQL error codes are not standardized across versions — error handling may differ","No built-in error suppression or sanitization — sensitive information may leak in error messages","Timeout errors are not distinguished from other execution errors"],"requires":["Valid PostgreSQL connection to receive error details"],"input_types":["SQL query string (may be syntactically invalid)"],"output_types":["Error response (JSON): error code, message, context, suggestion","Successful query result (JSON) if query is valid"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"postgresql-mcp-server__cap_7","uri":"capability://data.processing.analysis.transaction.isolation.and.consistency.guarantees","name":"transaction isolation and consistency guarantees","description":"Executes queries within PostgreSQL transaction isolation levels (READ COMMITTED, REPEATABLE READ, SERIALIZABLE), ensuring consistent reads and preventing dirty reads or phantom reads. The server configures transaction isolation at the connection level and handles transaction boundaries transparently. Each query runs in its own transaction by default, with support for explicit transaction control if needed.","intents":["Ensure queries see consistent snapshots of data","Prevent race conditions when multiple LLM agents query simultaneously","Guarantee ACID properties for analytical queries"],"best_for":["Concurrent LLM agents querying the same database","Analytical queries that require consistent snapshots","Applications with strict consistency requirements"],"limitations":["Higher isolation levels (SERIALIZABLE) may cause transaction conflicts and retries","No explicit transaction control — each query is auto-committed","Isolation level is fixed at server startup, not per-query","Long-running transactions may block other queries due to lock contention"],"requires":["PostgreSQL transaction isolation level configured at server startup"],"input_types":["SQL SELECT query (executed within configured isolation level)"],"output_types":["Query result set (JSON) with consistency guarantees"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"postgresql-mcp-server__cap_8","uri":"capability://memory.knowledge.mcp.resource.exposure.for.schema.and.query.result.caching","name":"mcp resource exposure for schema and query result caching","description":"Exposes schema metadata and query results as MCP Resources, enabling MCP clients to cache and reference data across multiple interactions without re-querying. The server implements the MCP Resource primitive to serve schema information, table metadata, and optionally cached query results as named, versioned resources. Clients can subscribe to resource updates and invalidate caches when schema changes.","intents":["Cache schema metadata to avoid repeated introspection queries","Reference query results across multiple LLM interactions","Enable MCP clients to maintain consistent context about database state"],"best_for":["Long-running LLM conversations that reference the same database","Multi-turn interactions where schema context should persist","Clients with limited bandwidth or high query latency"],"limitations":["Resources are static snapshots — no real-time updates if schema changes","Client-side cache invalidation requires explicit resource refresh","Large result sets may exceed resource size limits in some MCP clients","No built-in versioning or change tracking — clients must manually detect schema changes"],"requires":["MCP client that supports Resource primitives (most modern MCP clients do)","Schema metadata must be stable or explicitly refreshed"],"input_types":["Resource name/URI (e.g., 'schema://public.users')"],"output_types":["Resource content (JSON): schema metadata, query results, or other structured data","Resource metadata: MIME type, size, last modified"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"postgresql-mcp-server__cap_9","uri":"capability://automation.workflow.configurable.database.connection.parameters.with.environment.variable.support","name":"configurable database connection parameters with environment variable support","description":"Accepts PostgreSQL connection parameters (host, port, database, user, password, SSL mode) via environment variables or configuration files, enabling flexible deployment across different environments without code changes. The server validates connection parameters at startup and establishes the connection pool using the provided configuration. Supports SSL/TLS encryption for secure remote connections.","intents":["Deploy the MCP server to different environments (dev, staging, prod) with different databases","Secure database credentials using environment variables instead of hardcoding","Enable SSL/TLS encryption for remote PostgreSQL connections"],"best_for":["Production deployments with environment-specific configuration","Teams using containerization (Docker) or orchestration (Kubernetes)","Organizations with strict credential management policies"],"limitations":["Connection parameters must be set before server startup — no runtime reconfiguration","Environment variables are not validated until connection attempt — invalid config may not be detected until first query","No built-in credential rotation — requires external secret management","SSL certificate validation depends on PostgreSQL client library configuration"],"requires":["Environment variables: DATABASE_URL or individual PGHOST, PGPORT, PGDATABASE, PGUSER, PGPASSWORD","PostgreSQL server accessible at configured host:port","SSL certificates (if using SSL mode)"],"input_types":["Environment variables or configuration file with PostgreSQL connection parameters"],"output_types":["Connection status (implicit — reflected in server startup success/failure)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"postgresql-mcp-server__headline","uri":"capability://tool.use.integration.mcp.server.for.postgresql.database.interaction","name":"mcp server for postgresql database interaction","description":"An official Model Context Protocol (MCP) server designed for seamless interaction with PostgreSQL databases, enabling schema inspection, SQL query execution, and safe database exploration with connection pooling support.","intents":["best MCP server for PostgreSQL","MCP server for database exploration","MCP server for SQL query execution","PostgreSQL server with connection pooling","official PostgreSQL MCP server"],"best_for":["developers needing safe database interaction"],"limitations":["not a production-ready solution"],"requires":["PostgreSQL database"],"input_types":["SQL queries"],"output_types":["query results"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":60,"verified":false,"data_access_risk":"high","permissions":["PostgreSQL 12+ database instance","Node.js 18+ (TypeScript implementation)","MCP-compatible client (Claude Desktop, custom MCP client, or MCP SDK)","Network connectivity to PostgreSQL server","PostgreSQL user account with SELECT privileges on target tables","Connection pooling configured (default: 10 connections)","Query must be valid SQL SELECT syntax","Writable filesystem or log aggregation endpoint","Log retention and privacy policies in place","PostgreSQL information_schema access (standard on all PostgreSQL installations)"],"failure_modes":["MCP protocol overhead adds ~50-100ms per request due to JSON-RPC serialization","No built-in query optimization — LLM-generated queries may be inefficient without explicit guidance","Requires MCP client support; 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