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Implements a standardized query interface that translates MCP tool calls into PostgreSQL information_schema queries, returning structured metadata that LLMs can use to understand database structure before constructing queries. The server maintains read-only access enforcement at the connection level, preventing accidental or malicious write operations.","intents":["I need to understand a PostgreSQL database schema so I can write correct queries without trial-and-error","I want an LLM to inspect table structures and relationships before generating SQL","I need to expose database metadata to Claude or other MCP clients for context-aware assistance"],"best_for":["LLM-assisted SQL generation workflows","Teams building AI agents that query PostgreSQL databases","Developers integrating Claude with existing PostgreSQL instances for schema-aware assistance"],"limitations":["Read-only access only — cannot retrieve dynamic statistics or real-time query plans","No support for custom types or extensions beyond standard PostgreSQL types","Schema introspection latency depends on database size; large schemas with thousands of tables may have noticeable delays","Does not expose row-level security policies or column-level permissions"],"requires":["PostgreSQL 10.0 or later","Network connectivity to PostgreSQL server","Valid database credentials with SELECT privilege on information_schema","MCP client implementation (e.g., Claude Desktop, custom MCP host)"],"input_types":["MCP tool call parameters (table name, schema name as strings)"],"output_types":["structured JSON with table metadata","column definitions with types and constraints","relationship and foreign key information"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-postgresql__cap_1","uri":"capability://tool.use.integration.mcp.based.database.query.execution.with.read.only.enforcement","name":"mcp-based database query execution with read-only enforcement","description":"Translates MCP tool calls into PostgreSQL queries and returns results through the MCP protocol, with built-in query validation and read-only enforcement. The server parses incoming MCP tool invocations, validates SQL against a whitelist or read-only filter, executes the query against the PostgreSQL connection, and serializes results back as structured MCP responses. Connection-level read-only mode prevents any write operations (INSERT, UPDATE, DELETE, DROP) from executing, even if a user attempts to inject them.","intents":["I want Claude to execute SELECT queries against my PostgreSQL database and return results in a structured format","I need to safely expose database query capabilities to an LLM without risking data modification","I want to integrate PostgreSQL query execution into an MCP-based agent workflow"],"best_for":["AI agents that need to query PostgreSQL for context or decision-making","LLM-powered analytics or reporting tools","Teams building multi-step workflows where LLMs need to fetch data mid-reasoning"],"limitations":["Read-only enforcement means no INSERT, UPDATE, DELETE, or DDL operations — suitable only for data retrieval workflows","Query timeout and result size limits depend on PostgreSQL server configuration; very large result sets may be truncated","No built-in query optimization or cost estimation — slow queries will block the MCP connection","Transaction isolation is limited to the scope of a single query; multi-step transactions are not supported"],"requires":["PostgreSQL 10.0 or later with network access","Database user with SELECT privilege on target tables","MCP client capable of invoking tools (Claude, custom MCP host)","Network connectivity and firewall rules allowing client to reach PostgreSQL"],"input_types":["SQL SELECT queries as strings","query parameters (if parameterized queries are supported)"],"output_types":["JSON-serialized result sets","error messages with SQL error details","row counts and metadata"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-postgresql__cap_2","uri":"capability://tool.use.integration.mcp.protocol.transport.and.tool.registry.binding","name":"mcp protocol transport and tool registry binding","description":"Implements the Model Context Protocol server specification, exposing database capabilities as a set of registered MCP tools that clients can discover and invoke. The server implements MCP's JSON-RPC 2.0 transport layer (typically over stdio or HTTP), maintains a tool registry that describes available database operations (schema introspection, query execution), and handles tool invocation requests from MCP clients. This enables seamless integration with MCP-compatible clients like Claude Desktop without requiring custom API wrappers.","intents":["I want to expose my PostgreSQL database as an MCP server that Claude Desktop can connect to directly","I need my LLM client to discover available database tools dynamically through the MCP protocol","I want to integrate PostgreSQL into a multi-tool MCP environment alongside other servers (GitHub, Slack, etc.)"],"best_for":["Claude Desktop users integrating PostgreSQL with AI assistance","Teams building MCP-based agent platforms with multiple tool providers","Developers creating custom MCP clients that need standardized database access"],"limitations":["MCP protocol overhead adds ~50-100ms per tool invocation due to JSON-RPC serialization and deserialization","Stdio transport (default) is blocking and single-threaded; concurrent requests from multiple clients are serialized","No built-in authentication beyond environment variables or configuration files — requires external auth layer for multi-user scenarios","Tool discovery is static at server startup; dynamic tool registration is not supported"],"requires":["MCP client implementation (Claude Desktop 0.1.0+, or custom MCP host)","Node.js 18+ or Python 3.9+ (depending on server implementation language)","Configuration file or environment variables with PostgreSQL connection details"],"input_types":["MCP tool call requests (JSON-RPC 2.0 format)","tool parameters as JSON objects"],"output_types":["MCP tool response messages (JSON-RPC 2.0)","structured tool results","error responses with diagnostic information"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-postgresql__cap_3","uri":"capability://automation.workflow.connection.pooling.and.lifecycle.management.for.postgresql.connections","name":"connection pooling and lifecycle management for postgresql connections","description":"Manages a pool of PostgreSQL connections with configurable pool size, timeout, and idle connection cleanup. The server maintains persistent connections to the database, reuses them across multiple tool invocations to reduce connection overhead, and implements graceful connection cleanup on server shutdown. Connection pooling is typically implemented using a library like pg-pool (Node.js) or psycopg2 connection pooling (Python), with configurable parameters for min/max pool size and idle timeout.","intents":["I want my MCP server to efficiently reuse database connections instead of creating a new connection for each query","I need to prevent connection exhaustion when multiple LLM clients invoke database tools simultaneously","I want to ensure database connections are properly cleaned up when the MCP server shuts down"],"best_for":["Production deployments of MCP PostgreSQL servers handling multiple concurrent requests","Long-running MCP servers that need to maintain stable connection pools","Teams concerned with database connection efficiency and resource utilization"],"limitations":["Connection pool size must be tuned based on expected concurrency; undersized pools cause queuing, oversized pools waste database resources","Idle connections consume memory and database resources; aggressive idle timeout may cause reconnection overhead","No built-in connection monitoring or health checks; stale connections may not be detected until query execution fails","Pool configuration is typically static at server startup; dynamic pool resizing is not supported"],"requires":["PostgreSQL server with sufficient max_connections setting to accommodate the pool","Connection pooling library (pg-pool for Node.js, psycopg2 for Python)","Configuration parameters for pool size, timeout, and idle cleanup"],"input_types":["pool configuration (min/max size, idle timeout, connection timeout)"],"output_types":["active database connections","pool statistics (size, idle count, wait queue length)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-postgresql__cap_4","uri":"capability://tool.use.integration.error.handling.and.diagnostic.reporting.through.mcp.responses","name":"error handling and diagnostic reporting through mcp responses","description":"Captures PostgreSQL errors (connection failures, syntax errors, permission errors, timeout errors) and translates them into structured MCP error responses that include diagnostic information. When a query fails, the server extracts the PostgreSQL error code, message, and context, formats it as an MCP error response, and returns it to the client. This enables LLMs to understand why a query failed and potentially retry or reformulate the query.","intents":["I want the LLM to understand why a query failed so it can retry or fix the query","I need diagnostic information about database errors to debug integration issues","I want to prevent raw PostgreSQL errors from leaking sensitive information to the LLM"],"best_for":["LLM-assisted SQL debugging and query refinement","Production systems where error diagnostics are needed for troubleshooting","Teams building resilient AI agents that can recover from database errors"],"limitations":["Error messages may expose schema details or table names that could be considered sensitive; sanitization is not built-in","Some PostgreSQL errors (e.g., connection timeouts) may not include detailed diagnostic information","Error context is limited to the scope of a single query; multi-step transaction errors are not tracked","No built-in error logging or alerting; errors are only reported through MCP responses"],"requires":["PostgreSQL error handling in the server implementation","MCP error response format support"],"input_types":["failed SQL queries","PostgreSQL error objects"],"output_types":["MCP error responses with error code and message","diagnostic information (line number, SQL context, constraint violation details)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-postgresql__cap_5","uri":"capability://safety.moderation.parameterized.query.support.with.sql.injection.prevention","name":"parameterized query support with sql injection prevention","description":"Supports parameterized queries (prepared statements) where query parameters are passed separately from the SQL template, preventing SQL injection attacks. The server accepts a SQL template with parameter placeholders (e.g., $1, $2 in PostgreSQL) and a separate array of parameter values, passes them to the PostgreSQL driver using the native parameterized query API, and returns results. This ensures that parameter values are never interpreted as SQL code, even if they contain SQL keywords or special characters.","intents":["I want to safely pass user-supplied or LLM-generated values into SQL queries without risk of SQL injection","I need to construct dynamic queries where some parts are SQL and others are data values","I want to ensure that even if an LLM generates a malicious query, parameter values cannot break out of their intended context"],"best_for":["Production systems where SQL injection prevention is critical","LLM-assisted query generation where the LLM may not understand SQL injection risks","Teams building multi-tenant systems where query isolation is essential"],"limitations":["Parameterized queries cannot be used for dynamic SQL structure (table names, column names, ORDER BY clauses) — only for data values","LLMs may not naturally generate parameterized queries; the MCP server must enforce parameterization or validate queries","Parameter binding adds minimal overhead (~5-10ms per query) but requires explicit parameter passing from the client","Not all PostgreSQL features support parameterization (e.g., some dynamic SQL constructs)"],"requires":["PostgreSQL driver with parameterized query support (pg, psycopg2, etc.)","MCP client that can pass parameters separately from SQL"],"input_types":["SQL template with parameter placeholders ($1, $2, etc.)","array of parameter values"],"output_types":["query results with parameters safely bound"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["PostgreSQL 10.0 or later","Network connectivity to PostgreSQL server","Valid database credentials with SELECT privilege on information_schema","MCP client implementation (e.g., Claude Desktop, custom MCP host)","PostgreSQL 10.0 or later with network access","Database user with SELECT privilege on target tables","MCP client capable of invoking tools (Claude, custom MCP host)","Network connectivity and firewall rules allowing client to reach PostgreSQL","MCP client implementation (Claude Desktop 0.1.0+, or custom MCP host)","Node.js 18+ or Python 3.9+ (depending on server implementation language)"],"failure_modes":["Read-only access only — cannot retrieve dynamic statistics or real-time query plans","No support for custom types or extensions beyond standard PostgreSQL types","Schema introspection latency depends on database size; large schemas with thousands of tables may have noticeable delays","Does not expose row-level security policies or column-level permissions","Read-only enforcement means no INSERT, UPDATE, DELETE, or DDL operations — suitable only for data retrieval workflows","Query timeout and result size limits depend on PostgreSQL server configuration; very large result sets may be truncated","No built-in query optimization or cost estimation — slow queries will block the MCP connection","Transaction isolation is limited to the scope of a single query; multi-step transactions are not supported","MCP protocol overhead adds ~50-100ms per tool invocation due to JSON-RPC serialization and deserialization","Stdio transport (default) is blocking and single-threaded; concurrent requests from multiple clients are serialized","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"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":"2026-06-17T09:51:04.047Z","last_scraped_at":"2026-05-03T14:00:18.053Z","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=postgresql","compare_url":"https://unfragile.ai/compare?artifact=postgresql"}},"signature":"stmSdIKblyzn6lZuMemkH4nWWLLfbk+qlPPuT+mMuxW9mA8l20aRTQ00cEzu1Nh60A21LOb962Vj6LDe720yBw==","signedAt":"2026-06-20T14:28:03.564Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/postgresql","artifact":"https://unfragile.ai/postgresql","verify":"https://unfragile.ai/api/v1/verify?slug=postgresql","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"}}