{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm_npm-iflow-mcpgarethcott-enhanced-postgres-mcp-server","slug":"npm-iflow-mcpgarethcott-enhanced-postgres-mcp-server","name":"@iflow-mcp/garethcott_enhanced-postgres-mcp-server","type":"mcp","url":"https://www.npmjs.com/package/@iflow-mcp/garethcott_enhanced-postgres-mcp-server","page_url":"https://unfragile.ai/npm-iflow-mcpgarethcott-enhanced-postgres-mcp-server","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","claude","anthropic","postgres","postgresql","database","llm"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm_npm-iflow-mcpgarethcott-enhanced-postgres-mcp-server__cap_0","uri":"capability://data.processing.analysis.postgresql.query.execution.via.mcp.protocol","name":"postgresql query execution via mcp protocol","description":"Executes arbitrary SQL queries (SELECT, INSERT, UPDATE, DELETE) against PostgreSQL databases through the Model Context Protocol, translating LLM-generated SQL into database operations. Implements MCP resource and tool handlers that parse SQL strings, execute them via node-postgres driver, and return structured result sets with row counts and column metadata. Supports both read and write operations with connection pooling managed by the underlying pg library.","intents":["I want Claude to query my PostgreSQL database and return results as structured data","I need to let an LLM agent perform database writes (inserts, updates, deletes) safely","I want to expose database operations to Claude without building a custom API layer"],"best_for":["Teams building LLM agents that need real-time database access","Developers prototyping data-driven AI applications with PostgreSQL backends","Organizations migrating from REST APIs to MCP for LLM integration"],"limitations":["No built-in query validation or SQL injection prevention — relies on LLM prompt engineering and connection-level permissions","Synchronous query execution blocks MCP handler — long-running queries may timeout depending on client timeout settings","No transaction support — each query is auto-committed, limiting multi-step atomic operations","Result set size unbounded — large queries can exhaust memory if returning millions of rows"],"requires":["PostgreSQL 9.6 or higher","Node.js 16+","Valid PostgreSQL connection string (host, port, database, user, password)","node-postgres (pg) driver installed as peer dependency"],"input_types":["SQL query string (SELECT, INSERT, UPDATE, DELETE)","Connection parameters (host, port, database, user, password)"],"output_types":["JSON array of row objects","Row count (for INSERT/UPDATE/DELETE)","Column metadata (names, types)","Error messages with SQL context"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-iflow-mcpgarethcott-enhanced-postgres-mcp-server__cap_1","uri":"capability://memory.knowledge.mcp.resource.based.database.schema.introspection","name":"mcp resource-based database schema introspection","description":"Exposes PostgreSQL database schema (tables, columns, constraints, indexes) as MCP resources that Claude can query to understand database structure. Implements information_schema queries to retrieve table definitions, column types, primary keys, foreign keys, and indexes, returning structured metadata that helps LLMs generate correct SQL. Resources are registered with the MCP server and made available as queryable endpoints without requiring separate schema documentation.","intents":["I want Claude to understand my database schema before generating queries","I need to expose table structure and relationships to the LLM without manual documentation","I want Claude to discover available tables and columns dynamically at runtime"],"best_for":["Developers building schema-aware LLM agents","Teams with frequently-changing database schemas who need dynamic introspection","Organizations wanting to reduce prompt engineering overhead by auto-discovering schema"],"limitations":["Schema introspection queries add latency to MCP initialization — full schema discovery may take 500ms+ for large databases","No caching of schema metadata — each MCP session re-queries information_schema","Limited to PostgreSQL information_schema — custom types and extensions may not be fully represented","Does not expose row-level security policies or column-level permissions"],"requires":["PostgreSQL 9.6+","SELECT permissions on information_schema tables","MCP client that supports resource endpoints"],"input_types":["Database connection parameters"],"output_types":["JSON schema metadata (table names, column definitions, types, constraints)","Foreign key relationships","Index definitions"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-iflow-mcpgarethcott-enhanced-postgres-mcp-server__cap_2","uri":"capability://tool.use.integration.mcp.tool.based.sql.generation.and.execution","name":"mcp tool-based sql generation and execution","description":"Registers SQL execution as MCP tools that Claude can invoke with natural language intent, translating LLM tool calls into parameterized SQL queries. Implements tool schemas that define input parameters (table name, WHERE conditions, column selections), validates them against the database schema, and executes the resulting SQL through the node-postgres driver. Supports both simple CRUD operations and complex queries with filtering, sorting, and pagination parameters.","intents":["I want Claude to execute database operations by calling tools, not by generating raw SQL","I need parameterized queries to prevent SQL injection when Claude generates database operations","I want Claude to perform filtered queries with WHERE clauses, sorting, and limits without writing SQL"],"best_for":["Teams prioritizing safety by constraining LLM-generated SQL to predefined tool schemas","Developers building multi-turn agents that need repeatable, parameterized database operations","Organizations with strict SQL governance requirements"],"limitations":["Tool schemas must be manually defined — no automatic schema-to-tool generation","Complex queries (JOINs, aggregations, subqueries) difficult to express through tool parameters","Parameter validation adds ~50ms overhead per tool invocation","Limited to tools registered at MCP server startup — dynamic tool registration not supported"],"requires":["MCP client supporting tool calling (Claude, etc.)","Tool schema definitions in MCP format","PostgreSQL 9.6+"],"input_types":["Tool call with parameters (table, filters, columns, limit, offset)","Parameter values as strings or numbers"],"output_types":["JSON array of filtered rows","Row count","Execution status and error messages"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-iflow-mcpgarethcott-enhanced-postgres-mcp-server__cap_3","uri":"capability://automation.workflow.connection.pooling.and.lifecycle.management","name":"connection pooling and lifecycle management","description":"Manages PostgreSQL connection pooling using the node-postgres (pg) library, maintaining a pool of reusable database connections to reduce connection overhead. Implements connection initialization on MCP server startup, health checks to validate connections, and graceful shutdown that closes all pooled connections. Pool size and timeout parameters are configurable, allowing tuning for different workload patterns (high-concurrency agents vs. low-frequency queries).","intents":["I want multiple Claude interactions to reuse database connections efficiently","I need to limit the number of concurrent PostgreSQL connections to avoid exhausting database resources","I want the MCP server to cleanly disconnect from the database on shutdown"],"best_for":["Production deployments with sustained LLM agent traffic","Teams running multiple concurrent MCP clients against a single PostgreSQL instance","Developers optimizing for low-latency database access in high-frequency scenarios"],"limitations":["Connection pool size must be tuned manually — no adaptive pooling based on load","Idle connections held in pool consume PostgreSQL server resources","No built-in connection retry logic — failed connections are removed from pool without automatic reconnection","Pool configuration is static at startup — cannot adjust pool size at runtime"],"requires":["node-postgres (pg) library","PostgreSQL server with available connection slots","Node.js 16+"],"input_types":["Pool configuration (min/max connections, idle timeout, connection timeout)"],"output_types":["Active connection handle","Pool status (available connections, waiting clients)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-iflow-mcpgarethcott-enhanced-postgres-mcp-server__cap_4","uri":"capability://data.processing.analysis.error.handling.and.query.result.formatting","name":"error handling and query result formatting","description":"Catches PostgreSQL errors (syntax errors, constraint violations, permission errors) and formats them as structured MCP responses with error context and SQL details. Implements error classification to distinguish between client errors (malformed SQL), constraint violations (unique key, foreign key), and server errors (connection loss, out of memory). Result formatting converts PostgreSQL result objects into JSON-serializable structures with column metadata, row counts, and execution time.","intents":["I want Claude to understand why a query failed and adjust its approach","I need detailed error messages that help debug SQL generation issues","I want query results formatted consistently for Claude to parse and reason about"],"best_for":["Developers building multi-turn agents that need to recover from query failures","Teams debugging LLM-generated SQL issues in production","Organizations requiring detailed audit logs of database operations"],"limitations":["Error messages expose database structure and constraints to the LLM — may leak sensitive schema information","Large result sets (millions of rows) can cause JSON serialization to exceed token limits","No built-in result truncation — developers must implement pagination to avoid overwhelming Claude","Execution time measurement adds ~5ms overhead per query"],"requires":["PostgreSQL 9.6+","MCP client that can parse JSON error responses"],"input_types":["PostgreSQL error objects","Query result sets"],"output_types":["JSON error object with code, message, and context","Formatted result set with metadata","Execution statistics (rows affected, execution time)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-iflow-mcpgarethcott-enhanced-postgres-mcp-server__cap_5","uri":"capability://safety.moderation.write.operation.safety.constraints","name":"write operation safety constraints","description":"Enforces write operation safety through configurable constraints: read-only mode to disable INSERT/UPDATE/DELETE, table whitelisting to restrict which tables can be modified, and operation-level permissions (e.g., allow SELECT but deny DELETE). Implements constraint checking at the MCP tool handler level before executing queries, rejecting unsafe operations with clear error messages. Supports environment-based configuration to enable/disable write modes per deployment.","intents":["I want to restrict Claude to read-only access in production","I need to allow writes only to specific tables (e.g., logs, cache tables)","I want different write permissions for different MCP clients or deployment environments"],"best_for":["Production deployments where LLM agents should have limited write access","Teams with strict data governance requiring table-level access control","Organizations running untrusted or experimental LLM agents"],"limitations":["Constraints are enforced at MCP layer, not database layer — a compromised MCP server can bypass constraints","No row-level security — constraints are table-level only, cannot restrict writes to specific rows","Whitelist must be manually maintained — no automatic discovery of safe tables","Does not prevent indirect writes through triggers or stored procedures"],"requires":["Configuration file or environment variables defining constraints","PostgreSQL 9.6+"],"input_types":["Configuration (read-only flag, table whitelist, operation permissions)"],"output_types":["Constraint violation error message","Allowed operation confirmation"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["PostgreSQL 9.6 or higher","Node.js 16+","Valid PostgreSQL connection string (host, port, database, user, password)","node-postgres (pg) driver installed as peer dependency","PostgreSQL 9.6+","SELECT permissions on information_schema tables","MCP client that supports resource endpoints","MCP client supporting tool calling (Claude, etc.)","Tool schema definitions in MCP format","node-postgres (pg) library"],"failure_modes":["No built-in query validation or SQL injection prevention — relies on LLM prompt engineering and connection-level permissions","Synchronous query execution blocks MCP handler — long-running queries may timeout depending on client timeout settings","No transaction support — each query is auto-committed, limiting multi-step atomic operations","Result set size unbounded — large queries can exhaust memory if returning millions of rows","Schema introspection queries add latency to MCP initialization — full schema discovery may take 500ms+ for large databases","No caching of schema metadata — each MCP session re-queries information_schema","Limited to PostgreSQL information_schema — custom types and extensions may not be fully represented","Does not expose row-level security policies or column-level permissions","Tool schemas must be manually defined — no automatic schema-to-tool generation","Complex queries (JOINs, aggregations, subqueries) difficult to express through tool parameters","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"ecosystem":0.5000000000000001,"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":"2026-05-24T12:16:23.903Z","last_scraped_at":"2026-04-22T08:09:53.662Z","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=npm-iflow-mcpgarethcott-enhanced-postgres-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=npm-iflow-mcpgarethcott-enhanced-postgres-mcp-server"}},"signature":"uLz65toQyqrtZdx8qu5Vq95uoB6f2Zj2dp1cBJJRITaJOjF9Ytsw4tlPTKRU/TSVpBLJtfTuiafPGUyaxHVUAg==","signedAt":"2026-06-21T04:04:39.897Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-iflow-mcpgarethcott-enhanced-postgres-mcp-server","artifact":"https://unfragile.ai/npm-iflow-mcpgarethcott-enhanced-postgres-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=npm-iflow-mcpgarethcott-enhanced-postgres-mcp-server","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"}}