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Implements MCP's tool-calling interface to expose database operations as callable functions with schema validation, parameter binding, and result serialization back to the LLM context.","intents":["Enable Claude to query my PostgreSQL database and retrieve structured data for analysis","Allow an LLM agent to write records to my database based on conversation context","Integrate database operations into multi-step LLM workflows without manual API wrapping"],"best_for":["Teams building Claude-powered applications that need real-time database access","Developers creating LLM agents that perform CRUD operations on PostgreSQL","Non-technical founders prototyping database-backed chatbots with Claude"],"limitations":["No built-in query optimization or cost estimation — unbounded queries can scan entire tables","Synchronous execution only — long-running queries block the MCP connection","No automatic transaction management — concurrent writes from multiple LLM calls may cause race conditions","SQL injection risk if LLM generates raw SQL without parameterization (requires careful prompt engineering)"],"requires":["PostgreSQL 12.0 or higher","Node.js 18.0 or higher","Valid PostgreSQL connection string (host, port, database, user, password)","MCP-compatible client (Claude Desktop, Cline, or custom MCP host)"],"input_types":["SQL query strings","Query parameters (strings, numbers, booleans, null)","Connection configuration (host, port, database credentials)"],"output_types":["JSON-serialized result sets (rows as objects)","Query metadata (rows affected, execution time)","Error messages with SQL context"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-enhanced-postgres-mcp-server__cap_1","uri":"capability://memory.knowledge.database.schema.introspection.and.exposure","name":"database schema introspection and exposure","description":"Automatically discovers PostgreSQL table schemas (columns, types, constraints, primary/foreign keys) and exposes them as MCP resources that the LLM can query to understand database structure. 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Implements schema validation on incoming tool calls to reject malformed queries before execution, with detailed error messages that guide the LLM to correct syntax.","intents":["Ensure the LLM only calls database operations with valid parameters","Provide the LLM with clear documentation of what queries it can execute","Catch parameter type mismatches before they reach the database"],"best_for":["Teams building production LLM agents where query safety is critical","Developers who want to constrain LLM behavior to specific database operations","Applications requiring audit trails of what the LLM attempted to execute"],"limitations":["Schema validation is syntactic only — does not prevent logically invalid queries (e.g., querying non-existent columns)","No support for custom validation rules beyond JSON Schema (e.g., regex patterns on string parameters)","Parameter binding is basic — does not support complex types like arrays or nested objects","Error messages from validation failures may confuse the LLM if they reference internal schema details"],"requires":["MCP client that supports tool schemas (Claude, Cline, etc.)","JSON Schema understanding in the LLM"],"input_types":["Tool call requests with parameters","JSON Schema definitions"],"output_types":["Validation success/failure responses","Detailed error messages with parameter hints"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-enhanced-postgres-mcp-server__cap_3","uri":"capability://automation.workflow.connection.pooling.and.lifecycle.management","name":"connection pooling and lifecycle management","description":"Manages PostgreSQL client connections using a connection pool (likely pg.Pool or similar) to reuse connections across multiple queries, reducing connection overhead. Handles connection initialization, error recovery, and graceful shutdown of the MCP server while ensuring no queries are orphaned. Implements connection timeout and idle timeout settings to prevent resource exhaustion.","intents":["Improve performance by reusing database connections across multiple LLM queries","Prevent connection leaks when the MCP server restarts or crashes","Handle database unavailability gracefully without hanging the LLM"],"best_for":["Production deployments where connection efficiency is critical","Long-running LLM agents that execute many queries over time","Teams deploying the MCP server in resource-constrained environments"],"limitations":["Connection pool size is fixed at startup — cannot dynamically scale with load","No built-in monitoring of pool utilization or connection health","Connection errors are not automatically retried — failed queries fail immediately","Pool exhaustion (all connections in use) blocks new queries until a connection is released"],"requires":["PostgreSQL server accepting connections","Sufficient file descriptors on the host OS (connection pooling uses one per connection)"],"input_types":["Connection pool configuration (pool size, timeout settings)"],"output_types":["Connection status (available, in-use, idle)","Error messages on connection failures"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-enhanced-postgres-mcp-server__cap_4","uri":"capability://safety.moderation.error.handling.and.query.failure.reporting","name":"error handling and query failure reporting","description":"Catches PostgreSQL errors (syntax errors, constraint violations, permission denied, etc.) and translates them into human-readable messages that are returned to the LLM. Preserves error context (line number, SQL state code) to help the LLM understand what went wrong and retry with corrected queries. Implements timeout handling for long-running queries.","intents":["Help the LLM understand why a query failed and how to fix it","Prevent database errors from crashing the MCP server","Provide detailed error context for debugging LLM-generated queries"],"best_for":["Teams building LLM agents that need to learn from query failures","Developers debugging why the LLM generates invalid SQL","Production systems where database errors must not crash the server"],"limitations":["Error messages may leak sensitive information (table names, column names) to the LLM","No automatic error recovery — the LLM must decide whether to retry","Timeout errors are generic — do not distinguish between slow queries and hung connections","Error messages are PostgreSQL-specific — may not be meaningful to non-SQL-fluent LLMs"],"requires":["PostgreSQL error handling in the Node.js pg library"],"input_types":["SQL queries that may fail"],"output_types":["Error messages with SQL state codes","Timeout notifications","Constraint violation details"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-enhanced-postgres-mcp-server__cap_5","uri":"capability://safety.moderation.read.only.query.mode.with.write.protection","name":"read-only query mode with write protection","description":"Optionally restricts the MCP server to execute only SELECT queries, blocking INSERT/UPDATE/DELETE operations at the MCP layer before they reach the database. Implements a query parser or regex-based filter to detect write operations and reject them with a clear error message. Useful for read-only access patterns or multi-user scenarios where only certain users should modify data.","intents":["Prevent the LLM from accidentally modifying data in production","Provide read-only database access to untrusted LLM agents","Enforce data governance policies at the MCP layer"],"best_for":["Teams deploying LLM agents in production where data safety is critical","Multi-user systems where some users should only read data","Prototyping phases where you want to prevent accidental data corruption"],"limitations":["Write protection is enforced at the MCP layer, not the database — a compromised MCP server can still write","Query parsing is simplistic (likely regex-based) — may not catch all write patterns (e.g., CTEs with INSERT)","No granular control — either all writes are blocked or all are allowed (no per-table or per-user restrictions)","Disables legitimate use cases like data import or ETL workflows"],"requires":["Configuration flag to enable read-only mode"],"input_types":["SQL queries"],"output_types":["Query execution results (SELECT only)","Rejection messages for write attempts"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-enhanced-postgres-mcp-server__cap_6","uri":"capability://data.processing.analysis.result.pagination.and.large.dataset.handling","name":"result pagination and large dataset handling","description":"Handles large query result sets by implementing pagination or streaming, preventing the MCP server from loading entire tables into memory. Returns results in chunks with metadata (total row count, current page) to allow the LLM to request additional data if needed. Implements configurable result limits to prevent runaway queries from consuming all available memory.","intents":["Query large tables without running out of memory","Stream results to the LLM incrementally rather than buffering everything","Prevent the LLM from accidentally requesting millions of rows"],"best_for":["Teams working with large PostgreSQL databases (millions of rows)","Long-running LLM agents that execute many queries over time","Resource-constrained deployments (edge devices, serverless functions)"],"limitations":["Pagination adds complexity — the LLM must understand how to request additional pages","Result limits may truncate important data — the LLM may not realize it's only seeing a subset","Streaming is not supported by all MCP clients — some may still buffer entire results","Offset-based pagination is slow for large offsets — no cursor-based pagination support"],"requires":["Configuration for result limit (default likely 1000-10000 rows)"],"input_types":["SQL queries","Pagination parameters (limit, offset)"],"output_types":["Paginated result sets","Metadata (total rows, current page, has_more flag)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-enhanced-postgres-mcp-server__cap_7","uri":"capability://automation.workflow.transaction.support.and.multi.statement.execution","name":"transaction support and multi-statement execution","description":"Optionally supports executing multiple SQL statements in a single transaction (BEGIN/COMMIT/ROLLBACK), allowing the LLM to perform atomic multi-step operations. Implements transaction isolation and rollback on error, ensuring data consistency. May support savepoints for nested transactions or partial rollbacks.","intents":["Execute multi-step database operations atomically (e.g., transfer funds between accounts)","Ensure data consistency when the LLM performs related updates","Rollback all changes if any step in a workflow fails"],"best_for":["Teams building LLM agents that perform complex, multi-step database operations","Applications requiring ACID guarantees for LLM-driven workflows","Financial or critical systems where data consistency is non-negotiable"],"limitations":["Transaction support adds complexity — the LLM must understand BEGIN/COMMIT semantics","Long-running transactions lock resources and may block other queries","No automatic deadlock detection or retry — the LLM must handle transaction conflicts","Savepoint support is optional — may not be implemented","Transaction state is not persisted across MCP server restarts"],"requires":["PostgreSQL transaction support (all versions)","Configuration flag to enable multi-statement execution"],"input_types":["Multi-statement SQL with BEGIN/COMMIT","Individual statements within a transaction"],"output_types":["Transaction status (active, committed, rolled back)","Results from each statement","Rollback notifications on error"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":34,"verified":false,"data_access_risk":"high","permissions":["PostgreSQL 12.0 or higher","Node.js 18.0 or higher","Valid PostgreSQL connection string (host, port, database, user, password)","MCP-compatible client (Claude Desktop, Cline, or custom MCP host)","PostgreSQL 12.0 or higher with information_schema accessible","Database user with SELECT privilege on information_schema","MCP client that supports resource discovery","MCP client that supports tool schemas (Claude, Cline, etc.)","JSON Schema understanding in the LLM","PostgreSQL server accepting connections"],"failure_modes":["No built-in query optimization or cost estimation — unbounded queries can scan entire tables","Synchronous execution only — long-running queries block the MCP connection","No automatic transaction management — concurrent writes from multiple LLM calls may cause race conditions","SQL injection risk if LLM generates raw SQL without parameterization (requires careful prompt engineering)","Schema discovery is synchronous and blocks on large databases (100+ tables may add 500ms+ latency)","Does not expose stored procedures, views, or custom types — only base tables","No caching of schema metadata — each MCP call re-queries information_schema","Foreign key relationships are discovered but not validated for referential integrity","Schema validation is syntactic only — does not prevent logically invalid queries (e.g., querying non-existent columns)","No support for custom validation rules beyond JSON Schema (e.g., regex patterns on string parameters)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.06848453616444125,"quality":0.41,"ecosystem":0.6000000000000001,"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":"2026-06-17T09:51:04.693Z","last_scraped_at":"2026-05-03T14:04:47.472Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":110,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=enhanced-postgres-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=enhanced-postgres-mcp-server"}},"signature":"84zQ1o9yR+vFy9kGqqflZgXnU2R1uuyxBgNfHjSnq/AvV2cBpmH8IdeO2KCSP5MDqeoNW9L0TegpBKVh8j41DQ==","signedAt":"2026-06-22T09:09:14.247Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/enhanced-postgres-mcp-server","artifact":"https://unfragile.ai/enhanced-postgres-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=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"}}