{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-designcomputer-mysql_mcp_server","slug":"mcp-designcomputer-mysql_mcp_server","name":"mysql_mcp_server","type":"mcp","url":"https://github.com/designcomputer/mysql_mcp_server","page_url":"https://unfragile.ai/mcp-designcomputer-mysql_mcp_server","categories":["mcp-servers"],"tags":["ai","mcp","model-context-protocol","mysql"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-designcomputer-mysql_mcp_server__cap_0","uri":"capability://tool.use.integration.mcp.compliant.database.resource.discovery.and.enumeration","name":"mcp-compliant database resource discovery and enumeration","description":"Implements the Model Context Protocol resource listing interface to dynamically enumerate available MySQL tables and schemas without requiring manual configuration. The server translates MCP resource requests into INFORMATION_SCHEMA queries, returning structured metadata about available tables that AI assistants can then interact with. This enables clients to discover database structure at runtime rather than relying on static configuration.","intents":["I want my AI assistant to automatically discover what tables exist in my MySQL database","I need the AI to understand the database schema before executing queries","I want to expose only specific tables to the AI without hardcoding a list"],"best_for":["AI developers building Claude integrations with MySQL backends","Teams deploying AI agents that need dynamic database awareness","Organizations wanting to avoid manual schema configuration in AI systems"],"limitations":["Resource listing reflects only table-level metadata; column-level schema details require separate read operations","No built-in filtering of system tables — all accessible tables are enumerated unless database user permissions restrict them","Discovery latency depends on INFORMATION_SCHEMA query performance, which can be slow on databases with thousands of tables"],"requires":["MySQL 5.7+","Python 3.9+","mcp Python package","mysql-connector-python package","Valid MySQL user with SELECT privilege on INFORMATION_SCHEMA"],"input_types":["MCP resource list request (protocol-level)"],"output_types":["structured JSON array of table names and metadata"],"categories":["tool-use-integration","database-discovery"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-designcomputer-mysql_mcp_server__cap_1","uri":"capability://data.processing.analysis.controlled.table.data.reading.with.row.level.access.limits","name":"controlled table data reading with row-level access limits","description":"Implements MCP resource reading to fetch table data with built-in pagination and row limits, preventing AI assistants from accidentally loading entire large tables into context. The server translates resource read requests into SELECT queries with LIMIT clauses, returning structured JSON representations of table rows. This capability enforces a safety boundary by capping the amount of data returned per request, protecting against context window exhaustion and excessive database load.","intents":["I want the AI to read sample data from a table without loading millions of rows","I need to prevent the AI from accidentally querying a 10GB table","I want to expose table data to the AI with automatic pagination"],"best_for":["Teams deploying AI agents against production databases with large tables","Developers building read-only analytics interfaces powered by AI","Organizations needing to balance AI context window constraints with data access"],"limitations":["Row limits are fixed per request — no streaming or cursor-based pagination for large result sets","No column-level filtering — all columns are returned unless the underlying database user has column-level restrictions","Sorting and filtering must be implemented by the AI through SQL tool execution, not through the resource reading interface","Large text columns (BLOBs, JSON) are returned in full, potentially consuming significant context"],"requires":["MySQL 5.7+","Python 3.9+","mcp Python package","mysql-connector-python package","Valid MySQL user with SELECT privilege on target tables"],"input_types":["MCP resource read request with table name"],"output_types":["JSON array of row objects with column names as keys"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-designcomputer-mysql_mcp_server__cap_10","uri":"capability://safety.moderation.error.handling.and.user.friendly.error.messages.for.query.failures","name":"error handling and user-friendly error messages for query failures","description":"Catches MySQL exceptions (connection errors, syntax errors, permission errors, etc.) and translates them into readable error messages that are returned to the AI assistant. The server distinguishes between different error types (syntax errors, permission denied, table not found, etc.) and provides context-specific guidance. This enables the AI to understand what went wrong and attempt corrective actions without requiring manual debugging.","intents":["I want the AI to understand why a query failed and fix it automatically","I need clear error messages that help the AI debug SQL syntax errors","I want the AI to know when it lacks permissions to access a table"],"best_for":["Interactive AI query tools where the AI needs to refine queries based on feedback","Debugging workflows where error messages guide the AI toward correct queries","Multi-turn conversations where the AI learns from query failures"],"limitations":["Error messages are generic — no query-specific suggestions for fixing syntax errors","Permission errors don't distinguish between table-level and column-level restrictions","Connection errors are not automatically recovered — the server must be restarted","No error logging — errors are only returned to the client, not persisted for debugging"],"requires":["Python 3.9+","mysql-connector-python package"],"input_types":["SQL query string"],"output_types":["error message string or query result"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-designcomputer-mysql_mcp_server__cap_2","uri":"capability://tool.use.integration.sql.query.execution.with.validation.and.error.handling.via.mcp.tools","name":"sql query execution with validation and error handling via mcp tools","description":"Exposes SQL query execution as an MCP tool that AI assistants can invoke with structured input validation. The server receives SQL queries through the MCP tool calling interface, executes them against MySQL using mysql-connector-python, and returns results as structured JSON or error messages. This capability includes error handling that translates MySQL exceptions into readable messages for the AI, enabling iterative query refinement and debugging.","intents":["I want the AI to execute custom SQL queries to analyze data","I need the AI to perform joins, aggregations, and complex queries beyond simple table reads","I want the AI to get clear error messages when a query fails so it can fix and retry"],"best_for":["Data analysts using AI to explore and query databases interactively","Developers building AI-powered business intelligence tools","Teams needing AI to perform complex analytical queries on MySQL data"],"limitations":["No query timeout enforcement — long-running queries can block the MCP server; requires external timeout configuration","No transaction support — each query is executed independently; multi-statement transactions must be handled by the AI","No prepared statement support — queries are executed as raw strings, increasing SQL injection risk if input validation is insufficient","Result sets are fully materialized in memory before being returned, causing memory issues with very large result sets (>100MB)"],"requires":["MySQL 5.7+","Python 3.9+","mcp Python package","mysql-connector-python package","Valid MySQL user with appropriate SELECT/INSERT/UPDATE/DELETE privileges"],"input_types":["SQL query string (via MCP tool input)"],"output_types":["JSON array of result rows, or error message string"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-designcomputer-mysql_mcp_server__cap_3","uri":"capability://safety.moderation.environment.based.secure.credential.management.for.database.connections","name":"environment-based secure credential management for database connections","description":"Manages MySQL connection credentials through environment variables rather than embedding them in code or configuration files. The server reads database host, port, username, password, and database name from the environment at startup, establishing a single persistent connection that is reused for all subsequent requests. This pattern isolates credential storage from the application code and enables secure deployment in containerized and cloud environments.","intents":["I want to deploy the MCP server securely without hardcoding database credentials","I need to use different database credentials in dev, staging, and production without code changes","I want to rotate database passwords without redeploying the application"],"best_for":["DevOps teams deploying MCP servers in Docker or Kubernetes","Organizations with strict credential management policies","Teams using CI/CD pipelines that inject secrets as environment variables"],"limitations":["Credentials are loaded at server startup only — changing environment variables requires server restart","No credential rotation without downtime — long-lived connections persist with original credentials","Environment variables are visible to all processes running as the same user, creating local privilege escalation risk","No audit logging of credential access or connection attempts"],"requires":["Python 3.9+","Environment variables: MYSQL_HOST, MYSQL_PORT, MYSQL_USER, MYSQL_PASSWORD, MYSQL_DATABASE","mysql-connector-python package"],"input_types":["environment variables (system-level)"],"output_types":["active MySQL connection object"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-designcomputer-mysql_mcp_server__cap_4","uri":"capability://tool.use.integration.mcp.protocol.server.implementation.with.stdio.transport","name":"mcp protocol server implementation with stdio transport","description":"Implements a full MCP server that communicates with clients through standard input/output (stdio) streams, following the Model Context Protocol specification. The server handles MCP message serialization/deserialization, implements the resource and tool interfaces, and manages the request-response lifecycle. This transport mechanism enables integration with Claude Desktop, VS Code, and other MCP-aware applications without requiring network configuration.","intents":["I want to integrate MySQL access into Claude Desktop without managing a separate network service","I need the MCP server to work with local AI tools that support the MCP protocol","I want to avoid exposing the database server to the network"],"best_for":["Individual developers using Claude Desktop with local MySQL databases","Teams deploying MCP servers in containerized environments with stdio-based orchestration","Organizations preferring local-first AI integrations without network exposure"],"limitations":["Stdio transport is single-threaded — only one client connection per server instance","No built-in connection pooling — each server instance maintains a single database connection","Scaling requires running multiple server instances with separate database connections, increasing resource consumption","Debugging stdio-based communication is difficult without specialized MCP protocol analyzers"],"requires":["Python 3.9+","mcp Python package (>=0.1.0)","Client that supports MCP protocol over stdio (Claude Desktop, VS Code with MCP extension, etc.)"],"input_types":["MCP protocol messages via stdin (JSON-RPC format)"],"output_types":["MCP protocol messages via stdout (JSON-RPC format)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-designcomputer-mysql_mcp_server__cap_5","uri":"capability://automation.workflow.database.connection.pooling.and.lifecycle.management","name":"database connection pooling and lifecycle management","description":"Manages a persistent MySQL connection that is established at server startup and reused across all incoming requests. The server handles connection initialization, error recovery, and graceful shutdown, ensuring that database connections are properly closed when the server terminates. This approach reduces connection overhead compared to creating new connections per request, but requires careful handling of connection state and error recovery.","intents":["I want to minimize database connection overhead for high-frequency AI queries","I need the server to handle database disconnections gracefully and reconnect automatically","I want to ensure database connections are properly closed when the server shuts down"],"best_for":["Deployments with moderate query frequency (10-100 queries per minute)","Environments where connection establishment is expensive (high network latency, SSL handshakes)","Single-client scenarios where connection pooling complexity is unnecessary"],"limitations":["Single connection per server instance — no concurrent query execution; queries are serialized","No connection pooling — high-concurrency scenarios require multiple server instances","Connection state is not isolated between requests — long-running queries block subsequent requests","No automatic reconnection on network failures — requires manual server restart if database connection is lost","Memory leaks possible if queries return large result sets that are not properly garbage collected"],"requires":["Python 3.9+","mysql-connector-python package","MySQL server with network connectivity from the server host"],"input_types":["connection configuration (host, port, user, password, database)"],"output_types":["active MySQL connection object"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-designcomputer-mysql_mcp_server__cap_6","uri":"capability://tool.use.integration.integration.with.claude.desktop.and.vs.code.via.mcp.configuration","name":"integration with claude desktop and vs code via mcp configuration","description":"Provides configuration templates and documentation for integrating the MySQL MCP server with Claude Desktop and VS Code through their respective MCP configuration files. The server can be registered as an MCP provider in Claude Desktop's configuration, enabling Claude to access MySQL databases through the server's resource and tool interfaces. This integration is declarative — the client application reads the configuration and spawns the server process with appropriate environment variables.","intents":["I want to add MySQL database access to Claude Desktop without writing code","I need to configure VS Code to use the MySQL MCP server for database queries","I want to manage multiple MCP servers (MySQL, filesystem, etc.) in a single Claude Desktop configuration"],"best_for":["Claude Desktop users who want to query MySQL databases interactively","VS Code users building AI-powered database tools","Teams standardizing on MCP for AI-database integration"],"limitations":["Configuration is static — adding new databases requires editing configuration files and restarting the client","No built-in UI for managing MCP server configurations — requires manual JSON editing","Environment variable injection is client-specific — different clients (Claude Desktop vs VS Code) use different configuration formats","No automatic server discovery — each database requires explicit configuration entry"],"requires":["Claude Desktop 0.1.0+ or VS Code with MCP extension","Python 3.9+ installed and in system PATH","mysql_mcp_server package installed via pip","Valid MySQL credentials and connection parameters"],"input_types":["MCP configuration JSON (claude_desktop_config.json or VS Code settings)"],"output_types":["spawned server process with stdio transport"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-designcomputer-mysql_mcp_server__cap_7","uri":"capability://automation.workflow.docker.containerization.and.deployment.support","name":"docker containerization and deployment support","description":"Provides Docker configuration and deployment guidance for running the MySQL MCP server in containerized environments. The server can be packaged as a Docker image with Python, dependencies, and the mysql_mcp_server package pre-installed, enabling deployment in Kubernetes, Docker Compose, and other container orchestration platforms. Environment variables are passed to the container at runtime, allowing credential injection through container orchestration secrets management.","intents":["I want to deploy the MySQL MCP server in Kubernetes with automatic secret injection","I need to run the MCP server in a Docker container alongside other services","I want to use Docker Compose to manage the MCP server and MySQL database together"],"best_for":["Teams deploying MCP servers in Kubernetes or Docker Compose environments","Organizations with container-based CI/CD pipelines","DevOps teams managing multiple AI services in containerized infrastructure"],"limitations":["Docker image size includes Python runtime and dependencies, increasing deployment artifact size (~200-300MB)","No built-in health checks — requires external liveness/readiness probe configuration","Single-threaded server means container resources (CPU, memory) are underutilized for high-concurrency workloads","No automatic scaling — horizontal scaling requires manual container orchestration configuration"],"requires":["Docker 20.10+","Docker Compose 2.0+ (for Docker Compose deployments)","Kubernetes 1.20+ (for Kubernetes deployments)","Container registry access for image storage"],"input_types":["Dockerfile or docker-compose.yml configuration"],"output_types":["running container with stdio-based MCP server"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-designcomputer-mysql_mcp_server__cap_8","uri":"capability://automation.workflow.pypi.package.distribution.and.version.management","name":"pypi package distribution and version management","description":"Distributes the MySQL MCP server as a Python package on PyPI, enabling installation via pip with automatic dependency resolution. The package includes version pinning for mcp and mysql-connector-python, ensuring compatibility across installations. Users can install specific versions or use version constraints to manage upgrades, and the package is integrated with standard Python packaging tools (setuptools, pyproject.toml).","intents":["I want to install the MySQL MCP server with a single pip command","I need to pin the MCP server version in my requirements.txt to ensure reproducible deployments","I want to upgrade to the latest version of the MCP server without manual configuration"],"best_for":["Python developers using standard package management workflows","Teams with pip-based dependency management in CI/CD pipelines","Organizations standardizing on PyPI for internal tool distribution"],"limitations":["PyPI distribution requires public package visibility — no private package hosting without additional infrastructure","Version pinning can create dependency conflicts if mcp or mysql-connector-python have incompatible versions","No automatic updates — users must manually run pip upgrade commands or use automated dependency management tools","Package size includes all dependencies, increasing installation time on slow networks"],"requires":["Python 3.9+","pip package manager","Network access to PyPI (pypi.org)"],"input_types":["pip install command with optional version specifier"],"output_types":["installed mysql_mcp_server package with dependencies"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-designcomputer-mysql_mcp_server__cap_9","uri":"capability://data.processing.analysis.query.result.serialization.to.json.with.type.preservation","name":"query result serialization to json with type preservation","description":"Converts MySQL query results into JSON format with automatic type mapping for MySQL data types (INT, VARCHAR, DATETIME, DECIMAL, etc.). The server preserves type information during serialization, converting MySQL-specific types into JSON-compatible representations (e.g., DATETIME to ISO 8601 strings, DECIMAL to numeric strings to avoid precision loss). This enables AI assistants to work with structured data while maintaining semantic understanding of data types.","intents":["I want the AI to understand that a column contains dates, not just strings","I need decimal values to be preserved without floating-point precision loss","I want query results formatted as JSON that the AI can easily parse and analyze"],"best_for":["Data analysis workflows where type information is important for AI reasoning","Financial applications where decimal precision must be preserved","Time-series analysis where datetime types need semantic understanding"],"limitations":["BLOB and BINARY types are not serialized — queries returning binary data will fail or truncate","JSON type columns are returned as strings, not as nested JSON objects","Custom MySQL types (ENUM, SET) are converted to strings, losing type semantics","Large result sets with many columns can produce very large JSON payloads, consuming AI context"],"requires":["Python 3.9+","mysql-connector-python package","MySQL 5.7+ (for standard type support)"],"input_types":["MySQL query result rows with typed columns"],"output_types":["JSON array of objects with string keys and typed values"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["MySQL 5.7+","Python 3.9+","mcp Python package","mysql-connector-python package","Valid MySQL user with SELECT privilege on INFORMATION_SCHEMA","Valid MySQL user with SELECT privilege on target tables","Valid MySQL user with appropriate SELECT/INSERT/UPDATE/DELETE privileges","Environment variables: MYSQL_HOST, MYSQL_PORT, MYSQL_USER, MYSQL_PASSWORD, MYSQL_DATABASE","mcp Python package (>=0.1.0)","Client that supports MCP protocol over stdio (Claude Desktop, VS Code with MCP extension, etc.)"],"failure_modes":["Resource listing reflects only table-level metadata; column-level schema details require separate read operations","No built-in filtering of system tables — all accessible tables are enumerated unless database user permissions restrict them","Discovery latency depends on INFORMATION_SCHEMA query performance, which can be slow on databases with thousands of tables","Row limits are fixed per request — no streaming or cursor-based pagination for large result sets","No column-level filtering — all columns are returned unless the underlying database user has column-level restrictions","Sorting and filtering must be implemented by the AI through SQL tool execution, not through the resource reading interface","Large text columns (BLOBs, JSON) are returned in full, potentially consuming significant context","Error messages are generic — no query-specific suggestions for fixing syntax errors","Permission errors don't distinguish between table-level and column-level restrictions","Connection errors are not automatically recovered — the server must be restarted","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4657537928751069,"quality":0.32,"ecosystem":0.52,"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":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:34.856Z","last_commit":"2025-06-05T11:05:28Z"},"community":{"stars":1241,"forks":232,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-designcomputer-mysql_mcp_server","compare_url":"https://unfragile.ai/compare?artifact=mcp-designcomputer-mysql_mcp_server"}},"signature":"9/DK3Aaw1kVlShYYg/rvRErZl7iePkTMSAvMBdqJlw1IHAQRmKAazhIurs2gsK/4R85fvuJAVp4Ya24y1v9XCA==","signedAt":"2026-06-21T18:19:06.872Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-designcomputer-mysql_mcp_server","artifact":"https://unfragile.ai/mcp-designcomputer-mysql_mcp_server","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-designcomputer-mysql_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"}}