{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-centralmind-gateway","slug":"centralmind-gateway","name":"centralmind/gateway","type":"mcp","url":"https://github.com/centralmind/gateway","page_url":"https://unfragile.ai/centralmind-gateway","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-centralmind-gateway__cap_0","uri":"capability://data.processing.analysis.ai.powered.database.schema.discovery.and.api.generation","name":"ai-powered database schema discovery and api generation","description":"Automatically analyzes database schemas by connecting to the source, extracting table/column/relationship metadata, sampling data to understand content patterns, and feeding this context to an LLM (via configurable AI provider) to generate optimized API configurations. The system creates a gateway.yaml file containing REST endpoint definitions, query parameters, and filtering logic tailored to the database structure without manual API design.","intents":["I want to expose my database to AI agents without writing API code","I need to quickly generate REST endpoints from an existing database schema","I want the API to be optimized for LLM consumption with smart filtering and pagination"],"best_for":["teams building AI agent backends with existing databases","developers migrating legacy databases to AI-accessible systems","non-technical founders prototyping data-driven AI applications"],"limitations":["LLM-generated configurations may require manual refinement for complex business logic","Schema analysis requires read access to database; cannot discover views or computed columns in all connectors","API generation quality depends on LLM provider quality and prompt engineering"],"requires":["Database connection credentials (host, port, username, password)","API key for LLM provider (OpenAI, Anthropic, or compatible)","Database must be one of: PostgreSQL, MySQL, ClickHouse, Snowflake, MSSQL, BigQuery, Oracle, SQLite, ElasticSearch","Go 1.18+ or Docker for running the CLI"],"input_types":["database connection string","database credentials","optional custom prompts for API generation"],"output_types":["gateway.yaml configuration file","OpenAPI/Swagger specification"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-centralmind-gateway__cap_1","uri":"capability://tool.use.integration.multi.protocol.api.server.hosting.rest.mcp.mcp.sse","name":"multi-protocol api server hosting (rest, mcp, mcp-sse)","description":"Hosts generated API configurations as three distinct server types from a single gateway.yaml definition: REST API with OpenAPI/Swagger documentation for HTTP clients, MCP (Model Context Protocol) server for direct AI agent integration via stdio/SSE transport, and MCP-SSE (Server-Sent Events) for browser-based agent communication. Each protocol exposes the same underlying data access logic through protocol-specific serialization and transport layers.","intents":["I want to serve my database API to both REST clients and AI agents using MCP","I need to host the same API endpoints in multiple formats without duplicating logic","I want to enable browser-based AI agents to query my database via MCP-SSE"],"best_for":["teams building multi-client AI systems (REST + MCP agents)","developers integrating with frameworks like LlamaIndex, LangChain that support MCP","organizations needing browser-based agent access without CORS complexity"],"limitations":["MCP server uses stdio transport by default; SSE variant requires HTTP wrapper","REST API and MCP expose identical query capabilities; no protocol-specific optimizations","Single gateway instance serves all protocols; no per-protocol rate limiting or authentication strategies"],"requires":["gateway.yaml configuration file with API definitions","Port availability (default 8080 for REST, configurable for MCP-SSE)","For MCP agents: client library supporting MCP protocol (e.g., LlamaIndex MCP integration)"],"input_types":["gateway.yaml configuration","HTTP requests (REST)","MCP tool call messages (MCP)","SSE stream messages (MCP-SSE)"],"output_types":["JSON responses (REST)","MCP tool result messages","OpenAPI/Swagger documentation"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-centralmind-gateway__cap_10","uri":"capability://automation.workflow.configuration.driven.api.definition.without.code.changes","name":"configuration-driven api definition without code changes","description":"Stores all API definitions, endpoint configurations, and server settings in a single gateway.yaml file that can be edited, versioned, and deployed independently of gateway binary. Changes to gateway.yaml (adding endpoints, modifying filters, adjusting pagination) take effect on server restart without recompilation, enabling rapid iteration and configuration management through version control.","intents":["I want to modify my API endpoints without rebuilding the gateway","I need to version control my API configuration separately from code","I want to deploy different API configurations to different environments"],"best_for":["teams using configuration-as-code practices","organizations with frequent API changes","DevOps teams managing multiple gateway instances"],"limitations":["gateway.yaml format and schema not fully documented; users must infer from examples","No built-in validation or linting for gateway.yaml; invalid configs only caught at runtime","No hot-reload; server restart required for configuration changes","No built-in configuration versioning or rollback mechanisms"],"requires":["gateway.yaml file in correct format","ability to restart gateway server"],"input_types":["gateway.yaml configuration file"],"output_types":["API endpoints","server configuration","security and performance settings"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-centralmind-gateway__cap_2","uri":"capability://data.processing.analysis.unified.database.connector.abstraction.with.multi.database.support","name":"unified database connector abstraction with multi-database support","description":"Implements a common connector interface that abstracts database-specific details (connection pooling, query dialects, data type mapping) for 9+ database systems including PostgreSQL, MySQL, Snowflake, BigQuery, Oracle, and ElasticSearch. Each connector handles authentication, schema introspection, query execution, and result serialization while exposing a uniform API to the gateway core, enabling single codebase to support heterogeneous data sources.","intents":["I want to query multiple database types through a single gateway interface","I need to migrate my API from PostgreSQL to Snowflake without rewriting gateway logic","I want to add support for a new database type without modifying core gateway code"],"best_for":["enterprises with multi-database architectures (data warehouses + operational DBs)","teams building data integration platforms","developers needing database-agnostic API layers"],"limitations":["Connector implementations vary in feature completeness; some databases lack full schema introspection support","Query optimization is database-specific; gateway cannot automatically translate queries across dialects","Connection pooling and timeout behavior differs per connector; no unified configuration","ElasticSearch connector may have different semantics than SQL connectors for filtering/aggregation"],"requires":["Database-specific driver (e.g., pq for PostgreSQL, mysql for MySQL, snowflake-go for Snowflake)","Valid connection credentials and network access to database","Database user with SELECT permissions on target tables/schemas"],"input_types":["database connection string","database credentials","SQL queries or ElasticSearch DSL"],"output_types":["structured result sets","schema metadata (tables, columns, types)","connection status and diagnostics"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-centralmind-gateway__cap_3","uri":"capability://tool.use.integration.plugin.based.extensibility.system.for.security.and.performance","name":"plugin-based extensibility system for security and performance","description":"Provides interceptor and wrapper-based plugin architecture allowing custom middleware to be injected into request/response pipeline without modifying core gateway code. Supports security plugins (authentication, authorization, rate limiting) and performance plugins (caching, query optimization, result transformation) as composable units that execute before/after API operations.","intents":["I want to add custom authentication or authorization logic to my API","I need to cache frequently accessed queries to reduce database load","I want to transform or redact sensitive data in API responses"],"best_for":["teams with custom security requirements beyond basic API key validation","organizations needing performance optimization without database changes","developers building extensible data platforms"],"limitations":["Plugin API and documentation appear minimal; custom plugin development requires reading gateway source code","No built-in plugin marketplace or community plugin registry","Plugin execution order and error handling behavior not clearly specified","Plugins execute in-process; no isolation or sandboxing between plugins"],"requires":["Go knowledge to implement custom plugins","Understanding of gateway's internal request/response structures","Ability to rebuild gateway binary with custom plugins linked"],"input_types":["HTTP requests (for security plugins)","query parameters and request bodies","API responses (for transformation plugins)"],"output_types":["modified requests/responses","authentication/authorization decisions","cached results or transformed data"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-centralmind-gateway__cap_4","uri":"capability://code.generation.editing.openapi.swagger.documentation.generation.from.database.schema","name":"openapi/swagger documentation generation from database schema","description":"Automatically generates OpenAPI 3.0 specification from discovered database schema and generated API configuration, creating interactive Swagger UI documentation that describes all available endpoints, parameters, request/response schemas, and data types. Documentation is served alongside REST API and can be used by API clients for code generation and validation.","intents":["I want to provide API documentation to developers without writing it manually","I need to generate client SDKs from my database API","I want to validate API requests against a schema before executing queries"],"best_for":["teams publishing APIs to external developers","organizations using API-first development practices","developers integrating with API client generators"],"limitations":["OpenAPI spec generation is derived from database schema; complex business logic or computed fields may not be represented","Documentation accuracy depends on quality of AI-generated API configuration","No support for custom documentation annotations or examples beyond schema inference"],"requires":["gateway.yaml configuration with API definitions","REST API server running to serve Swagger UI"],"input_types":["database schema metadata","generated API configuration"],"output_types":["OpenAPI 3.0 JSON/YAML specification","interactive Swagger UI HTML"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-centralmind-gateway__cap_5","uri":"capability://tool.use.integration.mcp.tool.schema.generation.and.function.calling.integration","name":"mcp tool schema generation and function calling integration","description":"Converts database API endpoints into MCP tool definitions with JSON schema specifications for parameters and return types, enabling AI agents to discover and invoke database queries as native function calls. Each generated tool maps to a database operation (SELECT, INSERT, UPDATE, DELETE) with schema-validated inputs and structured outputs compatible with LLM function-calling APIs.","intents":["I want AI agents to query my database as if calling native functions","I need to expose database operations as MCP tools for LlamaIndex or LangChain agents","I want schema validation on database queries before execution"],"best_for":["teams building AI agents that need database access","developers integrating with LlamaIndex, LangChain, or other MCP-compatible frameworks","organizations using Claude or other LLMs with function-calling capabilities"],"limitations":["MCP tool schemas are derived from database schema; complex business logic cannot be expressed in tool definitions","Tool discovery happens at server startup; dynamic schema changes require server restart","No support for tool versioning or deprecation","Agent must understand tool semantics; gateway does not provide semantic descriptions beyond parameter names"],"requires":["MCP server running (stdio or SSE transport)","MCP-compatible client library (e.g., LlamaIndex MCP integration)","gateway.yaml with API definitions"],"input_types":["MCP tool call messages with parameters","JSON-serialized query parameters"],"output_types":["MCP tool result messages","JSON-serialized database results","tool schema definitions (JSON Schema)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-centralmind-gateway__cap_6","uri":"capability://automation.workflow.docker.and.kubernetes.deployment.packaging","name":"docker and kubernetes deployment packaging","description":"Provides pre-built Docker images and Kubernetes manifests for containerized gateway deployment, enabling single-command deployment to cloud platforms. Includes environment variable configuration for database credentials, API keys, and server settings, allowing gateway instances to be spun up without code changes or rebuilds.","intents":["I want to deploy my gateway to Docker or Kubernetes without building from source","I need to run multiple gateway instances with different database configurations","I want to scale my gateway horizontally in a Kubernetes cluster"],"best_for":["DevOps teams deploying AI infrastructure","organizations using Kubernetes for container orchestration","teams needing rapid gateway deployment to cloud platforms"],"limitations":["Docker image size and startup time not specified; may be large for resource-constrained environments","Kubernetes manifests appear to be basic examples; no production-grade features like auto-scaling, resource limits, or health checks documented","No built-in support for secrets management (e.g., Kubernetes Secrets integration); credentials passed via environment variables","Single gateway instance per pod; no built-in clustering or state sharing"],"requires":["Docker 20.10+ or Kubernetes 1.20+","Container registry access for pulling images","Environment variables for database credentials and API keys"],"input_types":["Docker image","Kubernetes manifests (YAML)","environment variables"],"output_types":["running container","deployed Kubernetes pods","accessible API endpoints"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-centralmind-gateway__cap_7","uri":"capability://automation.workflow.cli.driven.workflow.for.discovery.configuration.and.server.launch","name":"cli-driven workflow for discovery, configuration, and server launch","description":"Provides command-line interface with three main subcommands: `discover` (runs schema analysis and AI-powered API generation), `start` (launches REST/MCP/MCP-SSE servers), and supporting commands for configuration management. CLI accepts database credentials, API keys, and server options as flags or environment variables, enabling fully scriptable deployment workflows without GUI or manual configuration files.","intents":["I want to automate gateway setup in CI/CD pipelines","I need to generate APIs for multiple databases in batch","I want to launch gateway servers with different configurations from scripts"],"best_for":["DevOps engineers building automated deployment pipelines","teams using infrastructure-as-code practices","developers integrating gateway into larger automation workflows"],"limitations":["CLI documentation appears minimal; complex workflows may require reading source code","No built-in support for configuration file validation or dry-run mode","Error messages and logging may not be detailed enough for debugging complex issues","No interactive mode for guided setup; all configuration must be provided upfront"],"requires":["gateway binary (pre-built or compiled from source)","database connection credentials as flags or environment variables","API key for LLM provider (for discover command)"],"input_types":["command-line flags","environment variables","database credentials"],"output_types":["gateway.yaml configuration file","running server process","console output and logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-centralmind-gateway__cap_8","uri":"capability://planning.reasoning.llm.powered.api.configuration.optimization","name":"llm-powered api configuration optimization","description":"Uses configurable LLM providers (OpenAI, Anthropic, or compatible APIs) to analyze database schema and generate optimized API configurations including intelligent filtering, pagination, sorting, and aggregation parameters. The system constructs context-aware prompts combining schema information, sample data, and API design best practices, then parses LLM responses into structured gateway.yaml configurations.","intents":["I want the API to automatically support smart filtering based on data semantics","I need pagination and sorting to be optimized for my specific data patterns","I want aggregation endpoints generated for common analytical queries"],"best_for":["teams building data APIs without API design expertise","organizations wanting semantically-aware APIs without manual design","developers using Claude, GPT-4, or other advanced LLMs"],"limitations":["Generated configurations may require manual refinement for complex business logic","LLM quality directly impacts API quality; weaker models may generate suboptimal configurations","No validation that generated configurations are executable against actual database","Prompt engineering is not exposed; users cannot customize LLM instructions","Cost per API generation depends on LLM provider pricing and schema size"],"requires":["API key for LLM provider (OpenAI, Anthropic, or compatible)","Database connection for schema analysis","LLM provider must support function calling or structured output"],"input_types":["database schema metadata","sample data from tables","optional custom API design instructions"],"output_types":["gateway.yaml configuration","API endpoint definitions","parameter schemas"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-centralmind-gateway__cap_9","uri":"capability://data.processing.analysis.data.sampling.and.pattern.analysis.for.api.optimization","name":"data sampling and pattern analysis for api optimization","description":"Extracts representative sample data from database tables during schema discovery to understand data distributions, cardinality, and content patterns. This sample data is included in LLM prompts to enable generation of optimized API configurations with intelligent defaults for filtering, pagination, and aggregation based on actual data characteristics rather than schema alone.","intents":["I want the API to have smart pagination defaults based on my actual data sizes","I need filtering parameters optimized for my data distributions","I want the API to suggest aggregations based on data patterns"],"best_for":["teams with large or complex datasets","organizations wanting data-aware API design","developers optimizing for specific data access patterns"],"limitations":["Sampling may be slow on very large tables; no configurable sample size limits","Sample data is sent to LLM provider; sensitive data may be exposed","Sampling is read-only but still requires database access and may impact performance","Sample data may not represent edge cases or rare values; API may not handle them well"],"requires":["database connection with SELECT permissions","sufficient database performance to execute sampling queries"],"input_types":["database tables","schema metadata"],"output_types":["sample data rows","data statistics (cardinality, distributions)","pattern analysis for LLM"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Database connection credentials (host, port, username, password)","API key for LLM provider (OpenAI, Anthropic, or compatible)","Database must be one of: PostgreSQL, MySQL, ClickHouse, Snowflake, MSSQL, BigQuery, Oracle, SQLite, ElasticSearch","Go 1.18+ or Docker for running the CLI","gateway.yaml configuration file with API definitions","Port availability (default 8080 for REST, configurable for MCP-SSE)","For MCP agents: client library supporting MCP protocol (e.g., LlamaIndex MCP integration)","gateway.yaml file in correct format","ability to restart gateway server","Database-specific driver (e.g., pq for PostgreSQL, mysql for MySQL, snowflake-go for Snowflake)"],"failure_modes":["LLM-generated configurations may require manual refinement for complex business logic","Schema analysis requires read access to database; cannot discover views or computed columns in all connectors","API generation quality depends on LLM provider quality and prompt engineering","MCP server uses stdio transport by default; SSE variant requires HTTP wrapper","REST API and MCP expose identical query capabilities; no protocol-specific optimizations","Single gateway instance serves all protocols; no per-protocol rate limiting or authentication strategies","gateway.yaml format and schema not fully documented; users must infer from examples","No built-in validation or linting for gateway.yaml; invalid configs only caught at runtime","No hot-reload; server restart required for configuration changes","No built-in configuration versioning or rollback mechanisms","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"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":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=centralmind-gateway","compare_url":"https://unfragile.ai/compare?artifact=centralmind-gateway"}},"signature":"r6kHr1IQmO6ayrslMhFWsaiJoImcAqOfuMV3ThTLnVG9UxbxpmTL/y1NCv4mwQme90tKSTSjAwguboU0ABQMBg==","signedAt":"2026-06-20T01:06:28.557Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/centralmind-gateway","artifact":"https://unfragile.ai/centralmind-gateway","verify":"https://unfragile.ai/api/v1/verify?slug=centralmind-gateway","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"}}