| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes Railway's core infrastructure operations through the Model Context Protocol, allowing LLM agents and Claude instances to programmatically query and manage Railway projects, services, deployments, and environments. Implements MCP server specification with Railway API client bindings, enabling structured tool calling for infrastructure automation without direct API knowledge.
Unique: Official Railway MCP server implementation with native Railway API client bindings, providing first-party integration that stays synchronized with Railway's API evolution and feature releases. Uses MCP's standardized tool schema format to expose Railway operations, enabling seamless integration with Claude and other MCP-compatible LLM clients without custom adapter code.
vs alternatives: More reliable and feature-complete than community-built Railway integrations because it's officially maintained by Railway and guaranteed to support new API features immediately, versus third-party tools that may lag behind API changes.
Automatically generates MCP-compliant tool schemas (JSON Schema format) from Railway API endpoints, mapping REST operations to structured function definitions that Claude and other LLM clients can invoke. Implements schema generation patterns that translate Railway API parameters, response types, and error codes into MCP tool specifications with proper type hints and validation.
Unique: Generates MCP schemas directly from Railway's official API client library, ensuring schemas always match actual API capabilities and parameter requirements. This approach eliminates manual schema maintenance and schema-drift issues that plague hand-written integrations.
vs alternatives: More maintainable than manually-written MCP schemas because schema generation is automated and tied to Railway's API versioning, whereas custom integrations require manual updates whenever Railway's API changes.
Manages Railway API authentication tokens within the MCP server context, accepting API credentials at server initialization and securely passing them to all Railway API calls. Implements credential handling patterns that keep tokens out of tool parameters (preventing exposure in LLM logs) while ensuring they're available to all downstream API operations.
Unique: Implements credential isolation at the MCP server boundary, preventing Railway API tokens from ever appearing in Claude's context window or tool parameters. This design pattern ensures tokens remain server-side only, reducing exposure surface compared to approaches that pass credentials through LLM context.
vs alternatives: More secure than passing Railway API tokens directly in tool parameters because tokens never enter the LLM's context window, reducing risk of accidental exposure in logs or conversation history.
Provides tools to query current deployment status (running, failed, building, etc.) and detect changes since last query, enabling LLM agents to monitor Railway deployments without continuous polling. Implements state tracking patterns that cache deployment metadata and compare against fresh API queries to identify status transitions, new errors, or completed builds.
Unique: Implements client-side state tracking within the MCP server to detect deployment changes without requiring Railway webhooks or external state storage. This approach allows change detection to work immediately without infrastructure setup, though at the cost of polling latency.
vs alternatives: Simpler to set up than webhook-based monitoring because it requires no external state store or webhook infrastructure, but trades real-time detection for polling latency and Railway API rate limit exposure.
Exposes Railway's environment variable and secret management APIs through MCP tools, allowing Claude to query, create, update, and delete environment variables across Railway services and environments. Implements secure parameter passing patterns that prevent secrets from being logged or exposed in tool parameters, using server-side secret handling instead.
Unique: Implements server-side secret handling where environment variable values are never exposed in tool parameters or Claude's context — only variable names and metadata are visible to the LLM, while actual values remain server-side. This pattern prevents accidental secret exposure in conversation logs.
vs alternatives: More secure than exposing environment variables directly to Claude because secret values never enter the LLM's context window, reducing risk of exposure in logs or conversation history.
Provides tools to discover and introspect Railway services, plugins, and their configurations within a project, returning metadata about available services, their ports, environment variables, and dependencies. Implements introspection patterns that query Railway's project structure and return structured metadata that Claude can use to understand the deployment topology.
Unique: Provides structured introspection of Railway project topology through MCP tools, allowing Claude to build a mental model of the deployment without requiring manual documentation. This enables Claude to make informed suggestions about service configurations and dependencies.
vs alternatives: More accessible than requiring developers to manually document their infrastructure because Claude can query the actual project structure from Railway's API, but less detailed than application-level introspection that would require code analysis.
Exposes Railway's deployment and service logs through MCP tools, allowing Claude to retrieve historical logs or stream real-time logs for debugging and monitoring. Implements log retrieval patterns that fetch logs from Railway's log storage and format them for LLM consumption, with optional filtering by service, environment, or time range.
Unique: Integrates with Railway's native log storage and retrieval APIs, providing direct access to deployment and service logs without requiring external log aggregation tools. This approach keeps logs within Railway's ecosystem and ensures logs are always synchronized with actual deployments.
vs alternatives: More convenient than external log aggregation tools because logs are retrieved directly from Railway without requiring separate log shipping or storage infrastructure, but less flexible than centralized logging systems that support cross-service correlation.
Provides MCP tools to trigger new deployments, redeploy specific versions, and rollback to previous deployments. Implements deployment orchestration patterns that queue deployment requests with Railway's build system and track deployment progress, enabling Claude to automate deployment workflows and recovery procedures.
Unique: Enables Claude to directly trigger and manage Railway deployments through MCP tools, allowing deployment automation without external CI/CD systems. This approach integrates deployment control directly into Claude's agent loop, enabling reactive deployment decisions based on monitoring or user requests.
vs alternatives: More responsive than traditional CI/CD pipelines because Claude can trigger deployments immediately in response to events or user requests, but less robust than dedicated CI/CD systems that provide pre-deployment validation and safety checks.
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs @railway/mcp-server at 32/100. @railway/mcp-server leads on adoption and ecosystem, while Supabase is stronger on quality.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities