| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables Claude and other MCP clients to create, read, update, and delete work items (user stories, bugs, tasks) in Azure DevOps projects through standardized MCP tool calls. Translates MCP function schemas into Azure DevOps REST API calls, handling authentication via Personal Access Tokens (PAT) and marshaling work item fields (title, description, state, assignee, area path, iteration) between client and server.
Unique: Implements MCP server pattern specifically for Azure DevOps, translating MCP tool schemas directly to Azure DevOps REST API endpoints with PAT-based authentication, enabling Claude and other MCP clients to manipulate work items without custom integrations
vs alternatives: Provides native MCP integration for Azure DevOps work items, whereas alternatives like Azure DevOps CLI or REST API clients require manual orchestration and lack Claude-native tool calling
Exposes git repository operations (clone, branch listing, commit history, pull request creation/review) and repository metadata queries through MCP tool calls. Translates MCP requests into Azure DevOps Git REST API calls, managing authentication and handling repository references (project, repo name, branch names) to enable Claude to interact with source control without direct git CLI access.
Unique: Provides MCP-native git repository operations for Azure Repos, abstracting Azure DevOps Git REST API behind MCP tool schemas, enabling Claude to query branch/commit state and create PRs without git CLI or direct API knowledge
vs alternatives: Simpler than managing git CLI or REST API clients directly; provides Claude-native tool calling for Azure Repos operations, whereas GitHub-focused tools (GitHub MCP) don't support Azure DevOps
Enables triggering, querying, and monitoring Azure Pipelines (CI/CD) builds through MCP tool calls. Translates MCP requests into Azure DevOps Pipelines REST API, handling pipeline definitions, build queuing, status polling, and artifact retrieval. Supports parameterized pipeline execution (passing variables to pipeline runs) and build log streaming for debugging.
Unique: Exposes Azure Pipelines execution and monitoring as MCP tools, allowing Claude to queue builds with parameters and poll status, whereas most CI/CD integrations require webhook-based triggering or manual dashboard interaction
vs alternatives: Provides synchronous pipeline queuing and status queries via MCP, simpler than managing Azure DevOps REST API directly or setting up webhook-based automation
Provides access to test execution results, test case management, and test plan operations through MCP tool calls. Translates MCP requests into Azure DevOps Test Management REST API, enabling queries of test runs, test case status, and test plan metadata. Supports filtering by test suite, configuration, and outcome (passed/failed/skipped) to enable Claude to analyze test health and create test cases.
Unique: Integrates Azure Test Plans as MCP tools, allowing Claude to query test results and create test cases without manual dashboard navigation, whereas most test management tools lack conversational AI integration
vs alternatives: Provides Claude-native access to test results and test case management, simpler than parsing test reports manually or querying Azure DevOps REST API directly
Exposes project metadata, team membership, area paths, and iteration (sprint) information through MCP tool calls. Translates MCP requests into Azure DevOps Core REST API to retrieve organizational structure, team configurations, and project settings. Enables Claude to understand project context (available teams, iterations, area paths) for work item operations and team-aware task assignment.
Unique: Provides MCP-based project and team discovery, allowing Claude to query organizational structure and iteration metadata to inform work item creation and assignment, whereas most integrations assume static team/iteration knowledge
vs alternatives: Enables Claude to dynamically discover teams, iterations, and area paths, reducing manual configuration and enabling context-aware work item operations
Exposes release pipeline operations (create releases, approve deployments, query release status) through MCP tool calls. Translates MCP requests into Azure DevOps Release Management REST API, handling release definitions, deployment approvals, and environment-specific deployment status. Supports querying release history and triggering deployments to specific environments with approval workflows.
Unique: Provides MCP-based release and deployment management, allowing Claude to create releases, query deployment status, and approve deployments, whereas most release management tools require manual dashboard interaction or webhook-based automation
vs alternatives: Enables Claude to orchestrate multi-environment releases and approvals via conversational interface, simpler than managing Release Management REST API directly
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 @azure-devops/mcp at 35/100. @azure-devops/mcp leads on adoption, 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