| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to create, update, and manage work items directly from their coding environment by leveraging Azure DevOps REST APIs. It utilizes a context-aware approach to ensure that the work items are linked to the relevant code changes and branches, streamlining the workflow. The integration is designed to minimize context switching, allowing developers to stay focused on their tasks without leaving their IDE.
Unique: Integrates directly with Azure DevOps APIs to provide real-time updates and context-aware work item management, unlike generic task managers.
vs alternatives: More seamless integration with Azure DevOps compared to standalone project management tools, as it operates directly within the coding environment.
This capability enables users to run Azure DevOps pipelines and fetch their statuses, logs, and results directly from their coding workflow. It employs a command-based interface that interacts with Azure DevOps pipeline APIs, allowing developers to trigger builds and deployments without switching contexts. The design focuses on providing immediate feedback and results, which enhances productivity.
Unique: Utilizes a direct API integration for pipeline management, allowing for real-time interaction and feedback, unlike traditional CI/CD tools that require separate interfaces.
vs alternatives: Faster access to pipeline management features compared to using the Azure DevOps web interface, as it operates within the developer's existing workflow.
This capability allows users to create and manage branches and pull requests in Azure DevOps repositories directly from their coding environment. It employs a context-aware command system that interacts with Azure DevOps Git APIs, enabling developers to create branches based on the current context of their work and initiate pull requests with relevant code changes automatically included. This reduces the friction of managing source control.
Unique: Provides a streamlined interface for branch and pull request management that is deeply integrated with Azure DevOps, unlike generic Git tools that lack context awareness.
vs alternatives: More efficient than using standalone Git clients, as it allows for context-driven branch creation and pull request initiation directly from the coding environment.
This capability enables users to create and edit wiki pages within Azure DevOps directly from their coding environment. It uses Azure DevOps REST APIs to manage wiki content, allowing developers to document their projects and share knowledge without leaving their workflow. The integration supports markdown formatting, making it easy to create rich documentation.
Unique: Offers direct editing capabilities for Azure DevOps wikis from the coding environment, enhancing documentation workflows compared to traditional markdown editors.
vs alternatives: More integrated than using separate documentation tools, as it allows for immediate updates to project documentation based on ongoing development.
This capability allows users to perform semantic searches across Azure DevOps resources, including work items, repositories, and pipelines. It leverages Azure DevOps APIs to retrieve relevant resources based on user queries, providing a context-aware search experience that helps developers find the information they need quickly. The implementation focuses on optimizing search relevance and speed.
Unique: Provides a unified search interface for various Azure DevOps resources, enhancing discoverability compared to using separate search tools for each resource type.
vs alternatives: Faster and more relevant search results than using the Azure DevOps web interface, as it is tailored for developer workflows.
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 45/100 vs azure-devops-mcp at 32/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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