| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 15 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes 45+ Jira tools that map to the Jira REST API v3, including issue creation, retrieval, updates, and deletion with automatic field schema discovery. Uses a JiraClient mixin-based architecture that adapts payloads between Cloud (*.atlassian.net) and Server/Data Center deployments, handling custom fields, issue types, and project-specific field constraints through dynamic schema introspection rather than static field mappings.
Unique: Implements dual-platform field schema adaptation via JiraClient mixins that automatically normalize Cloud vs Server/Data Center API differences at runtime, eliminating the need for separate client implementations while preserving platform-specific field constraints and custom field handling
vs alternatives: Handles both Jira Cloud and Server/Data Center with a single codebase through runtime format adaptation, whereas most Jira integrations require separate clients or manual field mapping per platform
Provides search operations that execute Jira Query Language (JQL) queries through the Jira Search API, returning paginated issue results with support for field projection, sorting, and result aggregation. Implements server-side filtering and result ordering to reduce payload size and network overhead, with built-in pagination handling for large result sets (>50 issues) that abstracts the complexity of offset/limit management from the caller.
Unique: Abstracts JQL pagination complexity through server-side result ordering and automatic offset management, allowing callers to request 'next page' without tracking state, while preserving full JQL expressiveness for complex multi-field filtering
vs alternatives: Provides JQL-native search with automatic pagination handling, whereas REST API clients require manual JQL construction and offset tracking; more powerful than simple issue key lookup but less opinionated than pre-built dashboard filters
Provides tools for creating, updating, and querying comments on Jira issues and Confluence pages with support for user mentions (@username) and automatic notification triggering. Uses the Jira/Confluence REST APIs to handle comment creation with mention parsing, automatic @-notification of mentioned users, and comment visibility settings (private, team, public). Comment queries return full comment history with author metadata, timestamps, and edit history, enabling AI agents to participate in issue discussions and track conversation context.
Unique: Implements automatic mention parsing and notification triggering with per-comment visibility settings, enabling AI agents to participate in discussions while respecting privacy constraints and automatically notifying relevant users
vs alternatives: Provides automatic mention parsing and notification handling, whereas raw Jira/Confluence APIs require manual mention formatting; supports both Jira and Confluence comments from a unified interface
Provides tools for uploading files to Jira issues and Confluence pages, with automatic content type detection and file size validation. Supports both binary files (images, PDFs, archives) and text files, with automatic MIME type detection from file extension or content inspection. Attachment retrieval returns download URLs and metadata (filename, size, upload date, uploader), enabling AI agents to attach generated artifacts (reports, images, documents) to issues without manual file handling.
Unique: Implements automatic content type detection and file size validation with support for both binary and text files, enabling AI agents to attach generated artifacts without manual MIME type specification or size checking
vs alternatives: Provides automatic content type detection and validation, whereas raw Jira/Confluence APIs require manual MIME type specification; supports both Jira and Confluence attachments from a unified interface
Exposes tools for querying user information, managing user assignments to issues, and checking permissions for specific operations. Implements role-based access control (RBAC) queries that determine if a user has permission to perform an action (edit issue, create page, etc.) without attempting the operation. User queries return user metadata (name, email, avatar, active status) and can filter by project or issue context, enabling AI agents to assign issues to appropriate team members and validate permissions before attempting operations.
Unique: Implements role-based permission checking without attempting operations, enabling AI agents to validate access before taking action and provide better error messages, combined with context-specific user queries for issue assignment
vs alternatives: Provides permission validation without side effects, whereas raw Jira API requires attempting operations to discover permission errors; supports context-specific user queries (by project or issue) compared to global user lists
Provides tools for querying Confluence spaces and Jira projects, including space/project metadata (name, key, description, avatar), configuration (permissions, issue types, custom fields), and member lists. Implements hierarchical space navigation (space → pages → children) and project-specific field discovery (custom fields, issue types, workflows), enabling AI agents to understand the structure of Confluence/Jira instances and adapt operations based on project-specific constraints.
Unique: Implements hierarchical space/project navigation with automatic custom field and issue type discovery, enabling AI agents to understand instance structure and adapt operations based on project-specific constraints without manual configuration
vs alternatives: Provides unified space/project metadata queries with custom field discovery, whereas raw Jira/Confluence APIs require separate calls for each metadata type; supports both Jira and Confluence from a unified interface
Implements a dependency injection (DI) system using Python context managers and async context managers to provide JiraClient and ConfluenceClient instances to tool handlers, with per-request context isolation for multi-tenant deployments. Uses MainAppContext to store shared configuration (base URLs, authentication method) and per-request context to store user-specific credentials (from HTTP headers), enabling multiple users to authenticate with different credentials through the same server instance without credential leakage or cross-contamination.
Unique: Implements per-request context isolation using Python async context managers combined with dependency injection, enabling multi-tenant deployments where each request uses different credentials without manual credential passing or context management in tool handlers
vs alternatives: Provides automatic per-request context isolation with dependency injection, whereas most MCP servers require manual credential passing or global state management; async context manager approach is more robust than thread-local storage for concurrent requests
Exposes 27+ Confluence tools for creating, reading, updating, and deleting pages within hierarchical space structures, with support for parent-child page relationships and content versioning. Uses the Confluence REST API v2 (Cloud) or v1 (Server/DC) with automatic content format adaptation between storage format (XHTML-like) and view format (rendered HTML), enabling AI agents to work with human-readable content while preserving Jira markup and embedded resources.
Unique: Implements bidirectional content format adaptation (storage ↔ view) with automatic parent-child hierarchy resolution, allowing AI agents to work with human-readable content while preserving Confluence markup and embedded resource references without manual format conversion
vs alternatives: Handles content format translation transparently and supports hierarchical page organization, whereas raw Confluence API clients require manual format conversion and parent ID tracking; more flexible than static documentation templates but less opinionated than wiki-specific frameworks
+7 more capabilities
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 mcp-atlassian at 33/100.
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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