| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes Jira Cloud board data (sprints, columns, swimlanes, board settings) through MCP tool endpoints that serialize board state into structured JSON. Uses Jira REST API v3 under the hood with MCP's request-response pattern to translate tool calls into authenticated API requests, returning board metadata and configuration without polling overhead.
Unique: Implements MCP tool protocol as a native binding layer to Jira REST API v3, allowing LLM agents to query board state without custom HTTP client code; modular design separates board queries from issue/user operations for independent scaling
vs alternatives: Lighter than full Jira SDK integrations (no heavy client library) and more structured than raw REST API calls, with MCP protocol ensuring compatibility with any MCP-aware LLM host (Claude, custom agents)
Translates natural language or structured filter parameters into Jira Query Language (JQL) and executes searches against Jira Cloud, returning paginated issue results with full issue details (fields, history, transitions). Uses MCP tool endpoints to expose search as a callable function, handling JQL syntax generation and result pagination internally.
Unique: Exposes Jira's native JQL search engine as an MCP tool, allowing agents to leverage Jira's full query syntax without reimplementing search logic; handles pagination and result marshaling transparently
vs alternatives: More powerful than simple field-based filters because it uses JQL (Jira's native query language), enabling complex boolean logic and custom field searches; simpler than building a custom search DSL
unknown — insufficient data. The repository description does not specify whether the MCP server supports webhook subscriptions or event streaming. If implemented, this would likely use Jira Cloud webhooks to stream issue events (created, updated, transitioned) to MCP clients via server-sent events or similar streaming mechanism.
Creates new Jira issues via MCP tool endpoint, accepting issue type, summary, description, and optional custom fields. Validates required fields against Jira project schema before submission, maps caller-provided field names to Jira internal field IDs, and returns the created issue key and URL. Uses Jira REST API v3 issue creation endpoint with pre-flight schema validation.
Unique: Implements pre-flight schema validation and custom field ID mapping as part of the MCP tool, reducing caller burden of field ID lookup and validation; modular design allows custom field mappings to be configured per project
vs alternatives: Safer than raw REST API calls because it validates fields before submission; more flexible than simple issue templates because it supports custom field mapping and dynamic field population
Updates existing Jira issues via MCP tool endpoint, supporting field updates (summary, description, assignee, priority, custom fields) and workflow transitions (status changes). Queries available transitions for the current issue state, validates the requested transition is legal, and applies the transition with optional comment. Uses Jira REST API v3 issue update and transition endpoints.
Unique: Validates workflow transitions before applying them by querying available transitions from Jira, preventing illegal state changes and providing agents with visibility into valid next states; separates field updates from transitions for independent control
vs alternatives: More robust than direct REST API calls because it validates transitions; more flexible than simple status-change tools because it supports arbitrary field updates and optional comments
Queries Jira Cloud user directory via MCP tool endpoint, returning user details (name, email, avatar, active status) and optionally filtering by project or board membership. Uses Jira REST API v3 user search endpoint with optional permission filters to identify assignable users for a given project. Caches user list in memory to reduce API calls for repeated queries.
Unique: Implements in-memory user caching to reduce API calls for repeated user queries, with optional permission filtering to return only assignable users for a project; modular design allows cache invalidation strategies to be plugged in
vs alternatives: Faster than raw user search API calls due to caching; more contextual than generic user directories because it filters by project permissions
Creates comments on Jira issues and retrieves existing comments via MCP tool endpoints. Comment creation supports plain text and optional Jira markup formatting. Comment retrieval returns paginated comment history with author, timestamp, and edit history. Uses Jira REST API v3 comment endpoints with optional visibility restrictions (public vs. restricted to specific groups).
Unique: Supports both comment creation and retrieval through unified MCP tool endpoints, with optional visibility restrictions for sensitive comments; integrates comment history into agent context for decision-making
vs alternatives: More integrated than raw API calls because it handles Jira markup formatting; more flexible than simple logging because it supports visibility restrictions and comment history retrieval
Queries active and future sprints for a board, and assigns or moves issues between sprints via MCP tool endpoints. Sprint queries return sprint metadata (name, state, start/end dates, goal). Issue assignment uses Jira REST API v3 sprint endpoints to move issues atomically. Supports bulk sprint operations (move multiple issues to sprint in single call if Jira API supports it).
Unique: Provides unified MCP tool endpoints for both sprint querying and issue assignment, allowing agents to understand sprint state and make assignment decisions in a single interaction; modular design separates sprint metadata from issue operations
vs alternatives: More integrated than raw API calls because it abstracts sprint state queries and issue assignment; more flexible than simple sprint assignment because it provides sprint metadata for decision-making
+3 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 Jira MCP Server at 26/100.
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