| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables LLM agents and tools to create, read, update, and delete Jira issues through the Model Context Protocol, translating MCP tool calls into Jira Cloud REST API requests with automatic authentication via OAuth2 or API tokens. Implements request/response marshaling to convert between MCP's JSON-RPC 2.0 format and Jira's REST endpoint schemas, handling field mapping and validation.
Unique: Implements MCP protocol binding specifically for Jira Cloud, allowing LLMs to treat Jira as a native tool without custom API wrapper code — uses MCP's resource and tool discovery to expose Jira's full issue schema dynamically based on instance configuration
vs alternatives: Simpler than building custom Jira API integrations because MCP handles authentication, serialization, and tool registration; more flexible than Jira's native automation rules because it enables multi-step LLM reasoning across issues
Exposes Jira projects, boards, and issue hierarchies as MCP resources that LLM agents can query and traverse, implementing a resource-based discovery pattern where each project/board is addressable via a URI and returns structured metadata including board type (Scrum/Kanban), issue types, and custom fields. Uses Jira's REST API to enumerate and cache project/board listings.
Unique: Uses MCP's resource protocol to model Jira's hierarchical structure (workspace → projects → boards → issues) as traversable resources, enabling agents to navigate and understand Jira topology without separate discovery API calls
vs alternatives: More discoverable than raw Jira API because MCP exposes available projects/boards as first-class resources; more efficient than agents querying all endpoints because resource URIs provide direct access to specific entities
Translates natural language or structured filter parameters into Jira Query Language (JQL) and executes searches against the Jira Cloud API, returning paginated issue lists with full metadata. Implements a query builder pattern that maps common filter dimensions (assignee, status, project, date range) to JQL syntax and handles special characters/escaping.
Unique: Provides MCP-native search interface that abstracts JQL complexity, allowing LLMs to express queries in natural language or structured parameters rather than requiring agents to learn JQL syntax
vs alternatives: More accessible than raw JQL because it translates natural language to JQL; more powerful than simple field filters because it supports complex boolean logic and date ranges
Enables agents to read issue comments, activity history, and changelog entries through MCP tool calls, returning chronologically ordered comment threads with author metadata, timestamps, and edit history. Implements pagination for issues with many comments and supports filtering by date range or author.
Unique: Exposes Jira's activity stream and comment history as queryable MCP resources, allowing agents to reconstruct issue context and decision rationale from the full comment thread rather than just current state
vs alternatives: More contextual than issue snapshots because it includes full comment history; more efficient than polling Jira UI because it uses the REST API with pagination support
Resolves user mentions, team names, and assignee references to Jira user objects with full metadata (email, avatar, timezone, account ID), enabling agents to map natural language names to valid Jira user IDs for assignment operations. Implements a search-based resolution pattern that queries Jira's user directory and caches results.
Unique: Provides MCP-native user resolution that abstracts Jira's account ID complexity, allowing agents to work with human-readable names and email addresses while mapping to internal Jira identifiers
vs alternatives: More usable than raw account IDs because it supports name-based lookup; more reliable than hardcoded mappings because it queries the live Jira directory
Validates and executes issue status transitions through Jira's workflow engine, checking allowed transitions for the current issue state and enforcing required field values before moving to the next state. Implements a state machine pattern that queries Jira's workflow metadata to determine valid next states and required fields.
Unique: Implements workflow-aware state transitions that validate against Jira's workflow engine before executing, preventing invalid state changes and enforcing required field constraints defined in the workflow
vs alternatives: More robust than direct status updates because it respects workflow rules; more intelligent than blind transitions because it validates required fields and available next states
Discovers, validates, and maps custom fields defined in a Jira instance, translating between field names and field IDs and enforcing field type constraints (select lists, date fields, number fields, etc.). Implements a schema registry pattern that caches field definitions and provides type-aware field validation.
Unique: Provides MCP-native custom field schema discovery and validation, allowing agents to work with field names while automatically mapping to field IDs and enforcing type constraints defined in the Jira instance
vs alternatives: More flexible than hardcoded field mappings because it discovers fields dynamically; more reliable than manual field ID lookup because it validates against the live schema
Creates, reads, and manages issue links (relates to, blocks, duplicates, etc.) through MCP tool calls, enabling agents to establish relationships between issues and traverse dependency graphs. Implements link creation with validation of link types and bidirectional link management.
Unique: Exposes Jira's issue linking as MCP tools with bidirectional link management, allowing agents to establish and traverse issue relationships without understanding Jira's internal link ID system
vs alternatives: More discoverable than raw link API because it exposes link types as first-class concepts; more useful than read-only links because it supports link creation and deletion
+1 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-cloud-mcp at 24/100.
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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