| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements an MCP (Model Context Protocol) server that exposes Jira ticket data as tools callable by AI coding agents like Cursor. The server acts as a bridge between Jira's REST API and MCP-compatible clients, translating ticket metadata (issue keys, summaries, descriptions, status, assignees) into structured tool schemas that agents can invoke during code generation workflows. This enables agents to fetch real-time ticket context without requiring direct API credentials or manual context copying.
Unique: Bridges Jira and MCP protocol by implementing a lightweight MCP server that translates Jira REST API responses into MCP-compliant tool schemas, allowing AI agents to treat Jira tickets as first-class callable tools rather than requiring manual context management or custom integrations
vs alternatives: Simpler than building custom Jira integrations for each AI agent because it uses the standardized MCP protocol, enabling any MCP-compatible tool to access Jira without agent-specific code
Exposes Jira ticket data through MCP tool definitions that agents can call with ticket identifiers. The server queries Jira's REST API endpoints (typically /rest/api/3/issue/{key}) and returns structured metadata including issue key, summary, description, current status, assignee, priority, labels, and custom fields. The MCP protocol wraps these calls in a standardized tool schema, allowing agents to discover and invoke ticket lookups as part of their reasoning chain.
Unique: Implements lazy-loaded ticket metadata retrieval through MCP tools, allowing agents to fetch only the tickets they reference during reasoning rather than pre-loading entire backlogs, reducing context bloat and API overhead
vs alternatives: More efficient than embedding entire Jira backlogs in agent context because it fetches tickets on-demand through tool calls, keeping context window usage minimal while maintaining real-time accuracy
Implements a full MCP (Model Context Protocol) server that handles MCP client connections, tool schema registration, and request/response marshaling. The server exposes Jira operations as MCP tools with defined input schemas and output formats, handles authentication between the MCP client and Jira backend, and manages the lifecycle of connections from MCP-compatible clients like Cursor. This enables any MCP-aware application to treat Jira as a callable service without implementing Jira-specific logic.
Unique: Implements a lightweight MCP server that translates between MCP's JSON-RPC 2.0 protocol and Jira's REST API, abstracting protocol differences and allowing any MCP client to interact with Jira through a standardized interface without knowledge of Jira's specific API structure
vs alternatives: More flexible than direct Jira API integration because MCP decouples the client from the backend, allowing multiple AI tools to share a single Jira integration point and enabling future backend swaps without client changes
Manages Jira API authentication credentials (API tokens, username/password, or OAuth) and applies them to all outbound Jira REST API requests. The server stores credentials securely (typically via environment variables or configuration files) and injects them into HTTP headers (Authorization: Basic or Bearer tokens) for each API call. This decouples credential management from MCP clients, preventing credential exposure and centralizing authentication logic.
Unique: Centralizes Jira credential management at the MCP server level, preventing credentials from being exposed to AI agents or stored in agent context, and enabling credential rotation without updating client configurations
vs alternatives: More secure than embedding Jira credentials in agent prompts or context because credentials are managed server-side and never transmitted to the AI model, reducing attack surface and enabling centralized audit trails
Exposes Jira Query Language (JQL) search capabilities through MCP tools, allowing agents to search for tickets matching specific criteria (assignee, status, priority, labels, custom fields). The server translates JQL queries into Jira REST API search endpoints (/rest/api/3/search) and returns paginated results with ticket metadata. This enables agents to discover relevant tickets without requiring explicit ticket keys, supporting dynamic ticket lookup based on context.
Unique: Enables agents to construct and execute JQL queries dynamically, allowing context-aware ticket discovery based on runtime conditions (current user, project, status) rather than static ticket references, supporting adaptive workflows
vs alternatives: More powerful than static ticket lists because agents can search dynamically based on context, discovering related work and filtering by criteria without requiring pre-configuration or manual ticket enumeration
Defines and exposes MCP tool schemas that describe available Jira operations (get ticket, search tickets, etc.) with input parameter definitions, output formats, and descriptions. MCP clients use these schemas to discover available tools, validate input parameters, and understand expected outputs. The server implements the MCP tools/list and tools/call endpoints to support tool discovery and invocation, enabling clients to dynamically discover Jira capabilities without hardcoding tool names or parameters.
Unique: Implements MCP tool schema definitions that enable clients to discover and validate Jira operations dynamically, supporting self-documenting APIs where tool availability and parameters are discoverable at runtime rather than hardcoded
vs alternatives: More maintainable than hardcoded tool lists because schema definitions are centralized and versioned, allowing clients to adapt to tool changes without code updates and enabling better error messages when parameters are invalid
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 Context MCP at 20/100.
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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
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