| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Creates new Linear issues through MCP tool invocation by translating LLM natural language requests into Linear API mutations. The server validates required parameters (title, teamId) and optional fields (description, priority, status), then queues the request through a rate-limited client that enforces Linear's 1400 requests/hour limit. Returns structured issue metadata including ID, URL, and status for LLM context.
Unique: Implements MCP tool schema with Linear-specific parameter validation and rate-limit-aware queueing, ensuring LLM requests respect API quotas without blocking the client. Uses LinearMCPClient abstraction to decouple protocol handling from API integration.
vs alternatives: Simpler than building custom Linear integrations because it handles MCP protocol translation and rate limiting automatically, while remaining more flexible than Linear's native Slack/GitHub integrations by supporting any MCP-compatible LLM client.
Searches Linear issues using a query string combined with optional filters (teamId, status, assigneeId, labels, priority) by translating them into Linear GraphQL queries. The server constructs parameterized queries that filter across multiple dimensions simultaneously, returning paginated results with issue metadata. Supports both full-text search on title/description and structured filtering on issue properties.
Unique: Combines full-text search with structured filtering through a single MCP tool, allowing LLMs to express complex queries naturally ('find open bugs assigned to me') without requiring users to learn Linear's filter syntax. Rate limiter ensures search requests don't exhaust API quota.
vs alternatives: More flexible than Linear's built-in saved views because it accepts dynamic filter parameters from LLM context, and simpler than building custom GraphQL clients because the MCP server handles query construction and pagination.
Implements the Model Context Protocol (MCP) server specification by handling MCP requests (list resources, read resource, list tools, call tool) from LLM clients via stdio transport. The server translates MCP tool invocations into LinearMCPClient method calls and formats responses back to the protocol format. Exposes tool schemas that describe available operations and their parameters to the LLM client.
Unique: Implements full MCP server specification with stdio transport, enabling seamless integration with Claude Desktop and other MCP-compatible clients. Tool schemas are statically defined but cover all major Linear operations.
vs alternatives: Simpler than building custom REST APIs because MCP handles protocol translation automatically, and more flexible than Linear's native integrations because it works with any MCP-compatible LLM client.
Handles errors from Linear API calls and formats them as MCP-compliant error responses that LLMs can interpret. The server catches API errors (authentication failures, invalid parameters, rate limit errors) and serializes them with descriptive messages and error codes. Ensures that LLM clients receive actionable error information rather than raw API responses.
Unique: Translates Linear API errors into MCP-compliant error responses with descriptive messages, enabling LLM clients to understand failures without exposing raw API details. Error handling is transparent to MCP tools.
vs alternatives: More user-friendly than raw API errors because it provides MCP-formatted messages, and simpler than building custom error recovery because it delegates retry logic to the LLM client.
Defines MCP resource templates that allow clients to request issue data using URI patterns (e.g., 'linear://issue/{issueId}'), enabling LLMs to reference issues as persistent resources rather than one-off API calls. The server implements resource reading that fetches issue details when a client requests a resource URI, integrating issue context into the LLM's knowledge base.
Unique: Implements MCP resource templates for issues, allowing LLMs to treat Linear issues as first-class resources in the conversation context rather than requiring explicit tool calls
vs alternatives: More seamless than tool-based issue fetching because users can paste issue URIs directly; simpler than building a separate context manager because it leverages MCP's native resource protocol
Updates existing Linear issues by accepting an issue ID and a set of fields to modify (title, description, priority, status, assignee). The server constructs targeted GraphQL mutations that update only specified fields, avoiding unnecessary API calls or conflicts from partial updates. Returns the updated issue state to confirm changes to the LLM client.
Unique: Implements selective field updates through GraphQL mutations rather than full-object replacement, reducing API payload size and avoiding unnecessary field overwrites. Rate limiter queues mutations to respect Linear's request limits.
vs alternatives: More granular than Linear's REST API because it updates only specified fields, and safer than direct GraphQL access because the MCP server validates field names and types before submission.
Retrieves all issues assigned to a specific user by querying the Linear API with userId and optional filters (includeArchived, limit). The server constructs a GraphQL query that fetches the user's issue list with metadata, supporting pagination through limit parameters. Returns issues in a format suitable for LLM processing (title, status, priority, team, URL).
Unique: Provides a dedicated user-scoped query path that's more efficient than generic search for the common case of 'show me my issues', with built-in archive filtering to distinguish active from historical work. Integrates with rate limiter to queue requests.
vs alternatives: Simpler than building custom GraphQL queries because it abstracts away Linear's schema, and more efficient than searching by assigneeId because it's optimized for the single-user case.
Adds comments to Linear issues by accepting an issueId, comment body, and optional parameters for user attribution (createAsUser) and display customization (displayIconUrl). The server constructs a GraphQL mutation that appends the comment to the issue's activity stream. Supports both direct comments and comments attributed to specific users or bots with custom icons.
Unique: Supports optional user attribution and custom icon URLs, enabling LLM agents to post comments that appear to come from specific users or branded bots. Rate limiter queues comment mutations to avoid API quota exhaustion.
vs alternatives: More flexible than Linear's native integrations because it allows custom user attribution and icon customization, and simpler than building custom GraphQL clients because the MCP server handles mutation construction.
+5 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.
Linear MCP Server scores higher at 60/100 vs Supabase at 42/100.
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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