| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes LinkedIn account control through the Model Context Protocol (MCP), enabling AI assistants to execute authenticated actions on LinkedIn accounts by translating natural language intents into Linked API calls. The MCP server acts as a bridge between Claude/other LLM clients and the Linked API backend, handling OAuth token management, request serialization, and response parsing to maintain a stateless interface for AI agents.
Unique: Implements MCP server pattern specifically for LinkedIn, providing a standardized protocol interface that allows any MCP-compatible LLM client (Claude, Cline, etc.) to control LinkedIn accounts without custom integration code. Uses Linked API as the underlying authentication and API layer, abstracting away LinkedIn's complex OAuth and rate-limiting requirements.
vs alternatives: Simpler than building custom LinkedIn API integrations because it leverages MCP's standardized tool-calling protocol and Linked API's managed authentication, enabling plug-and-play LinkedIn automation in Claude and other LLM applications without OAuth implementation overhead.
Fetches live LinkedIn data (profiles, posts, connections, engagement metrics) through Linked API and returns structured JSON responses that LLMs can parse and reason over. The MCP server translates data retrieval requests into Linked API queries, handles pagination for large result sets, and formats responses to match expected schema, enabling AI assistants to make decisions based on current LinkedIn state.
Unique: Integrates Linked API's managed LinkedIn data access layer with MCP's tool-calling interface, allowing LLMs to query LinkedIn data without implementing LinkedIn's complex scraping logic or OAuth. Handles schema normalization so responses match expected JSON structures for downstream LLM reasoning.
vs alternatives: More reliable than direct LinkedIn API scraping because it uses Linked API's maintained infrastructure and handles LinkedIn's frequent API changes, while being more flexible than pre-built LinkedIn analytics tools because it exposes raw data for custom LLM-driven analysis.
Dynamically generates MCP-compliant tool schemas that describe available LinkedIn actions (post creation, profile updates, connection requests, etc.) with proper input validation, parameter types, and descriptions. The server introspects Linked API's capabilities and exposes them as MCP tools, enabling LLM clients to understand available actions through schema inspection and perform type-safe function calling.
Unique: Implements MCP's tool schema protocol to expose Linked API's LinkedIn capabilities as discoverable, type-safe tools. Unlike generic API wrappers, it generates schemas that match MCP's strict format requirements, enabling LLM clients to understand parameter constraints and perform validation before execution.
vs alternatives: More discoverable than raw API documentation because schemas are machine-readable and integrated into the LLM's tool-calling interface, and more type-safe than prompt-based instruction because validation happens at the protocol level before requests reach LinkedIn.
Manages LinkedIn OAuth tokens (access and refresh tokens) on behalf of the MCP client, handling token refresh cycles, expiration detection, and re-authentication flows transparently. The server stores and rotates credentials securely, ensuring that LinkedIn API calls always use valid tokens without requiring the LLM client to manage authentication state directly.
Unique: Abstracts LinkedIn OAuth complexity into the MCP server layer, allowing LLM clients to make authenticated LinkedIn calls without implementing OAuth flows themselves. Linked API handles the underlying OAuth provider integration, while the MCP server manages token lifecycle for the LLM client.
vs alternatives: Simpler than implementing OAuth in the LLM application because token refresh happens transparently in the MCP server, and more secure than storing credentials in the LLM client because tokens are managed server-side with potential for encryption and rotation.
Catches LinkedIn API errors (rate limits, authentication failures, network timeouts) and translates them into meaningful error messages that LLM clients can understand and act upon. The server implements retry logic for transient failures, provides structured error responses with recovery suggestions, and prevents cascading failures when LinkedIn is temporarily unavailable.
Unique: Implements MCP-aware error handling that translates LinkedIn and Linked API errors into tool-call failures that LLM clients can reason about and respond to. Includes automatic retry logic for transient failures, reducing the need for LLM clients to implement their own retry strategies.
vs alternatives: More robust than naive API wrapping because it handles transient failures automatically and provides structured error information for LLM reasoning, while being simpler than building a full circuit breaker pattern because retry logic is encapsulated in the MCP server.
Supports managing multiple LinkedIn accounts through a single MCP server instance by maintaining separate OAuth token stores and request contexts for each account. The server routes actions to the correct LinkedIn account based on account identifiers passed in tool calls, ensuring credential isolation and preventing cross-account data leaks.
Unique: Implements account-level credential isolation within a single MCP server, allowing multiple LinkedIn accounts to be managed through a unified interface without credential leakage. Routes requests to correct account context based on tool call parameters.
vs alternatives: More efficient than running separate MCP server instances per account because it consolidates token management and reduces infrastructure overhead, while maintaining credential isolation through request-level context switching.
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 Linked API at 24/100.
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