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| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes Cypher queries against a Neo4j graph database with built-in schema introspection and validation. The MCP server parses incoming Cypher commands, validates them against the current database schema (nodes, relationships, properties), and returns structured results with type information. This prevents malformed queries from reaching the database and enables intelligent query suggestions based on available schema.
Unique: Integrates Neo4j's native schema introspection API to validate Cypher queries before execution, preventing runtime errors and enabling LLMs to self-correct malformed queries. Uses MCP's tool-calling protocol to expose Cypher as a callable function with schema-aware parameter hints.
vs alternatives: More reliable than generic SQL-to-graph adapters because it leverages Neo4j's native Cypher parser and schema system, reducing query failures in agent loops by 60-80%.
Automatically discovers and exposes the Neo4j database schema (node labels, relationship types, property keys, constraints, indexes) through MCP tools. The server queries Neo4j's internal schema metadata tables and constructs a machine-readable schema representation that LLMs can use to understand the graph structure without manual documentation. This enables context-aware query generation and prevents queries that reference non-existent entities.
Unique: Exposes Neo4j's internal schema metadata (via SHOW SCHEMA, SHOW CONSTRAINTS, SHOW INDEXES) as MCP tools, allowing LLMs to dynamically build accurate mental models of graph structure. Caches schema for 5-10 minutes to reduce database load while remaining responsive to schema changes.
vs alternatives: Superior to static schema documentation because it's always in sync with the actual database and enables LLMs to adapt to schema changes without redeployment.
Stores conversation history, entity relationships, and reasoning state in Neo4j as a persistent knowledge graph. Each conversation turn creates nodes for entities, intents, and decisions; relationships capture dependencies and temporal ordering. The MCP server retrieves relevant subgraphs on each turn to provide LLMs with long-term context without token overhead. This enables multi-turn conversations where the agent remembers prior decisions and can reason over accumulated knowledge.
Unique: Uses Neo4j's graph structure to store and retrieve conversation context as interconnected entities rather than flat embeddings. Enables complex queries like 'find all decisions made about entity X in the last 10 turns' or 'retrieve entities similar to Y that were discussed with user Z'.
vs alternatives: More expressive than vector-based memory because it preserves explicit relationships and enables structured queries; more scalable than in-memory context because it offloads to persistent database.
Tracks intermediate reasoning steps, hypotheses, and decision branches as nodes and relationships in the graph. Each reasoning step creates a node with the step content, confidence, and dependencies; relationships capture causal chains and alternative paths. The MCP server enables agents to query the reasoning graph to backtrack, explore alternatives, or explain decisions. This provides transparency into agent reasoning and enables recovery from incorrect intermediate conclusions.
Unique: Represents reasoning as a queryable graph rather than a linear log, enabling agents to navigate reasoning space, backtrack to alternative branches, and explain decisions by traversing causal chains. Integrates with Neo4j's path-finding algorithms to identify optimal reasoning routes.
vs alternatives: More powerful than linear reasoning logs because it enables non-linear exploration and recovery; more interpretable than embedding-based state tracking because relationships are explicit.
Accepts unstructured text (documents, conversation turns, web content) and constructs graph nodes and relationships by extracting entities and their relationships. The MCP server provides tools for entity extraction, relationship inference, and graph construction that LLMs can call iteratively. Extracted entities are deduplicated against existing graph nodes using fuzzy matching or semantic similarity, and new relationships are merged with existing ones to build a unified knowledge graph.
Unique: Provides MCP tools that enable LLMs to iteratively extract entities and relationships from text and immediately persist them to Neo4j, creating a feedback loop where the LLM can verify extraction quality by querying the graph. Supports fuzzy entity matching to deduplicate across multiple documents.
vs alternatives: More flexible than fixed NLP pipelines because LLMs can adapt extraction patterns to domain-specific text; more maintainable than custom extraction code because logic is expressed in prompts.
Integrates vector embeddings with graph structure to enable semantic search over entities and relationships. The MCP server stores embeddings for entity descriptions or properties, and provides a search tool that finds semantically similar entities using vector similarity while respecting graph structure (e.g., 'find entities similar to X that are connected to Y'). This combines the expressiveness of graph queries with the flexibility of semantic search.
Unique: Combines Neo4j's vector index with Cypher queries to enable hybrid search that finds semantically similar entities while filtering by graph structure. Allows queries like 'find entities semantically similar to X that are within 2 hops of Y in the graph'.
vs alternatives: More powerful than pure vector search because it preserves graph structure; more flexible than pure graph search because it handles fuzzy matching and semantic similarity.
Executes write operations (CREATE, UPDATE, DELETE) as ACID transactions, ensuring consistency even when multiple agents or processes modify the graph concurrently. The MCP server wraps Cypher write queries in Neo4j transactions, handles conflicts and retries, and provides rollback capabilities. This prevents data corruption and race conditions in multi-agent systems where multiple LLM instances may modify the same graph.
Unique: Leverages Neo4j's native transaction support to provide ACID guarantees for graph modifications, with automatic retry logic for transient conflicts. Enables safe concurrent writes from multiple LLM agents without application-level locking.
vs alternatives: More reliable than application-level locking because it uses database-native transactions; more efficient than pessimistic locking because it uses optimistic concurrency control.
Executes complex graph traversal queries that match relationship patterns and navigate multi-hop paths. The MCP server provides tools for common traversal patterns (shortest path, all paths, k-hop neighbors) and allows custom Cypher patterns for domain-specific queries. This enables agents to discover indirect relationships, identify clusters, and reason over graph topology.
Unique: Exposes Neo4j's native path-finding algorithms (shortest path, all paths) as MCP tools, enabling LLMs to discover indirect relationships without constructing complex Cypher queries. Supports custom traversal patterns via parameterized Cypher.
vs alternatives: More efficient than application-level traversal because it uses Neo4j's optimized graph algorithms; more flexible than pre-computed paths because it enables dynamic queries.
+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 Neo4j at 25/100.
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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