| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes the _cat/indices Elasticsearch API through MCP to list all available indices with their metadata (size, document count, health status). The server acts as a protocol bridge that translates MCP tool calls into native Elasticsearch REST API requests, handling authentication and transport protocol abstraction (stdio, HTTP, SSE) transparently. This enables LLM clients to discover and inspect the data landscape before executing queries.
Unique: Rust-based MCP server bridges Elasticsearch _cat/indices API directly into Claude Desktop and other MCP clients without requiring custom API wrappers, supporting multiple transport protocols (stdio, HTTP, SSE) from a single binary
vs alternatives: Simpler than building custom REST API wrappers because it uses standardized MCP protocol that Claude Desktop natively understands, eliminating the need for separate authentication and transport layer management
Retrieves Elasticsearch field mappings via the _mapping API, exposing the complete schema (field names, data types, analyzers, nested structures) for one or more indices. The server translates MCP tool parameters into Elasticsearch mapping requests and returns structured field metadata that LLMs can use to understand data structure before constructing queries. Supports inspection of nested fields, keyword vs text analysis, and custom analyzer configurations.
Unique: Exposes Elasticsearch _mapping API through MCP protocol, allowing Claude and other LLM clients to introspect field schemas directly without requiring separate schema documentation or custom API endpoints
vs alternatives: More accurate than relying on LLM training data about Elasticsearch because it queries live mappings from the actual cluster, ensuring schema-aware query generation matches the current index structure
The project uses Renovate for automated dependency management, scanning Cargo.toml for outdated dependencies and submitting pull requests weekly. This ensures the Rust codebase stays current with security patches and bug fixes in upstream libraries (Elasticsearch client, MCP protocol, async runtime). The automation reduces manual maintenance burden and improves security posture by catching vulnerable dependencies automatically.
Unique: Renovate automation scans Cargo.toml weekly and submits pull requests for outdated dependencies, ensuring Elasticsearch MCP stays current with security patches without manual intervention
vs alternatives: More proactive than manual dependency updates because it automatically detects outdated packages; more reliable than ignoring updates because it catches security vulnerabilities before they become critical
Executes arbitrary Elasticsearch Query DSL queries via the _search API, supporting full-text search, filtering, aggregations, and complex boolean logic. The MCP server accepts Query DSL JSON payloads, translates them into Elasticsearch requests with proper authentication, and returns paginated results with hit counts and relevance scores. Supports all Elasticsearch query types (match, term, range, bool, aggregations) and handles response pagination through size/from parameters.
Unique: Rust MCP server directly proxies Elasticsearch Query DSL without query transformation or validation, allowing LLMs to construct and execute complex queries while maintaining full Elasticsearch semantics and performance characteristics
vs alternatives: More flexible than pre-built search templates because it accepts arbitrary Query DSL, enabling LLMs to generate context-specific queries; faster than REST API wrappers because it uses native Elasticsearch client libraries in Rust
Executes ES|QL (Elasticsearch SQL-like query language) queries via the _query API with ES|QL syntax support. The server translates ES|QL statements into Elasticsearch requests and returns tabular results. This capability bridges SQL-familiar users and LLMs to Elasticsearch by providing a SQL-like interface while leveraging Elasticsearch's distributed query engine. Supports ES|QL syntax including FROM, WHERE, GROUP BY, STATS, and other clauses.
Unique: Exposes Elasticsearch ES|QL API through MCP, enabling LLMs to generate SQL-like queries that execute against Elasticsearch clusters without requiring Query DSL knowledge or custom SQL-to-DSL translation layers
vs alternatives: More intuitive for SQL-familiar users and LLMs than Query DSL because ES|QL uses familiar SQL syntax; enables faster query generation because LLMs have stronger training data for SQL than for Elasticsearch-specific DSL
Retrieves shard allocation information via the _cat/shards API, exposing how data is distributed across cluster nodes. The server returns shard IDs, node assignments, shard state (STARTED, RELOCATING, etc.), and storage sizes. This capability enables visibility into cluster health, data distribution, and potential bottlenecks. Useful for understanding cluster topology before executing large queries or diagnosing performance issues.
Unique: Rust MCP server exposes _cat/shards API through standardized MCP protocol, allowing LLM clients and monitoring tools to inspect cluster topology without requiring custom Elasticsearch client libraries or REST API wrappers
vs alternatives: Simpler than building custom monitoring dashboards because it exposes raw shard data through MCP that any client can consume; more accessible than Elasticsearch Kibana because it works with any MCP-compatible client including Claude Desktop
The MCP server implements three transport protocols (stdio for desktop integration, HTTP for web services, SSE for real-time streaming) through a unified Rust architecture. The core MCP tool implementations are protocol-agnostic; transport is handled by a pluggable layer that translates between protocol-specific message formats and internal MCP structures. This allows the same server binary to be deployed in different environments (Claude Desktop, web services, containerized systems) without code changes.
Unique: Rust-based MCP server implements protocol abstraction layer that decouples tool implementations from transport, enabling single binary to support stdio (Claude Desktop), HTTP (web services), and SSE (streaming) without duplicating business logic
vs alternatives: More flexible than single-protocol servers because it supports multiple deployment patterns from one codebase; more maintainable than separate servers for each protocol because transport logic is centralized and tested once
The server supports three Elasticsearch authentication methods (API key via ES_API_KEY, basic auth via ES_USERNAME/ES_PASSWORD, and mTLS certificates) through environment variable configuration. Authentication is handled at the connection layer, transparently applied to all Elasticsearch API calls. The server also supports SSL/TLS configuration with optional certificate verification bypass via ES_SSL_SKIP_VERIFY for development environments. This abstraction allows deployment in different security contexts without code changes.
Unique: Rust MCP server abstracts Elasticsearch authentication at connection layer, supporting API keys, basic auth, and mTLS through environment variables without exposing credentials to MCP clients or requiring per-request authentication
vs alternatives: More secure than passing credentials through MCP messages because authentication is handled server-side; more flexible than hardcoded credentials because it supports multiple authentication methods through environment configuration
+3 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.
Elasticsearch MCP Server scores higher at 62/100 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