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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 18 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements the Model Context Protocol (MCP) server specification using the mark3labs/mcp-go framework, translating standardized MCP tool invocations into native Grafana REST API calls. The server exposes 20+ tool categories through a unified MCP interface, handling request/response marshaling, error translation, and protocol-level session management across stdio, SSE, and HTTP transports.
Unique: Built on mark3labs/mcp-go framework with multi-transport support (stdio, SSE, HTTP) and native session management, enabling both local development and cloud-scale deployments without code changes. Implements tool discovery via MCP's ListTools mechanism with dynamic schema generation from Grafana API introspection.
vs alternatives: Provides native MCP protocol support vs custom REST wrappers, enabling seamless integration with any MCP-compatible client and standardized tool composition patterns used across the AI assistant ecosystem.
Supports three distinct transport modes configured at startup: stdio for direct process integration with local clients, Server-Sent Events (SSE) for unidirectional streaming over HTTP, and streamable-HTTP for bidirectional communication. Each transport is implemented as a separate handler in cmd/mcp-grafana/main.go with transport-agnostic tool execution logic, enabling the same server binary to serve different deployment architectures without modification.
Unique: Single binary supports three transport modes with unified tool execution logic, implemented via transport-agnostic handler interfaces. Eliminates need for separate server implementations while maintaining protocol compliance for each transport variant.
vs alternatives: More flexible than single-transport MCP servers — supports local development (stdio), cloud deployment (HTTP), and streaming scenarios (SSE) from identical codebase, reducing operational complexity vs maintaining separate server variants.
Exposes Prometheus metrics from mcp-grafana itself, tracking tool invocation counts, execution latencies, error rates, and API call performance. Implements a /metrics endpoint (Prometheus format) that exports metrics like tool_invocations_total, tool_execution_duration_seconds, grafana_api_calls_total, and datasource_query_errors. Enables operators to monitor mcp-grafana's health and performance through Grafana dashboards, alerting on high error rates or slow tool execution.
Unique: Exports Prometheus metrics from mcp-grafana's tool execution path (cmd/mcp-grafana/main.go 21-23), tracking invocation counts, latencies, and errors. Provides /metrics endpoint in Prometheus text format, enabling integration with existing Prometheus monitoring infrastructure.
vs alternatives: Native Prometheus metrics vs custom logging — provides structured metrics with latency histograms and error counters, enables alerting on performance degradation, and integrates with existing Prometheus/Grafana monitoring without custom parsing.
Implements automatic tool discovery that generates MCP tool schemas dynamically based on Grafana's API capabilities and configured datasources. The tool management framework introspects Grafana's /api/datasources, /api/v1/rules, and other endpoints to determine available tools, then generates MCP-compliant tool schemas with typed parameters, descriptions, and validation rules. Clients discover available tools via MCP's ListTools mechanism, receiving only tools applicable to their session's Grafana instance and permissions.
Unique: Implements tool management framework that dynamically generates MCP tool schemas from Grafana API introspection, discovering available datasources and rules at runtime. Enables single mcp-grafana instance to expose different tools based on Grafana configuration and user permissions, without hardcoded tool definitions.
vs alternatives: Dynamic tool discovery vs static tool definitions — adapts to Grafana configuration changes without server restart, exposes only tools applicable to user's permissions, and enables multi-tenant deployments where different organizations have different available tools.
Manages Grafana authentication through API keys provided per session, enforcing role-based access control (RBAC) inherited from Grafana's permission model. Validates API keys against Grafana's /api/auth/identity endpoint, caches authentication state per session, and enforces Grafana's datasource and dashboard permissions on all tool invocations. Supports multiple authentication methods (API keys, OAuth tokens) and propagates Grafana's RBAC decisions to MCP tool execution, ensuring users can only query resources they have permission to access.
Unique: Validates API keys against Grafana's /api/auth/identity endpoint and enforces Grafana's RBAC on all tool invocations, inheriting datasource and dashboard permissions from Grafana's permission model. Enables multi-tenant deployments where different users access different resources based on Grafana's existing RBAC configuration.
vs alternatives: Grafana-native RBAC enforcement vs custom authorization — leverages existing Grafana permissions without duplication, prevents unauthorized data access through inherited RBAC, and simplifies permission management by using Grafana as the source of truth.
Supports TLS encryption for HTTP and SSE transports through configurable certificate and key files. Implements standard Go TLS server configuration with support for custom CA certificates, client certificate validation, and TLS version pinning. Enables secure communication between MCP clients and mcp-grafana server, protecting API keys and query results in transit. Configuration is provided via environment variables or command-line flags at server startup.
Unique: Implements standard Go TLS server configuration with support for custom certificates, client certificate validation, and TLS version pinning. Enables secure HTTP/SSE transports without custom TLS implementation, leveraging Go's standard library TLS support.
vs alternatives: Native TLS support vs plaintext HTTP — encrypts API keys and query results in transit, enables compliance with security requirements, and provides standard HTTPS security without custom implementation.
Implements context window awareness for LLM interactions by tracking token usage across tool invocations and providing token budgeting information to clients. Monitors query result sizes and estimates token consumption based on response content, enabling AI assistants to make informed decisions about query scope and result pagination. Provides token usage metrics through OpenTelemetry spans and Prometheus metrics, allowing operators to track and optimize token consumption.
Unique: Tracks token usage across tool invocations by measuring response sizes and estimating token consumption, providing token budgeting information to clients. Exposes token metrics through OpenTelemetry and Prometheus, enabling operators to optimize query scope and result pagination.
vs alternatives: Built-in token tracking vs manual estimation — provides visibility into token consumption per query, enables AI assistants to make informed decisions about query scope, and supports cost optimization for token-based billing models.
Supports read-only deployment mode that disables all write operations and restricts tool invocations to query-only capabilities. Implements permission checks that prevent dashboard modifications, alert rule changes, and incident updates, exposing only tools for querying dashboards, datasources, alerts, and logs. Configuration is enforced at the tool execution layer, ensuring read-only semantics are maintained across all transport modes and authentication contexts.
Unique: Implements read-only deployment mode that disables all write operations at the tool execution layer, enforced across all transport modes and authentication contexts. Enables restricted access deployments without requiring separate server instances or custom authorization logic.
vs alternatives: Server-level read-only enforcement vs relying on API key permissions — provides defense-in-depth by preventing write operations even if API key has write permissions, simplifies access control for restricted deployments, and enables safe sharing of mcp-grafana with external parties.
+10 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 Grafana at 23/100.
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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