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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Retrieves error and performance issues from Sentry.io through the Model Context Protocol, implementing MCP's standardized tool-calling interface to expose Sentry's REST API as callable functions. The server translates MCP tool requests into authenticated Sentry API calls, handling pagination, filtering by project/organization, and returning structured issue data with stack traces, metadata, and resolution status. Uses MCP's resource-based architecture to expose Sentry organizations and projects as discoverable resources that LLMs can query.
Unique: Implements Sentry integration as an MCP server, exposing error monitoring as a first-class tool callable by LLMs through MCP's standardized protocol rather than requiring direct API integration. Follows MCP's resource discovery pattern to expose Sentry organizations and projects as queryable resources, enabling LLMs to dynamically discover available monitoring contexts.
vs alternatives: Provides LLM-native access to Sentry data through MCP's standardized interface, eliminating the need for custom API wrappers or prompt engineering to interact with error data, compared to passing raw Sentry API documentation to LLMs.
Implements the Model Context Protocol server specification, exposing Sentry capabilities as discoverable MCP tools with JSON Schema definitions. The server handles MCP's JSON-RPC 2.0 transport layer (stdio or HTTP), manages tool registration with input/output schemas, and routes incoming tool calls from MCP clients to appropriate Sentry API handlers. Implements MCP's resource and tool discovery mechanisms so clients can enumerate available operations before invoking them.
Unique: Implements full MCP server specification including resource discovery, tool schema registration, and JSON-RPC transport handling. Exposes Sentry as a composable tool within MCP's multi-tool ecosystem rather than a standalone API wrapper.
vs alternatives: Provides standardized MCP interface for Sentry integration, enabling seamless composition with other MCP servers (GitHub, Slack, databases) in unified agent workflows, versus custom API clients that require separate integration logic per service.
Manages Sentry API authentication by accepting and validating API tokens or DSN credentials, storing them securely for use in subsequent API requests. The server implements credential handling patterns that allow MCP clients to provide authentication once during initialization, then transparently includes credentials in all Sentry API calls without requiring the client to manage tokens. Supports both organization-level and project-level API tokens with appropriate scope validation.
Unique: Implements MCP-specific credential handling where tokens are provided once to the server during initialization, then transparently included in all downstream API calls, rather than requiring clients to manage and pass credentials with each tool invocation.
vs alternatives: Separates credential management from tool invocation logic, reducing security surface compared to passing API tokens as parameters in each LLM-generated tool call.
Transforms raw Sentry API responses into structured, LLM-friendly formats by mapping Sentry's native issue schema to simplified JSON objects with relevant fields (error message, stack trace, affected users, timestamps, resolution status). Implements field selection and flattening logic to reduce noise and focus on actionable debugging information. Handles nested Sentry data structures (events, tags, breadcrumbs) and presents them in a format optimized for LLM comprehension and reasoning.
Unique: Implements LLM-specific data transformation that prioritizes readability and reasoning capability over completeness, selecting and flattening Sentry's nested structures to match how LLMs best process error information.
vs alternatives: Provides pre-processed, LLM-optimized issue data compared to passing raw Sentry API responses, reducing the cognitive load on LLMs to parse complex nested structures and improving reasoning quality.
Exposes Sentry organizations and projects as discoverable MCP resources, allowing LLM clients to enumerate available monitoring contexts before querying issues. Implements MCP's resource listing pattern to return available projects with metadata (project slug, team, platform), enabling LLMs to dynamically discover which Sentry projects are accessible with the provided credentials. Supports filtering and pagination of resource lists for large Sentry instances.
Unique: Implements MCP's resource discovery pattern for Sentry, exposing projects as first-class discoverable resources rather than requiring clients to hardcode project identifiers or maintain separate project registries.
vs alternatives: Enables dynamic, context-aware project selection in LLM workflows compared to static project configuration, allowing agents to adapt to changing monitoring contexts.
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 Sentry at 20/100.
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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
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