| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements the Model Context Protocol (MCP) server specification to expose HTTP testing and request/response inspection capabilities through a standardized interface. Uses MCP's resource and tool abstractions to allow Claude and other MCP-compatible clients to invoke HTTP operations, manage test sessions, and retrieve results through a bidirectional message protocol rather than direct API calls.
Unique: Bridges HTTP testing (typically a developer CLI tool) into the MCP ecosystem, allowing AI assistants to perform HTTP inspection and testing through standardized protocol bindings rather than requiring separate tool invocations or API wrappers
vs alternatives: Provides MCP-native HTTP testing integration that works with any MCP-compatible client, whereas direct httpbin usage requires manual HTTP calls or custom client code
Executes arbitrary HTTP requests (GET, POST, PUT, DELETE, PATCH, HEAD, OPTIONS) with full support for custom headers, request bodies, and URL parameters. Routes requests through the MCP tool interface, allowing clients to specify HTTP semantics declaratively and receive parsed response metadata including status codes, response headers, and body content.
Unique: Exposes HTTP request execution as an MCP tool, allowing AI models to construct and execute HTTP calls with full semantic control (method, headers, body) without requiring the client to implement HTTP logic, versus traditional REST APIs that require the client to handle HTTP mechanics
vs alternatives: More flexible than curl-based MCP tools because it supports structured header and body input through MCP's type system, and integrates response parsing directly into the protocol layer
Parses HTTP responses and evaluates assertions against response data (status codes, header presence/values, body content matching). Uses pattern matching or structured comparison to validate that responses meet expected criteria, returning boolean results and detailed mismatch information to the MCP client for further analysis or conditional logic.
Unique: Integrates assertion evaluation into the MCP protocol layer, allowing AI assistants to reason about test results and make decisions based on assertion outcomes without requiring the client to implement comparison logic
vs alternatives: Provides assertion-as-a-tool capability that works with any MCP client, whereas traditional test frameworks require language-specific assertion libraries and test runners
Maintains session state across multiple HTTP requests within a single MCP connection, allowing tests to reference prior request/response data, extract values from responses, and use those values in subsequent requests. Implements context variables or session storage that persists across tool invocations within the same MCP session, enabling multi-step test workflows.
Unique: Implements session context as a first-class MCP capability, allowing AI assistants to manage multi-step workflows without requiring explicit state passing between tool calls, versus stateless HTTP clients that require the caller to manage context
vs alternatives: Simpler than external state stores (Redis, databases) for test automation because state is implicit in the MCP session, reducing boilerplate for AI agents orchestrating test workflows
Exposes HTTP testing capabilities and test metadata as MCP resources (read-only or read-write), allowing clients to discover available test endpoints, view test history, and access documentation about supported HTTP methods and assertion types. Uses MCP's resource URI scheme to organize test-related information hierarchically and provide clients with introspectable metadata about the server's capabilities.
Unique: Uses MCP's resource abstraction to expose test metadata and documentation, allowing clients to discover and understand server capabilities through a standardized protocol rather than hardcoded documentation or separate API endpoints
vs alternatives: More discoverable than REST API documentation because resources are queryable through the same MCP connection, reducing the need for separate documentation systems or OpenAPI specs
Parses HTTP response bodies into structured formats (JSON objects, arrays, key-value pairs) and extracts specific fields or values using path expressions (JSONPath, dot notation). Implements format detection and parsing logic, allowing LLMs to work with response data as structured objects rather than raw text, enabling easier inspection and assertion of API responses.
Unique: Provides automatic JSON parsing and JSONPath extraction as MCP tools, allowing LLMs to work with structured response data without manual JSON parsing or string manipulation
vs alternatives: More convenient than raw string inspection because it parses JSON automatically and supports JSONPath extraction vs. requiring LLMs to manually parse and navigate response text
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 xbtest at 23/100. xbtest leads on ecosystem, while Supabase is stronger on quality.
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