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| Pricing | Paid | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Evaluates AI agents' ability to solve real-world software engineering tasks by executing them against a curated benchmark of GitHub issues and pull requests. The system runs agent-generated solutions in isolated environments, validates outputs against ground-truth implementations, and measures success rates across multiple dimensions (task completion, code quality, test passage). Uses a standardized evaluation framework that normalizes metrics across different model architectures and agent implementations.
Unique: SWE-Bench uses real, unmodified GitHub issues and pull requests as evaluation tasks rather than synthetic coding problems, ensuring agents are tested against authentic software engineering challenges with genuine complexity, ambiguity, and multi-file dependencies that reflect production scenarios
vs alternatives: More representative of real-world coding tasks than HumanEval or MBPP because it evaluates full repository-level problem-solving with actual test suites and version control workflows, not isolated function implementations
Provides standardized evaluation infrastructure that allows direct performance comparison of different LLM models (GPT-4, Claude, Llama, etc.) and agent architectures (ReAct, Chain-of-Thought, tool-use patterns) on identical software engineering tasks. Normalizes evaluation across model-specific API differences, context window constraints, and function-calling conventions to produce comparable metrics. Tracks performance deltas as models are updated or new agents are introduced.
Unique: Provides unified evaluation harness that abstracts away model-specific API differences (function calling schemas, context window limits, token counting) allowing apples-to-apples comparison of fundamentally different model architectures without requiring separate integration work per model
vs alternatives: Unlike ad-hoc benchmarking scripts, SWE-Bench's standardized framework ensures consistent evaluation methodology across models, eliminating confounding variables from prompt engineering or agent implementation differences
Executes agent-generated code patches within the full context of the target repository, including all dependencies, test suites, and version control history. The system applies patches to a clean repository state, runs the full test suite to validate correctness, and captures execution logs and error traces. Uses sandboxed execution environments (containerized or VM-based) to safely run untrusted code without affecting the host system or benchmark infrastructure.
Unique: Executes patches in full repository context with all transitive dependencies and test suites intact, rather than testing code snippets in isolation, capturing real-world integration failures that unit-test-only approaches would miss
vs alternatives: More rigorous than static code analysis or AST-based validation because it actually runs the code and test suite, catching runtime errors, type mismatches, and logic bugs that static tools cannot detect
Segments benchmark results by software engineering task type (bug fixes, feature implementation, documentation, refactoring, etc.) and provides per-category success rates and performance analysis. Enables identification of which task categories agents excel at versus struggle with, revealing systematic weaknesses in agent reasoning or code generation capabilities. Uses task metadata and issue classification to automatically bucket results and generate category-specific reports.
Unique: Automatically segments results by software engineering task type (bug fix, feature, refactor, etc.) to reveal systematic capability gaps, rather than reporting only aggregate success rates that mask category-specific weaknesses
vs alternatives: Provides actionable insights about which real-world engineering tasks are safe to automate, whereas generic benchmarks only report overall performance without revealing which task categories drive failures
Captures detailed execution traces of agent decision-making, tool calls, and reasoning steps during task execution. Logs all intermediate states, API calls, code generation attempts, and error recovery actions in a structured format. Enables post-hoc analysis and replay of agent behavior to understand failure modes, debug agent logic, and identify where agents made suboptimal decisions. Supports both real-time streaming logs and batch analysis of completed runs.
Unique: Captures complete execution traces including all tool calls, reasoning steps, and error recovery attempts, enabling detailed post-hoc analysis of agent decision-making rather than just final pass/fail outcomes
vs alternatives: Provides visibility into agent reasoning process that simple success/failure metrics cannot reveal, enabling targeted improvements to agent prompts and architectures based on actual behavior patterns
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 varies at 16/100. Supabase also has a free tier, making it more accessible.
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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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