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| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Routes users to the automatically-generated OpenAPI specification hosted on Stainless Platform (app.stainless.com/api/spec/documented/openai/openapi.documented.yml), which reflects real-time API state through automated synchronization. The repository acts as a hub-and-spoke navigation layer that maintains a single source of truth pointer rather than storing specification copies, ensuring users always access the most current API contract without staleness risk.
Unique: Implements a hub-and-spoke navigation architecture where the repository itself contains zero specification copies, instead routing to Stainless Platform's automated spec generation pipeline. This ensures zero-latency propagation of API changes without manual repository updates or version drift.
vs alternatives: Eliminates specification staleness compared to alternatives that store OpenAPI files in Git, since changes propagate automatically through Stainless' synchronization rather than requiring manual commits.
Provides access to a human-reviewed, manually-curated OpenAPI specification stored in the manual_spec Git branch, enabling stable, validated API contracts for critical integrations. This specification undergoes explicit curation and review before publication, trading update frequency for reliability and documentation quality.
Unique: Separates specification concerns into two tracks: automated (live) and curated (manual). The manual_spec branch implements a human-review gate before specification publication, enabling explicit versioning and audit trails absent from auto-generated specs.
vs alternatives: Provides specification stability and human validation that live auto-generated specs cannot offer, making it suitable for regulated environments where API contract changes require explicit approval before tooling updates.
Implements a hub-and-spoke navigation model in README.md that routes users to either live or manual specifications based on their use case, with explicit decision criteria (SDK generation vs. documentation, real-time vs. stable). The repository acts as a decision router that surfaces the tradeoff between currency and stability, helping users select the appropriate specification source.
Unique: Implements explicit decision routing in documentation that surfaces the currency-vs-stability tradeoff, rather than hiding it. The hub-and-spoke architecture makes the specification sourcing strategy transparent and allows users to make informed choices based on their integration requirements.
vs alternatives: More transparent than alternatives that provide a single specification source, since it explicitly documents the tradeoffs and helps users avoid mismatches between their needs (e.g., production stability) and specification characteristics (e.g., experimental features).
Provides a GitHub Issues-based mechanism for reporting specification problems, inaccuracies, or discrepancies between the OpenAPI spec and actual API behavior. Issues are tracked in the repository's issue tracker, enabling community-driven specification validation and creating an audit trail of known specification gaps.
Unique: Separates specification issue reporting from general OpenAI support, creating a dedicated feedback loop for specification accuracy. This enables community-driven specification validation and creates an explicit audit trail of known gaps between specification and implementation.
vs alternatives: More transparent than closed-loop specification maintenance, since issues are publicly visible and tracked, allowing other users to discover known problems and reducing duplicate reporting.
Routes users to the OpenAI support portal (help.openai.com) for general API support, account issues, and questions outside the scope of specification accuracy. This separation of concerns directs specification-specific issues to the repository while routing other support needs to the official support channel.
Unique: Implements explicit separation of concerns by routing specification issues to GitHub Issues and general support to help.openai.com, preventing specification feedback from being lost in general support channels.
vs alternatives: Clearer than alternatives that route all issues to a single support channel, since it ensures specification feedback reaches the appropriate team and doesn't get diluted in general support queues.
Maintains OpenAPI 3.x format compliance for both live and manual specifications, ensuring compatibility with standard OpenAPI tooling ecosystems (code generators, validators, documentation renderers). The specification adheres to OpenAPI 3.x schema standards, enabling interoperability with any OpenAPI-compatible tool without custom parsing.
Unique: Commits to OpenAPI 3.x format standardization across both live and manual specifications, ensuring zero friction with the OpenAPI ecosystem. This eliminates custom specification parsing and enables drop-in compatibility with any OpenAPI-aware tool.
vs alternatives: More interoperable than proprietary specification formats, since OpenAPI 3.x is a widely-adopted standard with mature tooling, reducing integration friction compared to custom API description languages.
Leverages Stainless Platform's automated synchronization pipeline to keep the live specification synchronized with OpenAI API changes in near-real-time. The live specification is generated automatically from OpenAI's API implementation, eliminating manual specification maintenance and ensuring the specification reflects current API state without human intervention.
Unique: Delegates specification maintenance to Stainless Platform's automated synchronization pipeline, eliminating the need for manual specification updates in the repository. This architecture ensures zero-latency propagation of API changes without repository commits or version management overhead.
vs alternatives: More agile than Git-based specification management, since changes propagate automatically without requiring manual commits, enabling real-time API contract awareness for downstream tooling.
Enables explicit version pinning of the OpenAPI specification by referencing the manual_spec Git branch, allowing users to lock their tooling to a specific, known-good specification version. Git's version control semantics provide commit-level granularity for specification versioning, enabling reproducible builds and explicit change tracking.
Unique: Leverages Git's native version control semantics to provide specification versioning with commit-level granularity and full change history. This enables explicit version pinning without requiring a separate versioning system.
vs alternatives: More transparent than alternatives that version specifications outside Git, since Git provides native diff, blame, and history capabilities that make specification changes auditable and reviewable.
+1 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 OpenAI specification at 24/100.
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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