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| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Translates between the Model Context Protocol (MCP) stdio-based communication and OpenAI SDK-compatible REST APIs through a unified adapter layer. The server uses the official MCP SDK for protocol handling and the OpenAI Node.js SDK for standardized API communication, enabling any OpenAI-format endpoint (Perplexity, Groq, xAI, etc.) to be exposed as an MCP tool without custom integration code.
Unique: Uses environment variable-based configuration (AI_CHAT_KEY, AI_CHAT_MODEL, AI_CHAT_BASE_URL) to dynamically instantiate OpenAI SDK clients without code changes, enabling zero-modification provider swapping. Implements MCP protocol handler via official MCP SDK for stdio communication, ensuring compatibility with any MCP client.
vs alternatives: Simpler than building provider-specific MCP servers because it leverages OpenAI SDK's built-in compatibility layer rather than implementing custom HTTP clients for each provider.
Enables running multiple MCP server instances simultaneously, each configured for a different AI provider through separate environment variable sets. Each instance exposes a uniquely-named tool (via AI_CHAT_NAME) to the MCP client, allowing Claude Desktop or LibreChat to access Perplexity, Groq, xAI, and other providers as distinct tools in a single session without provider conflicts.
Unique: Implements instance isolation through environment variable namespacing (AI_CHAT_* prefix) rather than config files, allowing each process to be independently deployed via npx, Docker, or Smithery without shared state. Tool naming is dynamically derived from AI_CHAT_NAME, enabling arbitrary provider combinations.
vs alternatives: More flexible than monolithic multi-provider servers because each instance can be independently versioned, restarted, or scaled without affecting others.
Implements the Model Context Protocol (MCP) server specification using the official MCP SDK, communicating with MCP clients (Claude Desktop, LibreChat) via stdin/stdout. The server registers a single 'chat' tool (or custom-named tool via AI_CHAT_NAME) that clients can invoke, with the MCP SDK handling protocol serialization, message routing, and error handling.
Unique: Uses the official MCP SDK for protocol implementation rather than custom JSON-RPC parsing, ensuring spec compliance and compatibility with all MCP clients. The SDK abstracts away protocol details, allowing the server to focus on provider integration.
vs alternatives: More reliable than custom MCP implementations because it leverages the official SDK's battle-tested protocol handling and error recovery logic.
Provides pre-configured integration patterns for both Claude Desktop (via claude_desktop_config.json) and LibreChat (via YAML configuration). The server exposes itself as an MCP tool through stdio communication, automatically registering with these clients when properly configured. Supports both local execution (node /path/to/build/index.js) and remote deployment (npx, Docker, Smithery).
Unique: Provides client-specific configuration templates (JSON for Claude Desktop, YAML for LibreChat) that abstract away MCP protocol details, allowing non-technical users to add providers through configuration alone. Supports three deployment methods (npx, local build, Smithery) with identical functionality.
vs alternatives: Simpler onboarding than generic MCP servers because it includes pre-written configuration examples for the two most popular MCP clients, reducing setup friction.
Exposes a single MCP tool with a dynamically-determined name derived from the AI_CHAT_NAME environment variable, enabling each provider instance to be identified distinctly in the MCP client UI. The tool name is set at server startup and remains constant for the lifetime of that instance, allowing multiple instances to coexist with different identities (e.g., 'groq-chat', 'perplexity-chat').
Unique: Tool name is derived from a single environment variable (AI_CHAT_NAME) rather than hardcoded or inferred from provider URL, enabling arbitrary naming without code changes. This design pattern allows the same server binary to be deployed multiple times with different identities.
vs alternatives: More flexible than servers with hardcoded tool names because it supports arbitrary naming schemes and multi-instance deployments with distinct identities.
Configures all provider-specific settings (API key, model, base URL) through a standardized set of environment variables (AI_CHAT_KEY, AI_CHAT_MODEL, AI_CHAT_BASE_URL) rather than configuration files or code. The OpenAI SDK client is instantiated at server startup using these variables, enabling provider swapping without recompilation or code changes.
Unique: Uses a minimal, standardized environment variable schema (4 variables) that maps directly to OpenAI SDK constructor parameters, avoiding configuration file parsing or custom schema validation. This design enables zero-code provider swapping and simplifies containerized deployment.
vs alternatives: Simpler than config-file-based approaches because environment variables are natively supported by container orchestration platforms (Docker, Kubernetes) and CI/CD systems without additional tooling.
Supports both streaming (token-by-token deltas via Server-Sent Events) and non-streaming (complete response) chat completion modes through the OpenAI SDK's built-in streaming parameter. The server passes the streaming preference to the OpenAI SDK, which handles protocol-level details, and the MCP protocol layer forwards responses back to the client.
Unique: Delegates streaming implementation to the OpenAI SDK rather than implementing custom streaming logic, ensuring compatibility with all OpenAI-format providers that support the streaming parameter. The MCP protocol layer transparently forwards streaming responses.
vs alternatives: More reliable than custom streaming implementations because it leverages the OpenAI SDK's battle-tested streaming logic and error handling.
Enables running the MCP server directly via 'npx @pyroprompts/any-chat-completions-mcp' without local installation, cloning, or building. NPX automatically downloads the latest published version from npm, executes it with provided environment variables, and handles cleanup. This approach requires only Node.js to be installed on the system.
Unique: Publishes pre-built JavaScript bundle to npm, enabling npx execution without requiring TypeScript compilation or build tools on the user's machine. This approach eliminates the 'works on my machine' problem by distributing compiled artifacts.
vs alternatives: Faster onboarding than source-based deployment because users don't need to clone, install dependencies, or build — npx handles everything automatically.
+3 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 any-chat-completions-mcp at 26/100.
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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