{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-openai","slug":"openai","name":"OpenAI","type":"mcp","url":"https://github.com/pierrebrunelle/mcp-server-openai","page_url":"https://unfragile.ai/openai","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-openai__cap_0","uri":"capability://tool.use.integration.cross.model.llm.invocation.via.mcp.protocol","name":"cross-model llm invocation via mcp protocol","description":"Exposes OpenAI API endpoints (GPT-4, GPT-3.5, o1, etc.) as MCP tools callable directly from Claude or other MCP clients. Implements the Model Context Protocol server specification to translate MCP tool calls into OpenAI API requests, handling authentication, request marshaling, and response streaming back through the MCP transport layer. Enables seamless model-to-model composition without requiring the client to manage separate API credentials or HTTP clients.","intents":["Use GPT-4 or o1 reasoning capabilities from within a Claude conversation without switching contexts","Build multi-model agent workflows where Claude orchestrates calls to OpenAI models for specialized tasks","Query OpenAI models with Claude's native tool-calling syntax and context management","Delegate computationally expensive reasoning tasks to o1 while maintaining Claude as the primary orchestrator"],"best_for":["AI engineers building multi-model agent systems","Teams using Claude as primary LLM but needing access to OpenAI-specific models (o1, GPT-4 Turbo)","Developers prototyping model ensemble architectures"],"limitations":["Requires valid OpenAI API key with sufficient quota; costs accumulate for every OpenAI model invocation","Latency includes MCP serialization/deserialization overhead (~50-100ms per round-trip) plus OpenAI API latency","No built-in caching or request deduplication — repeated identical queries generate separate API calls","Streaming responses must be buffered through MCP transport, potentially increasing memory usage for large outputs","No rate-limiting or quota management — relies on OpenAI's account-level rate limits"],"requires":["OpenAI API key (from https://platform.openai.com/api-keys)","MCP-compatible client (Claude Desktop, custom MCP client, or compatible LLM interface)","Network access to api.openai.com","Node.js 16+ (inferred from typical MCP server implementations)"],"input_types":["text prompts","structured JSON tool parameters","conversation history/messages array"],"output_types":["text completions","structured JSON responses","streaming token sequences"],"categories":["tool-use-integration","multi-model-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openai__cap_1","uri":"capability://tool.use.integration.openai.model.parameter.configuration.and.selection","name":"openai model parameter configuration and selection","description":"Exposes configurable parameters for OpenAI API calls (model selection, temperature, max_tokens, top_p, frequency_penalty, presence_penalty, etc.) through MCP tool schema. Allows callers to specify model variant (GPT-4, GPT-3.5-turbo, o1, etc.) and fine-tune generation behavior per request without modifying server configuration. Parameters are validated against OpenAI API constraints and passed directly to the underlying API client.","intents":["Switch between GPT-4 and GPT-3.5-turbo based on task complexity or cost constraints within a single conversation","Adjust temperature and top_p for different use cases (deterministic extraction vs. creative generation)","Control output length with max_tokens to prevent runaway responses or optimize for latency","Fine-tune sampling parameters (frequency_penalty, presence_penalty) to reduce repetition or encourage diversity"],"best_for":["Developers building adaptive agents that select models based on task requirements","Teams optimizing for cost/quality trade-offs across different workload types","Researchers experimenting with hyperparameter effects on model outputs"],"limitations":["Parameter validation is limited to OpenAI API constraints; invalid combinations may fail at API call time rather than MCP tool invocation time","No parameter presets or templates — each request must specify full configuration","Model availability depends on OpenAI account tier and region; newer models (o1) may have restricted access","No cost estimation or quota warnings before API invocation"],"requires":["OpenAI API key with access to desired models","Knowledge of OpenAI API parameter semantics and valid ranges"],"input_types":["model name string (e.g., 'gpt-4', 'gpt-3.5-turbo', 'o1')","numeric parameters (temperature 0-2, top_p 0-1, max_tokens integer)","string parameters (stop sequences)"],"output_types":["text completions with specified parameters applied","usage metadata (prompt_tokens, completion_tokens, total_tokens)"],"categories":["tool-use-integration","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openai__cap_2","uri":"capability://tool.use.integration.streaming.response.handling.with.mcp.transport","name":"streaming response handling with mcp transport","description":"Implements streaming of OpenAI API responses through the MCP protocol, allowing large or real-time outputs to be transmitted incrementally rather than buffered entirely. Converts OpenAI's server-sent events (SSE) stream into MCP-compatible streaming responses, maintaining token-by-token delivery semantics while respecting MCP message framing. Enables low-latency perception of model outputs in Claude and other MCP clients.","intents":["Receive real-time token-by-token output from OpenAI models for interactive user experiences","Process long-form outputs (articles, code, etc.) incrementally without waiting for full completion","Monitor token generation in real-time for cost tracking or early stopping","Build responsive multi-turn conversations with immediate feedback"],"best_for":["Interactive applications requiring low-latency user feedback","Long-form content generation (articles, code generation, documentation)","Real-time monitoring and cost tracking of model outputs"],"limitations":["Streaming adds complexity to error handling — partial responses may be delivered before failure occurs","MCP transport overhead per token may reduce effective throughput compared to direct OpenAI API streaming","Client must implement streaming response handling; not all MCP clients support streaming","Token-level granularity may expose internal model behavior (e.g., reasoning tokens in o1) that clients must handle"],"requires":["MCP client with streaming support","OpenAI API key with streaming-enabled models","Network connection stable enough for sustained streaming"],"input_types":["text prompts","tool parameters"],"output_types":["streaming text tokens","completion metadata (finish_reason, usage stats)"],"categories":["tool-use-integration","streaming-response-handling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openai__cap_3","uri":"capability://memory.knowledge.conversation.history.and.multi.turn.context.management","name":"conversation history and multi-turn context management","description":"Accepts OpenAI-compatible message arrays (with role, content, and optional function_calls fields) as input, enabling multi-turn conversations with full context history. Passes conversation state directly to OpenAI API without modification, allowing Claude to manage conversation context and delegate specific turns to OpenAI models. Supports system prompts, user messages, assistant responses, and tool/function call results in standard OpenAI format.","intents":["Maintain multi-turn conversations where Claude and OpenAI models exchange messages with full context","Inject system prompts or custom instructions into OpenAI model invocations","Delegate specific conversation turns to OpenAI while Claude maintains overall orchestration","Build complex workflows where Claude interprets OpenAI responses and decides next steps"],"best_for":["Multi-model conversational agents with stateful interactions","Teams building complex reasoning workflows across multiple models","Applications requiring custom system prompts or role-based instructions per model"],"limitations":["Context window is limited by OpenAI model (e.g., 128K for GPT-4 Turbo); no automatic truncation or summarization","No built-in conversation persistence — history must be managed by the client","Message format must strictly follow OpenAI API schema; malformed messages fail at API call time","No deduplication or caching of repeated conversation segments"],"requires":["OpenAI API key","Understanding of OpenAI message format (role, content, function_calls)"],"input_types":["message array with role (system/user/assistant), content (string), and optional function_calls","system prompt string","tool/function call results"],"output_types":["assistant message with content and optional function_calls","usage metadata (prompt_tokens, completion_tokens)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openai__cap_4","uri":"capability://tool.use.integration.function.calling.and.tool.schema.integration","name":"function calling and tool schema integration","description":"Exposes OpenAI's function calling API through MCP tool schema, allowing Claude to request that OpenAI models invoke specific functions or tools. Translates MCP tool definitions into OpenAI function_calls format, marshals function results back to OpenAI for follow-up reasoning, and handles the full function calling loop. Supports parallel function calls and automatic retry logic for failed invocations.","intents":["Have OpenAI models call external functions or APIs as part of their reasoning process","Build workflows where OpenAI models request specific computations or data lookups","Implement tool-augmented reasoning where OpenAI models decide which tools to invoke","Chain multiple function calls together with OpenAI model orchestration"],"best_for":["Developers building tool-augmented reasoning systems with OpenAI models","Teams implementing ReAct or similar agent patterns with OpenAI","Applications requiring OpenAI models to interact with external APIs or databases"],"limitations":["Function definitions must be provided in OpenAI schema format; no automatic schema generation from code","No built-in function execution — the MCP server only marshals requests; actual function execution is client responsibility","Function results must be returned to the server for follow-up reasoning; no asynchronous execution","Parallel function calls may increase latency if client executes sequentially"],"requires":["OpenAI API key with function calling support","Function definitions in OpenAI schema format (name, description, parameters)"],"input_types":["function schema definitions (name, description, parameters JSON schema)","function results (name, content)"],"output_types":["function_calls array with function name and arguments","follow-up reasoning after function execution"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openai__cap_5","uri":"capability://safety.moderation.api.key.and.authentication.management","name":"api key and authentication management","description":"Manages OpenAI API authentication by accepting and securely storing API keys (typically via environment variables or configuration). Injects credentials into all outbound OpenAI API requests without exposing them to the MCP client. Supports multiple authentication patterns (single key, key rotation, per-request key override) depending on deployment context.","intents":["Securely pass OpenAI credentials to the MCP server without embedding them in client code","Rotate or update API keys without restarting the server","Isolate credential management from the MCP client interface","Support multi-tenant deployments with different API keys per user or workspace"],"best_for":["Production deployments requiring secure credential isolation","Multi-tenant SaaS applications with per-user API keys","Teams using shared MCP servers with centralized credential management"],"limitations":["API key must be provided at server startup or via environment variables; no runtime credential injection UI","No built-in key rotation or expiration management — relies on OpenAI account management","Credentials are stored in server memory; no encryption at rest","No audit logging of API key usage or access patterns"],"requires":["OpenAI API key from https://platform.openai.com/api-keys","Environment variable or configuration file with OPENAI_API_KEY"],"input_types":["API key string (typically 48+ characters)"],"output_types":["authenticated API requests to OpenAI"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key (from https://platform.openai.com/api-keys)","MCP-compatible client (Claude Desktop, custom MCP client, or compatible LLM interface)","Network access to api.openai.com","Node.js 16+ (inferred from typical MCP server implementations)","OpenAI API key with access to desired models","Knowledge of OpenAI API parameter semantics and valid ranges","MCP client with streaming support","OpenAI API key with streaming-enabled models","Network connection stable enough for sustained streaming","OpenAI API key"],"failure_modes":["Requires valid OpenAI API key with sufficient quota; costs accumulate for every OpenAI model invocation","Latency includes MCP serialization/deserialization overhead (~50-100ms per round-trip) plus OpenAI API latency","No built-in caching or request deduplication — repeated identical queries generate separate API calls","Streaming responses must be buffered through MCP transport, potentially increasing memory usage for large outputs","No rate-limiting or quota management — relies on OpenAI's account-level rate limits","Parameter validation is limited to OpenAI API constraints; invalid combinations may fail at API call time rather than MCP tool invocation time","No parameter presets or templates — each request must specify full configuration","Model availability depends on OpenAI account tier and region; newer models (o1) may have restricted access","No cost estimation or quota warnings before API invocation","Streaming adds complexity to error handling — partial responses may be delivered before failure occurs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.579Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=openai","compare_url":"https://unfragile.ai/compare?artifact=openai"}},"signature":"8wvNesML3ixEJz6kxR2RoE5k3Y9gsEoeNUjz81IIEtRl4WSCrsKrzAxtrjf37/RxE2kUyj1Q9slFzr2COOfjAA==","signedAt":"2026-06-20T12:09:38.586Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai","artifact":"https://unfragile.ai/openai","verify":"https://unfragile.ai/api/v1/verify?slug=openai","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}