{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-z_ai-mcp-server","slug":"z_ai-mcp-server","name":"@z_ai/mcp-server","type":"mcp","url":"https://docs.z.ai/","page_url":"https://unfragile.ai/z_ai-mcp-server","categories":["mcp-servers"],"tags":["zai","mcp","vision"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-z_ai-mcp-server__cap_0","uri":"capability://tool.use.integration.mcp.server.transport.abstraction.with.z.ai.api.integration","name":"mcp server transport abstraction with z.ai api integration","description":"Implements Model Context Protocol server that bridges MCP clients (Claude Desktop, IDEs, agents) to Z.AI's backend API infrastructure. Uses stdio/SSE transport to expose Z.AI's language models, vision models, and tool capabilities through standardized MCP protocol, abstracting away Z.AI API authentication (Bearer token), endpoint routing, and request/response marshaling. Handles protocol negotiation, capability advertisement, and bidirectional message passing between MCP client and Z.AI backend.","intents":["I want to use Z.AI models inside Claude Desktop or other MCP-compatible clients without custom integration code","I need to expose Z.AI's capabilities (text generation, vision, coding) as MCP tools and resources to my agent framework","I want to standardize how my AI applications access Z.AI instead of direct REST API calls"],"best_for":["AI agent developers integrating Z.AI models into MCP-compatible frameworks","Teams standardizing on MCP protocol for multi-model AI workflows","Claude Desktop users wanting native Z.AI model access"],"limitations":["MCP server configuration and transport type (stdio vs SSE) not documented in provided materials","No explicit rate limiting, concurrent request limits, or quota management documented","Requires Z.AI API key management and authentication setup — no built-in key rotation or scope-based access control documented","Architectural details of MCP tool/resource definitions not provided — cannot verify schema completeness or validation"],"requires":["Z.AI API key from Z.AI Open Platform registration","Node.js runtime (npm package distribution)","MCP-compatible client (Claude Desktop, custom MCP host, or agent framework with MCP support)","Network access to Z.AI API endpoints (https://api.z.ai/api/paas/v4 or https://api.z.ai/api/coding/paas/v4)"],"input_types":["MCP protocol messages (JSON-RPC 2.0 format)","Tool invocation requests with parameters","Resource read/list requests"],"output_types":["MCP protocol responses (JSON-RPC 2.0)","Tool execution results","Resource representations"],"categories":["tool-use-integration","mcp-server"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-z_ai-mcp-server__cap_1","uri":"capability://text.generation.language.multi.model.language.generation.with.provider.agnostic.routing","name":"multi-model language generation with provider-agnostic routing","description":"Exposes Z.AI's language model family (GLM-5.1, GLM-5, GLM-5-Turbo, GLM-4.7, GLM-4.6, GLM-4.5, GLM-4-32B-0414-128K) through MCP tool interface, routing requests to appropriate model based on capability requirements (context window, latency, cost). Implements model selection logic that abstracts model-specific parameters, token limits, and performance characteristics. Supports streaming and batch inference modes with configurable temperature, top-p, and other generation parameters.","intents":["I want to choose between different Z.AI language models (GLM-5.1 vs GLM-5-Turbo) based on latency/cost tradeoffs without changing my MCP client code","I need to generate text with specific quality/speed requirements and want the server to select the optimal model","I want to use Z.AI models for chat, summarization, and code generation through a single MCP interface"],"best_for":["Developers building multi-model AI applications that need cost/latency optimization","Teams evaluating Z.AI models against other providers and wanting unified interface","Applications requiring fallback model selection based on availability or performance"],"limitations":["Model selection logic and routing strategy not documented — cannot verify if selection is automatic, manual, or heuristic-based","Token limit handling and context window management not specified for each model variant","No documented support for model-specific features (e.g., GLM-4-32B-0414-128K's 128K context) or how to access them via MCP","Streaming implementation details unknown — buffering, chunk size, error recovery not documented"],"requires":["Z.AI API key with access to language model endpoints","Knowledge of available model names and their capabilities (GLM-5.1, GLM-5-Turbo, etc.)","MCP client capable of passing model selection parameters in tool invocation"],"input_types":["Text prompts (string)","Generation parameters (temperature, top_p, max_tokens, model_name)","Optional: conversation history for chat models"],"output_types":["Generated text (string)","Token usage metadata (prompt_tokens, completion_tokens)","Optional: streaming chunks (text deltas)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-z_ai-mcp-server__cap_10","uri":"capability://tool.use.integration.bearer.token.authentication.with.api.key.management","name":"bearer token authentication with api key management","description":"Implements Bearer token authentication for Z.AI API access, accepting API keys from Z.AI Open Platform and converting them to Bearer tokens for API requests. Handles token lifecycle (generation, refresh if applicable, expiration), secure storage (environment variables or secure config), and per-request token injection into Authorization headers. Implements error handling for invalid/expired tokens with clear error messages.","intents":["I want to securely authenticate MCP server to Z.AI API using my API key","I need to manage API key lifecycle without exposing credentials in code","I want to rotate API keys without restarting the MCP server"],"best_for":["Developers deploying MCP server in production environments","Teams managing multiple API keys for different environments/users","Security-conscious organizations requiring credential management"],"limitations":["Token refresh mechanism not documented — unclear if tokens expire or are long-lived","API key rotation procedure not specified — may require server restart","No documented support for scope-based access control or permission levels","Secure storage mechanism (environment variables, secret managers) not specified","No documented support for API key expiration or automatic rotation","Rate limiting per API key not documented"],"requires":["Z.AI API key from Z.AI Open Platform registration","Secure storage mechanism for API key (environment variable, secret manager, etc.)","Network access to Z.AI API endpoints"],"input_types":["API key (string from Z.AI platform)"],"output_types":["Bearer token (for Authorization header)","Authentication status (success/failure with error details)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-z_ai-mcp-server__cap_11","uri":"capability://tool.use.integration.model.capability.advertisement.and.client.negotiation","name":"model capability advertisement and client negotiation","description":"Implements MCP protocol capability advertisement, informing clients of available models, tools, and resources exposed by the server. Uses MCP protocol initialization handshake to exchange supported capabilities, protocol version, and implementation details. Enables clients to discover available models (GLM-5.1, GLM-5V-Turbo, CogView-4, etc.) and tools (web search, function calling, etc.) without hardcoding assumptions.","intents":["I want my MCP client to discover what models and tools are available from the Z.AI MCP server","I need to verify server capabilities before attempting to use specific models or tools","I want to build clients that adapt to different MCP server configurations"],"best_for":["MCP client developers building flexible, server-agnostic clients","Teams deploying multiple MCP servers with different capabilities","Applications that need to adapt to available models at runtime"],"limitations":["Capability advertisement format and schema not documented","Model metadata (context window, latency, cost) not specified in capability advertisement","Tool schema advertisement format not documented","Dynamic capability updates (adding/removing models) not supported or not documented","No documented support for capability versioning or deprecation notices"],"requires":["MCP client with protocol negotiation support","MCP server initialization (stdio or SSE transport)"],"input_types":["MCP protocol initialization request"],"output_types":["Capability advertisement (available models, tools, resources)","Protocol version and implementation details","Server metadata (name, version)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-z_ai-mcp-server__cap_2","uri":"capability://image.visual.vision.and.multimodal.image.understanding","name":"vision and multimodal image understanding","description":"Exposes Z.AI's vision model family (GLM-5V-Turbo, GLM-4.6V, GLM-4.5V) and specialized models (GLM-OCR for document extraction, AutoGLM-Phone-Multilingual for mobile UI understanding) through MCP tool interface. Accepts image inputs (base64, URL, or file path) and processes them with vision-specific models, returning structured analysis (object detection, text extraction, scene understanding, OCR results). Implements image preprocessing (resizing, format conversion) and model-specific input validation.","intents":["I want to analyze images (screenshots, documents, photos) using Z.AI vision models through my MCP client","I need to extract text from documents or images using GLM-OCR without building custom OCR pipelines","I want to understand mobile UI screenshots or phone app interfaces using specialized vision models"],"best_for":["Developers building document processing or image analysis workflows","Teams automating screenshot analysis, UI testing, or accessibility auditing","Applications requiring OCR or text extraction from images"],"limitations":["Vision model selection logic and automatic routing between GLM-5V-Turbo, GLM-4.6V, GLM-4.5V not documented","Image input format support (base64, URL, file path) and size limits not specified","OCR-specific output format (bounding boxes, confidence scores, layout structure) not documented","No documented support for batch image processing or multi-image analysis in single request","AutoGLM-Phone-Multilingual language support and mobile UI understanding capabilities not detailed"],"requires":["Z.AI API key with vision model access","Image input in supported format (JPEG, PNG, WebP, GIF, or base64 encoded)","MCP client capable of passing image data to tool invocation"],"input_types":["Image (base64-encoded string, URL, or file path)","Optional: analysis instructions or query (string)","Optional: model selection parameter (GLM-5V-Turbo, GLM-OCR, etc.)"],"output_types":["Structured analysis (JSON with detected objects, text, scene description)","OCR results (extracted text with optional bounding boxes)","Confidence scores and metadata"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-z_ai-mcp-server__cap_3","uri":"capability://image.visual.image.generation.with.cogview.4.and.style.control","name":"image generation with cogview-4 and style control","description":"Exposes Z.AI's image generation model (CogView-4) through MCP tool interface, accepting text prompts and optional style parameters to generate images. Implements prompt processing, style embedding, and image encoding (base64 or URL return format). Supports iterative refinement through prompt modification without explicit inpainting, leveraging CogView-4's prompt understanding for style consistency.","intents":["I want to generate images from text descriptions using Z.AI's CogView-4 model through MCP","I need to create styled images (specific art style, aesthetic) without managing image generation APIs directly","I want to integrate image generation into my AI agent workflow"],"best_for":["Content creators and designers automating image generation workflows","AI agents that need to generate visual content as part of task execution","Applications combining text and image generation in single MCP interface"],"limitations":["CogView-4 prompt engineering requirements and style control mechanisms not documented","Image resolution, aspect ratio, and quality settings not specified","Batch generation support (multiple images per prompt) not documented","Inpainting, outpainting, or image editing capabilities not mentioned — appears to be generation-only","Rate limiting and generation latency not documented","No documented support for negative prompts or advanced generation parameters"],"requires":["Z.AI API key with image generation model access","Text prompt describing desired image","MCP client capable of handling image output (base64 or URL)"],"input_types":["Text prompt (string)","Optional: style parameters (string or structured format)","Optional: generation parameters (seed, steps, guidance_scale if supported)"],"output_types":["Generated image (base64-encoded or URL)","Image metadata (dimensions, generation time, seed if applicable)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-z_ai-mcp-server__cap_4","uri":"capability://image.visual.video.generation.with.cogvideox.3.and.vidu.models","name":"video generation with cogvideox-3 and vidu models","description":"Exposes Z.AI's video generation models (CogVideoX-3, Vidu Q1, Vidu 2) through MCP tool interface, accepting text prompts or image+text inputs to generate short videos. Implements video encoding, streaming output, and asynchronous generation handling (polling or webhook-based completion notification). Supports different video quality/length tradeoffs across model variants.","intents":["I want to generate short videos from text descriptions using Z.AI's video models through MCP","I need to create videos from static images with motion and scene understanding","I want to integrate video generation into my AI agent without managing async job polling"],"best_for":["Content creators automating video generation workflows","AI agents that need to generate video content as part of task execution","Applications combining text, image, and video generation"],"limitations":["Video generation is inherently asynchronous — MCP tool invocation model may not map cleanly to long-running generation jobs","Polling mechanism or webhook notification for job completion not documented","Video duration, resolution, frame rate, and quality settings not specified for each model","CogVideoX-3 vs Vidu Q1 vs Vidu 2 capability differences and selection logic not documented","Video output format (MP4, WebM, etc.) and streaming/download mechanism not specified","No documented support for video editing, frame interpolation, or style transfer"],"requires":["Z.AI API key with video generation model access","Text prompt or image input describing desired video","MCP client capable of handling async operations or polling for completion","Storage/delivery mechanism for generated video files (potentially large)"],"input_types":["Text prompt (string)","Optional: reference image (base64 or URL)","Optional: model selection (CogVideoX-3, Vidu Q1, Vidu 2)","Optional: generation parameters (duration, quality)"],"output_types":["Video file (URL or base64-encoded stream)","Job ID for async polling","Generation metadata (duration, resolution, model used)"],"categories":["image-visual","tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-z_ai-mcp-server__cap_5","uri":"capability://data.processing.analysis.audio.speech.recognition.with.glm.asr.2512","name":"audio speech recognition with glm-asr-2512","description":"Exposes Z.AI's automatic speech recognition model (GLM-ASR-2512) through MCP tool interface, accepting audio input (file, URL, or stream) and returning transcribed text with optional speaker identification and timestamp metadata. Implements audio format detection, preprocessing (resampling, normalization), and streaming transcription for long audio files.","intents":["I want to transcribe audio files or live audio streams using Z.AI's ASR model through MCP","I need to extract text from voice recordings without managing audio processing pipelines","I want to build voice-enabled AI agents that understand spoken input"],"best_for":["Developers building voice-enabled AI applications and agents","Teams automating audio transcription workflows","Applications combining speech recognition with language understanding"],"limitations":["Audio format support (WAV, MP3, FLAC, OGG, etc.) and sample rate requirements not documented","Maximum audio duration and file size limits not specified","Speaker identification and diarization capabilities not documented","Timestamp/segment-level output format not specified","Language support for GLM-ASR-2512 not documented (multilingual capability unclear)","Streaming transcription implementation (chunking, latency) not detailed","No documented support for speaker-dependent models or acoustic model adaptation"],"requires":["Z.AI API key with ASR model access","Audio input in supported format","MCP client capable of streaming audio or passing audio file references"],"input_types":["Audio file (WAV, MP3, FLAC, OGG, or other supported format)","Audio URL (for remote files)","Optional: audio stream (for real-time transcription)","Optional: language hint (if multilingual)"],"output_types":["Transcribed text (string)","Confidence scores per segment","Optional: timestamps (start/end times for segments)","Optional: speaker identification (if supported)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-z_ai-mcp-server__cap_6","uri":"capability://tool.use.integration.function.calling.with.schema.based.tool.invocation","name":"function calling with schema-based tool invocation","description":"Implements function calling capability through MCP tool interface, allowing Z.AI language models to invoke external functions/tools based on semantic understanding of task requirements. Uses JSON Schema to define function signatures, parameters, and return types. Routes function calls from Z.AI models to actual implementations (either local MCP tools or external APIs), handles parameter validation, execution, and result marshaling back to model for further reasoning.","intents":["I want Z.AI language models to autonomously call external tools/APIs based on task requirements","I need to define custom functions that Z.AI models can invoke as part of reasoning chains","I want to build AI agents that can use tools to accomplish multi-step tasks"],"best_for":["AI agent developers building tool-using agents with Z.AI models","Teams automating complex workflows that require model reasoning + tool execution","Applications needing semantic function routing (model decides which tool to use)"],"limitations":["Function schema definition format and validation not documented","Parameter type support (primitives, objects, arrays, enums) not specified","Error handling for failed function calls and retry logic not documented","Maximum number of functions per model invocation not specified","Parallel function calling support not documented","Function execution timeout and resource limits not specified","No documented support for function result streaming or incremental execution"],"requires":["Z.AI API key with function calling model support","Function definitions in JSON Schema format","Function implementations (local or remote endpoints)","MCP client capable of handling tool invocation and result passing"],"input_types":["Task description or prompt (string)","Function definitions (JSON Schema array)","Optional: function implementations (local callables or API endpoints)"],"output_types":["Function invocation requests (function name + parameters)","Function execution results (return value + metadata)","Model reasoning trace (which functions were called and why)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-z_ai-mcp-server__cap_7","uri":"capability://search.retrieval.web.search.integration.with.semantic.result.ranking","name":"web search integration with semantic result ranking","description":"Exposes web search capability through MCP tool interface, accepting search queries and returning ranked results with semantic relevance scoring. Implements query expansion, result deduplication, and optional snippet extraction with source attribution. Integrates with Z.AI language models to enable fact-grounded generation and real-time information access.","intents":["I want Z.AI language models to search the web and ground responses in current information","I need to retrieve relevant web results for a query without managing search API integrations","I want to build AI agents that can research topics and cite sources"],"best_for":["AI agents that need real-time information access","Applications requiring fact-grounded language generation","Teams building research or knowledge discovery tools"],"limitations":["Search result ranking algorithm and semantic scoring mechanism not documented","Number of results returned per query not specified","Snippet extraction and content filtering not detailed","Search result freshness and index update frequency not documented","Query expansion and reformulation logic not specified","No documented support for advanced search operators (site:, filetype:, etc.)","Rate limiting and quota per API key not specified","Geographic/language targeting options not documented"],"requires":["Z.AI API key with web search capability enabled","Search query (string)","MCP client capable of handling search results"],"input_types":["Search query (string)","Optional: number of results to return","Optional: result type filter (news, academic, images, etc.)","Optional: language or geographic targeting"],"output_types":["Search results (array of {title, URL, snippet, relevance_score})","Result metadata (total results found, search time)","Optional: full page content (if snippet extraction enabled)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-z_ai-mcp-server__cap_8","uri":"capability://tool.use.integration.streaming.tool.call.execution.with.incremental.result.delivery","name":"streaming tool call execution with incremental result delivery","description":"Implements streaming execution of tool calls through MCP protocol, allowing long-running tools to return results incrementally rather than blocking until completion. Uses server-sent events or chunked transfer encoding to deliver partial results, enabling real-time feedback and progressive refinement. Handles backpressure, error recovery, and result aggregation for tools that produce large outputs (e.g., document processing, video generation).","intents":["I want tool results to stream back to the client as they become available instead of waiting for completion","I need to process large outputs (documents, videos, logs) without buffering entire results in memory","I want to provide real-time feedback to users while long-running tools execute"],"best_for":["Applications with long-running tools (video generation, document processing, data analysis)","Real-time AI agents that need to provide incremental feedback","Systems processing large outputs that don't fit in memory"],"limitations":["Streaming protocol details (SSE vs chunked encoding) not documented","Backpressure handling and flow control mechanisms not specified","Error recovery and partial result handling not detailed","Chunk size and buffering strategy not documented","Timeout handling for stalled streams not specified","No documented support for bidirectional streaming (client sending data while receiving results)"],"requires":["MCP client with streaming support","Tools that support incremental result delivery","Network connection capable of maintaining long-lived streams"],"input_types":["Tool invocation request (standard MCP format)","Optional: streaming preferences (chunk size, timeout)"],"output_types":["Streamed result chunks (partial tool output)","Completion signal (final chunk or explicit end marker)","Optional: progress metadata (bytes processed, estimated time remaining)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-z_ai-mcp-server__cap_9","uri":"capability://tool.use.integration.api.endpoint.routing.with.dual.endpoint.support.general.vs.coding.specific","name":"api endpoint routing with dual-endpoint support (general vs coding-specific)","description":"Implements intelligent routing between Z.AI's two API endpoints: general-purpose endpoint (https://api.z.ai/api/paas/v4) for standard use cases and coding-specific endpoint (https://api.z.ai/api/coding/paas/v4) for code generation, debugging, and analysis tasks. Routes requests based on tool type, model capability, or explicit endpoint selection. Handles endpoint-specific authentication, rate limiting, and error responses.","intents":["I want to automatically use the coding-optimized endpoint for code generation tasks without manual configuration","I need to route requests to the appropriate Z.AI endpoint based on task type","I want to optimize latency and cost by using specialized endpoints for specific workloads"],"best_for":["Applications with mixed workloads (general text + code generation)","Teams optimizing for cost/latency across different task types","Developers building coding assistants that need specialized endpoints"],"limitations":["Endpoint selection logic and routing heuristics not documented","Coding endpoint restrictions ('intended for supported tools only') not detailed — unclear which tools are supported","Rate limiting and quota differences between endpoints not specified","Fallback behavior if coding endpoint is unavailable not documented","No documented support for custom endpoint configuration or on-premise deployments"],"requires":["Z.AI API key valid for both endpoints","Knowledge of which endpoint is appropriate for specific tasks","MCP client capable of specifying endpoint preference (if manual routing needed)"],"input_types":["Tool invocation request with task type or model selection","Optional: explicit endpoint preference"],"output_types":["Response from selected endpoint","Endpoint metadata (which endpoint was used, latency)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Z.AI API key from Z.AI Open Platform registration","Node.js runtime (npm package distribution)","MCP-compatible client (Claude Desktop, custom MCP host, or agent framework with MCP support)","Network access to Z.AI API endpoints (https://api.z.ai/api/paas/v4 or https://api.z.ai/api/coding/paas/v4)","Z.AI API key with access to language model endpoints","Knowledge of available model names and their capabilities (GLM-5.1, GLM-5-Turbo, etc.)","MCP client capable of passing model selection parameters in tool invocation","Secure storage mechanism for API key (environment variable, secret manager, etc.)","Network access to Z.AI API endpoints","MCP client with protocol negotiation support"],"failure_modes":["MCP server configuration and transport type (stdio vs SSE) not documented in provided materials","No explicit rate limiting, concurrent request limits, or quota management documented","Requires Z.AI API key management and authentication setup — no built-in key rotation or scope-based access control documented","Architectural details of MCP tool/resource definitions not provided — cannot verify schema completeness or validation","Model selection logic and routing strategy not documented — cannot verify if selection is automatic, manual, or heuristic-based","Token limit handling and context window management not specified for each model variant","No documented support for model-specific features (e.g., GLM-4-32B-0414-128K's 128K context) or how to access them via MCP","Streaming implementation details unknown — buffering, chunk size, error recovery not documented","Token refresh mechanism not documented — unclear if tokens expire or are long-lived","API key rotation procedure not specified — may require server restart","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4905106126457851,"quality":0.34,"ecosystem":0.38999999999999996,"match_graph":0.25,"freshness":0.6,"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":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.472Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":14175,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=z_ai-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=z_ai-mcp-server"}},"signature":"zrVYk/p7mp21BvqoJJPKDQwQR9g+X0xO7WqlXUXexHAsdCWfzthB6ar9okYHhZE3fHR9expeaQeNn2GJIP3UBw==","signedAt":"2026-06-21T06:25:44.950Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/z_ai-mcp-server","artifact":"https://unfragile.ai/z_ai-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=z_ai-mcp-server","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"}}