{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-piapi","slug":"piapi","name":"PiAPI","type":"mcp","url":"https://github.com/apinetwork/piapi-mcp-server","page_url":"https://unfragile.ai/piapi","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-piapi__cap_0","uri":"capability://tool.use.integration.multi.provider.image.generation.via.unified.mcp.interface","name":"multi-provider image generation via unified mcp interface","description":"Generates images through Midjourney, Flux, or Hunyuan by translating MCP tool calls into PiAPI requests, handling asynchronous task polling, and returning generated image URLs. Uses a request-response pattern where clients submit structured prompts and receive URLs to completed assets after polling for task completion status.","intents":["Generate product mockups or design variations directly from Claude without leaving the chat interface","Create multiple image variations with different AI models to compare quality and style","Integrate image generation into multi-step workflows that combine text analysis and visual creation"],"best_for":["AI application developers building Claude-integrated creative tools","Design teams using Claude as a creative assistant","Builders prototyping multi-modal AI workflows"],"limitations":["Asynchronous polling adds latency — typical generation takes 30-120 seconds depending on model","No streaming of generation progress — clients must poll until task completion","Image quality and style consistency varies significantly between Midjourney, Flux, and Hunyuan models","Rate limiting depends on underlying PiAPI service quotas, not configurable per-client"],"requires":["Node.js 18+","PiAPI API key with active subscription","MCP-compatible client (Claude Desktop, Cursor IDE, or custom MCP client)","Network connectivity to PiAPI service"],"input_types":["text (natural language prompts)","structured parameters (style, aspect ratio, quality settings)"],"output_types":["image URLs (HTTPS links to generated assets)","task status metadata (pending, completed, failed)"],"categories":["tool-use-integration","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-piapi__cap_1","uri":"capability://tool.use.integration.video.generation.with.multiple.ai.backends","name":"video generation with multiple ai backends","description":"Generates videos through Kling, Luma Dream Machine, Hunyuan Video, Skyreels, Wan, or Hailuo by submitting text prompts or image-to-video requests to PiAPI and polling for completion. Supports both text-to-video and image-to-video workflows with model-specific parameters (duration, quality, effects).","intents":["Generate short-form video content from text descriptions for social media or marketing","Create animated sequences from static images for product demos or presentations","Batch generate multiple video variations to test different creative directions"],"best_for":["Content creators building AI-assisted video production workflows","Marketing teams generating promotional videos at scale","Developers building video-first AI applications"],"limitations":["Video generation is slower than image generation — typical 2-10 minute wait times","Model availability varies by region and PiAPI subscription tier","Output video quality and duration limits differ per model (e.g., Kling max 10 seconds, Luma max 2 minutes)","No real-time preview or iterative refinement — must regenerate entire video for changes"],"requires":["Node.js 18+","PiAPI API key with video generation tier","MCP-compatible client","Sufficient PiAPI credits for video generation (higher cost than images)"],"input_types":["text (video descriptions/prompts)","image URLs (for image-to-video workflows)","structured parameters (duration, aspect ratio, style)"],"output_types":["video URLs (HTTPS links to MP4 or WebM files)","task metadata (generation time, model used, resolution)"],"categories":["tool-use-integration","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-piapi__cap_10","uri":"capability://data.processing.analysis.output.validation.and.result.formatting","name":"output validation and result formatting","description":"Validates generation results from PiAPI (image URLs, video URLs, audio URLs, 3D model URLs) against expected formats and accessibility. Checks that URLs are valid HTTPS links, files are accessible, and metadata matches the request. Formats results into MCP-compatible response objects with structured metadata (dimensions, duration, file size, format). Handles missing or malformed results gracefully.","intents":["Ensure generated assets are accessible and usable before returning to clients","Provide structured metadata about generated assets for downstream processing","Detect and report generation failures or corrupted outputs"],"best_for":["Production systems requiring high result quality and reliability","Developers building downstream processing pipelines that depend on asset metadata","Teams needing audit trails of generated content"],"limitations":["URL validation only checks format — doesn't verify file accessibility (would add latency)","Metadata extraction is limited to what PiAPI returns — no deep inspection of assets","No content moderation or safety checks — relies on PiAPI's content filtering","Validation errors are reported to clients but don't trigger automatic regeneration"],"requires":["Node.js 18+","MCP-compatible client","Valid result URLs from PiAPI"],"input_types":["PiAPI generation results (URLs, metadata, status codes)"],"output_types":["validated result objects with structured metadata","error messages for invalid or missing results"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-piapi__cap_11","uri":"capability://automation.workflow.docker.deployment.and.containerization","name":"docker deployment and containerization","description":"Provides Docker configuration for containerized deployment of the PiAPI MCP server, including Dockerfile, docker-compose.yml, and environment variable templates. Supports both development (with hot-reload) and production (optimized image size) builds. Enables easy deployment to Kubernetes, Docker Swarm, or cloud container services (AWS ECS, Google Cloud Run, Azure Container Instances).","intents":["Deploy PiAPI MCP server to cloud infrastructure without manual configuration","Scale the MCP server horizontally using container orchestration platforms","Standardize deployment across development, staging, and production environments"],"best_for":["DevOps teams deploying PiAPI MCP to production infrastructure","Organizations using Kubernetes or Docker Swarm for container orchestration","Teams needing consistent deployment across multiple environments"],"limitations":["Docker image size is large (~500MB+) due to Node.js and dependencies","No built-in health checks or readiness probes — requires custom Kubernetes manifests","Environment variable configuration is basic — no support for secrets management","Single-container deployment doesn't include load balancing or auto-scaling"],"requires":["Docker 20.10+ or Docker Desktop","docker-compose 1.29+ (for multi-container deployments)","PiAPI API key configured as environment variable","Network access to PiAPI backend from container"],"input_types":["Dockerfile and docker-compose.yml configuration","environment variables (PiAPI_API_KEY, etc.)"],"output_types":["Docker image (ready for deployment)","running container with MCP server listening on stdio or network socket"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-piapi__cap_12","uri":"capability://automation.workflow.smithery.platform.integration.for.one.click.deployment","name":"smithery platform integration for one-click deployment","description":"Integrates with the Smithery platform to enable one-click deployment of the PiAPI MCP server to Smithery's managed hosting. Provides Smithery-specific configuration and deployment manifests. Handles authentication, environment variable setup, and server lifecycle management through Smithery's UI.","intents":["Deploy PiAPI MCP server to Smithery without manual configuration or DevOps knowledge","Share PiAPI MCP server with other users through Smithery's marketplace","Manage server updates and scaling through Smithery's dashboard"],"best_for":["Non-technical users wanting to deploy PiAPI MCP without DevOps experience","Developers sharing MCP servers through Smithery's marketplace","Teams using Smithery as their primary MCP hosting platform"],"limitations":["Smithery platform lock-in — migrating to other hosting requires manual reconfiguration","Limited customization compared to self-hosted Docker deployments","Smithery pricing and availability depend on platform decisions","No direct access to server logs or infrastructure — debugging requires Smithery support"],"requires":["Smithery account with active subscription","PiAPI API key configured in Smithery environment","Network access from Smithery infrastructure to PiAPI backend"],"input_types":["Smithery deployment manifest","environment variables (PiAPI_API_KEY, etc.)"],"output_types":["deployed MCP server accessible through Smithery","server URL and connection details"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-piapi__cap_13","uri":"capability://code.generation.editing.typescript.based.extensibility.for.adding.new.ai.tools","name":"typescript-based extensibility for adding new ai tools","description":"Provides a TypeScript-based framework for extending the MCP server with new AI generation tools. Developers can add new tools by implementing a standard interface (tool name, description, parameters, handler function) and registering them in the tool registry. Includes utilities for schema generation, parameter validation, and result formatting. Supports both synchronous and asynchronous tool implementations.","intents":["Add support for new AI generation models (e.g., new video models, image models) without modifying core MCP code","Implement custom generation workflows that combine multiple models","Extend the MCP server with proprietary or experimental generation capabilities"],"best_for":["Developers extending PiAPI MCP with custom generation tools","Teams building proprietary AI generation workflows","Contributors adding new models to the open-source project"],"limitations":["Requires TypeScript/JavaScript knowledge — not accessible to non-programmers","Tool registry must be rebuilt and server restarted for new tools to take effect","No built-in testing framework — developers must write their own tests","Documentation for extension points is limited — requires reading source code"],"requires":["Node.js 18+","TypeScript 4.5+","Understanding of MCP protocol and tool schema format","PiAPI API key for testing new tools"],"input_types":["TypeScript tool implementation (class or function)","tool schema definition (JSON schema format)"],"output_types":["registered MCP tool available to clients","tool results matching defined schema"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-piapi__cap_14","uri":"capability://automation.workflow.environment.variable.configuration.and.secrets.management","name":"environment variable configuration and secrets management","description":"Manages PiAPI credentials and server configuration through environment variables, supporting both .env files and system environment variables. Validates required configuration at startup and provides helpful error messages for missing credentials. Supports configuration overrides for different deployment environments (development, staging, production) through environment-specific .env files.","intents":["Securely manage PiAPI API keys without hardcoding them in source code","Configure different PiAPI endpoints or credentials for different deployment environments","Enable easy deployment to cloud platforms that use environment variables for secrets"],"best_for":["Teams deploying PiAPI MCP to production with security requirements","DevOps engineers managing multi-environment deployments","Developers working with cloud platforms (AWS, Google Cloud, Azure) that use environment variables"],"limitations":["Environment variables are visible in process listings — not suitable for highly sensitive secrets","No built-in secrets rotation — credentials must be manually updated",".env files are not encrypted — should not be committed to version control","Configuration validation is basic — invalid values may not be caught until runtime"],"requires":["Node.js 18+","PiAPI API key","dotenv package (included in dependencies)"],"input_types":[".env files or system environment variables","configuration keys (PiAPI_API_KEY, PiAPI_ENDPOINT, etc.)"],"output_types":["validated configuration object","error messages for missing or invalid configuration"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-piapi__cap_2","uri":"capability://tool.use.integration.music.and.audio.generation.with.style.control","name":"music and audio generation with style control","description":"Generates music and audio through Suno, MMAudio, or zero-shot TTS by submitting prompts with style/mood parameters to PiAPI. Supports both standalone music generation and video-synchronized audio generation (MMAudio generates music matching video content). Uses asynchronous task polling to retrieve generated audio files.","intents":["Generate background music for videos with mood/style matching the visual content","Create original music tracks from text descriptions for projects without licensing concerns","Generate voiceovers or narration with zero-shot TTS without pre-recorded samples"],"best_for":["Video creators needing royalty-free music generation","Developers building audio-first AI applications","Content teams automating voiceover and music production"],"limitations":["Music generation quality is inconsistent — Suno produces better results than MMAudio but with longer wait times","Zero-shot TTS has limited voice variety and accent support","Generated music may have copyright/licensing ambiguity in some jurisdictions","Audio length limits vary by model (Suno max ~4 minutes, TTS depends on text length)"],"requires":["Node.js 18+","PiAPI API key with audio generation tier","MCP-compatible client","For MMAudio: video URL or video file to analyze for mood/style"],"input_types":["text (music descriptions, lyrics, or text for TTS)","structured parameters (style, mood, duration, voice type)","video URLs (for MMAudio video-to-music workflows)"],"output_types":["audio URLs (HTTPS links to MP3 or WAV files)","audio metadata (duration, sample rate, format)"],"categories":["tool-use-integration","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-piapi__cap_3","uri":"capability://tool.use.integration.image.manipulation.and.enhancement.toolkit","name":"image manipulation and enhancement toolkit","description":"Performs image transformations (face swap, background removal, segmentation, upscaling) by submitting images to PiAPI and retrieving processed results. Each operation uses specialized models: face swap uses identity-preserving diffusion, RMBG uses semantic segmentation, upscaling uses super-resolution networks. Operations are stateless and return processed image URLs.","intents":["Remove backgrounds from product photos for e-commerce listings","Upscale low-resolution images to higher quality for printing or display","Swap faces in images for creative effects or testing","Segment images to extract specific objects or regions"],"best_for":["E-commerce teams automating product image processing","Designers needing batch image enhancement","Developers building image editing tools within Claude"],"limitations":["Face swap quality depends on input image quality and face visibility — fails on obscured or profile faces","Background removal struggles with complex edges (hair, fur) and transparent objects","Upscaling has diminishing returns above 4x magnification and may introduce artifacts","Segmentation accuracy varies by object complexity — works best on distinct, well-lit subjects","All operations are destructive — no undo or layer support"],"requires":["Node.js 18+","PiAPI API key with image manipulation tier","MCP-compatible client","Image URLs or base64-encoded images as input"],"input_types":["image URLs (HTTPS links to source images)","base64-encoded images","structured parameters (upscale factor, segmentation class, face swap target)"],"output_types":["processed image URLs","segmentation masks (for segmentation operations)","image metadata (dimensions, format, processing time)"],"categories":["tool-use-integration","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-piapi__cap_4","uri":"capability://tool.use.integration.video.manipulation.and.enhancement","name":"video manipulation and enhancement","description":"Applies transformations to existing videos (face swap, upscaling) by submitting video URLs to PiAPI and polling for processed results. Uses frame-by-frame processing with temporal consistency to maintain coherence across video frames. Returns processed video URLs with metadata about processing time and output format.","intents":["Upscale low-resolution video footage to 4K or higher resolution","Apply face swap effects across entire video sequences","Enhance video quality for archival or restoration purposes"],"best_for":["Video editors automating enhancement workflows","Content creators improving video quality at scale","Developers building video processing pipelines"],"limitations":["Video processing is significantly slower than image processing — 5-30 minute wait times typical","Face swap across video frames may have temporal inconsistencies or flickering","Upscaling quality degrades with video length and complexity","Output video format and codec are fixed by PiAPI — no customization options","Very long videos (>30 minutes) may timeout or require chunking"],"requires":["Node.js 18+","PiAPI API key with video manipulation tier","MCP-compatible client","Video URLs (must be publicly accessible HTTPS)"],"input_types":["video URLs (HTTPS links to MP4, WebM, or MOV files)","structured parameters (upscale factor, face swap target)"],"output_types":["processed video URLs","video metadata (resolution, duration, codec, file size)"],"categories":["tool-use-integration","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-piapi__cap_5","uri":"capability://tool.use.integration.3d.model.generation.from.text.and.images","name":"3d model generation from text and images","description":"Generates 3D models (in GLB or OBJ format) from text descriptions or reference images using the Trellis model via PiAPI. Submits prompts or image URLs and polls for completion, returning downloadable 3D model files. Supports both text-to-3D and image-to-3D workflows with configurable mesh density and texture quality.","intents":["Generate 3D assets for games or AR applications from text descriptions","Convert 2D product images into 3D models for e-commerce or visualization","Rapidly prototype 3D designs without manual modeling"],"best_for":["Game developers needing rapid asset generation","3D designers automating model creation workflows","E-commerce platforms generating 3D product views"],"limitations":["Generated 3D models may have topology issues or non-manifold geometry requiring cleanup","Texture quality is limited — models often need manual texture refinement","Complex or detailed objects may fail to generate or produce low-quality results","Model size and polygon count are fixed by Trellis — no customization","Generation time is long (5-15 minutes typical) due to iterative mesh refinement"],"requires":["Node.js 18+","PiAPI API key with 3D generation tier","MCP-compatible client","3D model viewer or game engine to use generated assets (Blender, Unity, Unreal, etc.)"],"input_types":["text (3D object descriptions)","image URLs (reference images for image-to-3D)","structured parameters (mesh density, texture quality)"],"output_types":["3D model URLs (GLB or OBJ format)","model metadata (polygon count, texture resolution, file size)"],"categories":["tool-use-integration","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-piapi__cap_6","uri":"capability://automation.workflow.asynchronous.task.polling.and.status.tracking","name":"asynchronous task polling and status tracking","description":"Implements a polling-based task management system where clients submit generation requests and receive task IDs, then poll for completion status until results are ready. Uses exponential backoff and configurable timeout logic to avoid overwhelming the PiAPI backend. Tracks task state (pending, processing, completed, failed) and returns results or error messages based on final status.","intents":["Monitor long-running generation tasks without blocking the client","Implement timeout and retry logic for failed generation requests","Build progress indicators or status dashboards for ongoing tasks"],"best_for":["Developers building interactive AI applications with long-running tasks","Teams needing robust error handling for generation failures","Applications requiring task status visibility and progress tracking"],"limitations":["Polling adds latency compared to webhook-based notifications — typical 5-30 second polling intervals","No built-in persistence — task state is lost if the MCP server restarts","Timeout and retry logic must be configured per-client — no global defaults","Failed tasks provide limited debugging information — error messages are often generic"],"requires":["Node.js 18+","MCP-compatible client with polling capability","Network connectivity to PiAPI service","Configured timeout and retry parameters (see Configuration)"],"input_types":["task IDs (returned from initial generation request)","structured polling parameters (interval, max retries, timeout)"],"output_types":["task status (pending, processing, completed, failed)","result URLs (when completed)","error messages (when failed)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-piapi__cap_7","uri":"capability://tool.use.integration.tool.registry.system.with.dynamic.configuration","name":"tool registry system with dynamic configuration","description":"Manages a registry of 15+ AI generation tools organized by category (image, video, audio, 3D) with model-specific configuration objects (FLUX_MODEL_CONFIG, KLING_MODEL_CONFIG, etc.). Tools are dynamically loaded from configuration files and exposed as MCP tools with schema validation. Supports enabling/disabling tools and switching between models without code changes through environment variables or config files.","intents":["Enable or disable specific generation models based on subscription tier or regional availability","Switch between different models (e.g., Midjourney to Flux) without modifying client code","Add new generation models to the registry without rebuilding the MCP server"],"best_for":["Operators managing multi-tenant MCP servers with varying model availability","Teams deploying PiAPI MCP across different regions with region-specific models","Developers extending the MCP server with new generation models"],"limitations":["Configuration changes require server restart — no hot-reload of tool registry","Tool schema validation is performed at startup — invalid configs fail silently until first use","No built-in versioning of tool schemas — breaking changes to model APIs require manual updates","Tool discovery is static — clients cannot query available models at runtime"],"requires":["Node.js 18+","Configuration files (JSON or environment variables) defining tool registry","MCP-compatible client that supports dynamic tool discovery"],"input_types":["configuration objects (tool definitions, model configs, parameter schemas)","environment variables (for overriding config at runtime)"],"output_types":["MCP tool schemas (JSON schema format)","tool availability status (enabled/disabled)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-piapi__cap_8","uri":"capability://tool.use.integration.mcp.protocol.integration.and.schema.based.function.calling","name":"mcp protocol integration and schema-based function calling","description":"Implements the Model Context Protocol (MCP) server specification, exposing all generation tools as MCP tools with JSON schema definitions for parameters and outputs. Handles MCP request/response serialization, tool invocation, and error handling. Integrates with MCP-compatible clients (Claude Desktop, Cursor IDE) through stdio transport or network sockets, enabling seamless tool calling from AI assistants.","intents":["Call image/video/audio generation tools directly from Claude without leaving the chat interface","Use generation tools in multi-step workflows that combine text analysis and media creation","Integrate PiAPI generation capabilities into custom MCP clients or applications"],"best_for":["Claude Desktop and Cursor IDE users wanting native generation tool access","Developers building custom MCP clients that need media generation","Teams standardizing on MCP for AI tool integration"],"limitations":["MCP protocol overhead adds ~50-100ms latency per tool call compared to direct API calls","Tool schema validation is strict — invalid parameters are rejected before reaching PiAPI","No streaming of tool results — entire result must be buffered before returning to client","MCP transport (stdio) has limited throughput — large result payloads may be slow"],"requires":["Node.js 18+","MCP-compatible client (Claude Desktop 0.4+, Cursor IDE, or custom client)","stdio or network socket transport configured","PiAPI API key configured in environment"],"input_types":["MCP tool call requests (JSON-RPC format)","tool parameters matching JSON schema definitions"],"output_types":["MCP tool results (JSON-RPC responses)","tool output URLs and metadata"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-piapi__cap_9","uri":"capability://automation.workflow.piapi.backend.communication.with.error.handling.and.retry.logic","name":"piapi backend communication with error handling and retry logic","description":"Manages HTTP communication with the PiAPI backend service, handling request serialization, response parsing, and error recovery. Implements timeout and retry logic with exponential backoff for transient failures (network timeouts, rate limits). Translates PiAPI error responses into MCP-compatible error messages. Supports both synchronous requests (tool registration) and asynchronous task polling.","intents":["Reliably communicate with PiAPI backend despite network instability or temporary outages","Provide meaningful error messages to clients when generation fails","Implement rate limiting and backoff to avoid overwhelming the PiAPI service"],"best_for":["Production deployments requiring high availability and fault tolerance","Teams operating PiAPI MCP in unreliable network environments","Developers debugging generation failures and API errors"],"limitations":["Retry logic adds latency for transient failures — typical 5-30 second retry delays","Exponential backoff can cause cascading delays if multiple requests fail simultaneously","Error messages from PiAPI are often opaque — debugging requires PiAPI logs","No circuit breaker pattern — server will continue retrying even if PiAPI is down for extended periods","Timeout values are fixed — no per-request customization"],"requires":["Node.js 18+","Network connectivity to PiAPI backend (https://api.piapi.ai or configured endpoint)","Valid PiAPI API key with active subscription","Configured timeout and retry parameters"],"input_types":["generation requests (image, video, audio, 3D prompts)","task status polling requests"],"output_types":["task IDs (for asynchronous requests)","result URLs (when tasks complete)","error messages (on failure)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","PiAPI API key with active subscription","MCP-compatible client (Claude Desktop, Cursor IDE, or custom MCP client)","Network connectivity to PiAPI service","PiAPI API key with video generation tier","MCP-compatible client","Sufficient PiAPI credits for video generation (higher cost than images)","Valid result URLs from PiAPI","Docker 20.10+ or Docker Desktop","docker-compose 1.29+ (for multi-container deployments)"],"failure_modes":["Asynchronous polling adds latency — typical generation takes 30-120 seconds depending on model","No streaming of generation progress — clients must poll until task completion","Image quality and style consistency varies significantly between Midjourney, Flux, and Hunyuan models","Rate limiting depends on underlying PiAPI service quotas, not configurable per-client","Video generation is slower than image generation — typical 2-10 minute wait times","Model availability varies by region and PiAPI subscription tier","Output video quality and duration limits differ per model (e.g., Kling max 10 seconds, Luma max 2 minutes)","No real-time preview or iterative refinement — must regenerate entire video for changes","URL validation only checks format — doesn't verify file accessibility (would add latency)","Metadata extraction is limited to what PiAPI returns — no deep inspection of assets","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.5,"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":"2026-06-17T09:51:04.047Z","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=piapi","compare_url":"https://unfragile.ai/compare?artifact=piapi"}},"signature":"0fua2AwkVGERiGCwJA9Vhvnt8JF188KJH96QmiE5pnz1mz/du9kbwi4xm5cq5dsN9l2fT81pOV9ECJAXY2R2DQ==","signedAt":"2026-06-20T18:41:14.066Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/piapi","artifact":"https://unfragile.ai/piapi","verify":"https://unfragile.ai/api/v1/verify?slug=piapi","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"}}