{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-fulcra-context","slug":"fulcra-context","name":"Fulcra Context","type":"mcp","url":"https://github.com/fulcradynamics/fulcra-context-mcp","page_url":"https://unfragile.ai/fulcra-context","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-fulcra-context__cap_0","uri":"capability://tool.use.integration.personal.health.data.retrieval.via.mcp","name":"personal-health-data-retrieval-via-mcp","description":"Exposes personal health metrics (heart rate, blood pressure, glucose levels, etc.) through the Model Context Protocol as structured data resources. Implements MCP resource handlers that query the underlying Fulcra Context health database and serialize results into JSON-formatted responses, enabling LLM agents and tools to access real-time or historical health data without direct database access.","intents":["I want my AI assistant to know my current health metrics and provide personalized health advice","I need to build an agent that correlates my health data with my schedule and activities","I want to query my health history programmatically through a standardized protocol"],"best_for":["developers building health-aware AI agents","teams integrating personal health data into LLM workflows","individuals wanting privacy-first health data access for AI applications"],"limitations":["Data freshness depends on Fulcra Context sync frequency — may have latency of minutes to hours","No built-in aggregation or statistical analysis — returns raw data points only","Health data format and available fields are constrained by Fulcra Context schema"],"requires":["Fulcra Context application installed and configured on local system","MCP client implementation (e.g., Claude Desktop, custom MCP host)","Health data already synced to Fulcra Context from wearables or manual entry"],"input_types":["MCP resource requests (URI-based queries)","optional time range filters or metric type selectors"],"output_types":["JSON-structured health metrics","timestamped data points with units and confidence scores"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fulcra-context__cap_1","uri":"capability://tool.use.integration.workout.activity.logging.and.retrieval","name":"workout-activity-logging-and-retrieval","description":"Enables querying and retrieving workout and exercise activity logs stored in Fulcra Context through MCP resource endpoints. Parses structured workout data (exercise type, duration, intensity, calories burned, etc.) and exposes it as queryable resources that LLM agents can access to understand user fitness patterns, provide workout recommendations, or correlate exercise with other health metrics.","intents":["I want my AI coach to know what workouts I've done and suggest appropriate next sessions","I need to analyze my exercise patterns over time to optimize my training","I want to correlate my workout intensity with my sleep quality and recovery metrics"],"best_for":["fitness app developers integrating AI coaching","personal trainers building AI-assisted training plans","health-conscious users wanting AI-driven workout optimization"],"limitations":["Workout classification depends on Fulcra Context's activity detection — may misclassify ambiguous activities","No real-time streaming of in-progress workouts — only completed activity logs","Limited to workout types and metrics supported by Fulcra Context schema"],"requires":["Fulcra Context with workout tracking enabled","Connected fitness wearable or manual workout logging","MCP client with resource query capability"],"input_types":["MCP resource URIs with optional date range or activity type filters"],"output_types":["JSON workout summaries with metadata (type, duration, intensity, calories)","timestamped activity logs with geolocation if available"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fulcra-context__cap_2","uri":"capability://tool.use.integration.sleep.quality.and.recovery.data.access","name":"sleep-quality-and-recovery-data-access","description":"Provides MCP resource endpoints for querying sleep metrics (duration, quality score, REM/deep sleep percentages, sleep stages, disturbances) from Fulcra Context. Implements structured data handlers that serialize sleep session data into queryable resources, enabling LLM agents to assess recovery status, correlate sleep with performance, and provide sleep-based recommendations.","intents":["I want my AI assistant to understand my sleep patterns and suggest recovery strategies","I need to correlate my sleep quality with my workout performance and stress levels","I want an AI agent to alert me if my sleep metrics indicate overtraining or burnout risk"],"best_for":["athletes and fitness enthusiasts building AI recovery coaches","sleep researchers integrating personal sleep data into analysis workflows","wellness app developers adding AI-driven sleep insights"],"limitations":["Sleep stage detection accuracy depends on wearable sensor quality — may have ±15% variance","Historical sleep data availability limited to what Fulcra Context has synced","No real-time sleep stage streaming — only post-sleep analysis"],"requires":["Fulcra Context with sleep tracking enabled","Compatible sleep-tracking wearable (smartwatch, sleep tracker, etc.)","MCP client implementation"],"input_types":["MCP resource requests for sleep data","optional date range or metric type filters"],"output_types":["JSON sleep session summaries with duration, quality, stage breakdown","timestamped sleep metrics with confidence intervals"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fulcra-context__cap_3","uri":"capability://tool.use.integration.location.and.geospatial.context.retrieval","name":"location-and-geospatial-context-retrieval","description":"Exposes location history and geospatial context from Fulcra Context through MCP resources, including current location, location history with timestamps, and place categories (home, work, gym, etc.). Implements location data handlers that serialize geographic coordinates and metadata into queryable resources, enabling LLM agents to understand user context, provide location-aware recommendations, and correlate activities with places.","intents":["I want my AI assistant to know where I am and provide context-aware suggestions","I need to analyze my location patterns to understand my routine and optimize my schedule","I want an AI agent to correlate my location history with my activities and health metrics"],"best_for":["location-aware AI assistant developers","personal analytics builders tracking routine patterns","teams building context-aware mobile AI agents"],"limitations":["Location accuracy depends on GPS/network signal — may have 5-50m variance in urban areas","Location history retention limited by Fulcra Context storage and privacy settings","Place classification (home, work, etc.) requires manual configuration or inference"],"requires":["Fulcra Context with location tracking enabled","Device with GPS or network-based location services","MCP client with resource query support"],"input_types":["MCP resource URIs for location data","optional time range or location type filters"],"output_types":["JSON location objects with coordinates, timestamps, and place metadata","location history timelines with inferred activities"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fulcra-context__cap_4","uri":"capability://tool.use.integration.mcp.resource.schema.definition.and.discovery","name":"mcp-resource-schema-definition-and-discovery","description":"Implements MCP resource schema definitions that describe available health, workout, sleep, and location data resources with their query parameters, response formats, and metadata. Provides resource discovery endpoints that allow MCP clients to introspect available capabilities, understand data structures, and construct valid queries without hardcoding resource URIs or formats.","intents":["I want to discover what health and activity data is available through this MCP server","I need to understand the schema and query parameters for accessing specific data types","I want to build a dynamic MCP client that adapts to available resources"],"best_for":["MCP client developers integrating Fulcra Context","teams building generic MCP resource browsers","developers creating dynamic LLM prompts based on available data"],"limitations":["Schema discovery is static — doesn't reflect runtime data availability or user permissions","Resource URIs must follow Fulcra Context naming conventions","No schema validation — clients must implement their own validation logic"],"requires":["MCP client with resource discovery support","Fulcra Context MCP server running and accessible"],"input_types":["MCP resource list/describe requests"],"output_types":["JSON schema definitions for resources","resource metadata with query parameters and response formats"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fulcra-context__cap_5","uri":"capability://automation.workflow.local.mcp.server.lifecycle.management","name":"local-mcp-server-lifecycle-management","description":"Manages the MCP server process lifecycle including startup, shutdown, and connection handling for the Fulcra Context MCP bridge. Implements server initialization that connects to the local Fulcra Context application, handles authentication/authorization, and manages resource handlers for each data type. Provides graceful shutdown and error recovery to ensure reliable operation in MCP client environments.","intents":["I want to run the Fulcra Context MCP server as a local service for my AI tools","I need the MCP server to automatically connect to Fulcra Context on startup","I want reliable error handling and recovery if the connection to Fulcra Context is lost"],"best_for":["developers deploying Fulcra Context MCP in production environments","teams integrating MCP servers into Claude Desktop or other MCP hosts","users wanting automated health data access through AI assistants"],"limitations":["Server lifecycle depends on Fulcra Context application being running — no fallback if Fulcra Context crashes","No built-in clustering or high-availability — single-instance only","Connection pooling and resource limits not explicitly documented"],"requires":["Fulcra Context application installed and running on the same system","Node.js or Python runtime (depending on MCP server implementation)","MCP client or host application (e.g., Claude Desktop)"],"input_types":["server startup configuration","connection parameters for Fulcra Context"],"output_types":["server status and health metrics","connection logs and error messages"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fulcra-context__cap_6","uri":"capability://data.processing.analysis.multi.metric.correlation.and.context.aggregation","name":"multi-metric-correlation-and-context-aggregation","description":"Enables LLM agents to query and correlate multiple data types (health, workout, sleep, location) through a unified MCP interface, aggregating related metrics into contextual summaries. Implements resource handlers that can join data across different Fulcra Context domains (e.g., correlating workout intensity with sleep quality, or location with activity type) to provide holistic health context to AI agents.","intents":["I want my AI coach to understand how my workouts affect my sleep and recovery","I need to correlate my location patterns with my activity levels and health metrics","I want an AI agent to provide recommendations based on integrated health and lifestyle data"],"best_for":["health and wellness app developers building AI coaches","personal analytics platforms integrating multiple data sources","researchers analyzing relationships between lifestyle factors"],"limitations":["Correlation logic is limited to what LLM agents can infer — no built-in statistical analysis","Time alignment between different data types may have gaps or mismatches","No pre-computed correlations — each query requires real-time data aggregation"],"requires":["Access to multiple Fulcra Context data types (health, workout, sleep, location)","MCP client capable of making multiple resource queries","LLM with sufficient context window to process aggregated data"],"input_types":["MCP resource queries for multiple data types","optional correlation parameters or time ranges"],"output_types":["aggregated JSON objects with correlated metrics","contextual summaries linking multiple data types"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fulcra-context__cap_7","uri":"capability://safety.moderation.privacy.preserving.local.data.access.without.cloud.sync","name":"privacy-preserving-local-data-access-without-cloud-sync","description":"Implements a privacy-first architecture where all personal data (health, workouts, sleep, location) remains on the local system and is accessed through MCP without any cloud transmission or external API calls. Uses local resource handlers that query Fulcra Context's local database directly, ensuring sensitive biometric and location data never leaves the device while still enabling AI agent integration.","intents":["I want to use AI assistants with my personal health data without sending it to the cloud","I need to ensure my location history and biometric data stay private and local","I want to build AI agents that respect my privacy while providing personalized insights"],"best_for":["privacy-conscious individuals using AI assistants","healthcare organizations building compliant AI tools","teams building HIPAA or GDPR-compliant AI applications"],"limitations":["AI capabilities limited to local LLM models — cannot use cloud-based LLMs without additional privacy measures","No cloud backup or sync — data loss if local system fails","Requires users to manage their own data storage and security"],"requires":["Fulcra Context application with local data storage","Local MCP client or host (e.g., Claude Desktop)","optional: local LLM model for fully offline operation"],"input_types":["MCP resource requests from local clients only"],"output_types":["JSON data from local Fulcra Context database"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Fulcra Context application installed and configured on local system","MCP client implementation (e.g., Claude Desktop, custom MCP host)","Health data already synced to Fulcra Context from wearables or manual entry","Fulcra Context with workout tracking enabled","Connected fitness wearable or manual workout logging","MCP client with resource query capability","Fulcra Context with sleep tracking enabled","Compatible sleep-tracking wearable (smartwatch, sleep tracker, etc.)","MCP client implementation","Fulcra Context with location tracking enabled"],"failure_modes":["Data freshness depends on Fulcra Context sync frequency — may have latency of minutes to hours","No built-in aggregation or statistical analysis — returns raw data points only","Health data format and available fields are constrained by Fulcra Context schema","Workout classification depends on Fulcra Context's activity detection — may misclassify ambiguous activities","No real-time streaming of in-progress workouts — only completed activity logs","Limited to workout types and metrics supported by Fulcra Context schema","Sleep stage detection accuracy depends on wearable sensor quality — may have ±15% variance","Historical sleep data availability limited to what Fulcra Context has synced","No real-time sleep stage streaming — only post-sleep analysis","Location accuracy depends on GPS/network signal — may have 5-50m variance in urban areas","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"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":"2026-06-17T09:51:03.040Z","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=fulcra-context","compare_url":"https://unfragile.ai/compare?artifact=fulcra-context"}},"signature":"SNCCpumBkoXlGhpH6uEyxJ3QdfrQQo9lXiMmgcVQEHTolP+jUUlbymPeinchtX1wlwPohtvCGdsAV3trV3oaDg==","signedAt":"2026-06-20T23:27:13.332Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/fulcra-context","artifact":"https://unfragile.ai/fulcra-context","verify":"https://unfragile.ai/api/v1/verify?slug=fulcra-context","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"}}