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Uses MCP's resource URI scheme to map HR entities to queryable endpoints, allowing stateless, schema-validated access to organizational data without custom API wrappers.","intents":["Connect an AI agent to live HR data without building custom API integrations","Query employee records, org structure, and compensation data programmatically from LLM applications","Sync HR changes made through AI workflows back into TalentoHQ without manual data entry","Build multi-step HR automation workflows that read from and write to the HR system"],"best_for":["HR teams building AI-powered recruitment and onboarding workflows","Developers integrating TalentoHQ with LLM agents and autonomous systems","Organizations automating employee data management and reporting through AI"],"limitations":["MCP protocol is request-response only — no real-time event streaming or webhooks for HR changes","Requires MCP client implementation; not directly accessible via REST or GraphQL","Unknown transaction support for multi-entity HR operations (e.g., atomic employee + role + compensation updates)","No built-in data versioning or audit trail visibility through MCP interface"],"requires":["MCP-compatible client (Claude Desktop, custom MCP host, or LLM framework with MCP support)","Valid TalentoHQ account with API credentials","Network access to hr.talentohq.com/mcp endpoint","Understanding of MCP resource URI syntax and request/response patterns"],"input_types":["MCP resource requests (JSON-RPC 2.0 format)","HR entity identifiers (employee IDs, department codes)","Query filters and pagination parameters"],"output_types":["Structured HR data (JSON)","Employee records with nested relationships","Confirmation responses for write operations"],"categories":["tool-use-integration","hr-systems"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-talentohq__cap_1","uri":"capability://automation.workflow.employee.record.crud.operations.through.ai.agents","name":"employee record crud operations through ai agents","description":"Enables LLM agents to create, read, update, and delete employee records in TalentoHQ via MCP handlers that map CRUD operations to HR data mutations. Agents can parse natural language HR requests (e.g., 'add a new engineer named Alice'), validate against HR schema constraints (required fields, data types, business rules), and execute changes with confirmation workflows to prevent accidental modifications.","intents":["Let an AI agent onboard new employees by creating records with all required fields","Query employee details (name, role, department, contact info) on demand from an LLM application","Update employee information (title, department, manager) through conversational AI","Deactivate or archive employee records through automated workflows"],"best_for":["HR teams using AI chatbots for employee self-service and admin tasks","Recruitment teams automating candidate-to-employee record creation","Organizations building conversational HR assistants"],"limitations":["No batch CRUD operations — each employee record change requires a separate MCP request","Unknown support for complex HR workflows (e.g., cascading updates when changing department)","Confirmation workflows may add latency for high-volume operations","No built-in conflict resolution for concurrent updates to the same employee record"],"requires":["MCP client with write permissions enabled","TalentoHQ account with employee management permissions","Knowledge of required employee fields and validation rules","Proper error handling for schema validation failures"],"input_types":["Natural language HR requests","Structured employee data (JSON)","Employee IDs for read/update/delete operations"],"output_types":["Created/updated employee records (JSON)","Confirmation messages","Validation error details"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-talentohq__cap_2","uri":"capability://search.retrieval.organizational.hierarchy.and.department.structure.querying","name":"organizational hierarchy and department structure querying","description":"Exposes TalentoHQ's organizational structure (departments, reporting lines, team hierarchies) through MCP resources, allowing AI agents to traverse and query the org chart programmatically. Agents can retrieve parent-child relationships, identify reporting managers, and understand team composition without manual data extraction, enabling context-aware HR decisions and recommendations.","intents":["Query the reporting structure to understand who reports to whom","Retrieve all employees in a specific department or team","Find the manager of a given employee","Analyze organizational hierarchy for restructuring or span-of-control recommendations"],"best_for":["HR teams building org chart visualization and analysis tools","Managers using AI assistants to understand team structure and reporting lines","Organizations automating org design and restructuring workflows"],"limitations":["Unknown support for historical org structure versions or change tracking","No real-time notifications when org structure changes","Query performance unknown for very large organizations (1000+ employees)","Likely read-only access; org structure changes may require separate admin workflows"],"requires":["MCP client with read access to org structure data","TalentoHQ account with visibility into organizational hierarchy","Understanding of department and employee relationship models"],"input_types":["Department IDs or names","Employee IDs","Hierarchy traversal parameters"],"output_types":["Organizational hierarchy (JSON tree structure)","Employee lists by department","Reporting relationships and manager assignments"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-talentohq__cap_3","uri":"capability://data.processing.analysis.compensation.and.benefits.data.access.for.ai.driven.analysis","name":"compensation and benefits data access for ai-driven analysis","description":"Exposes employee compensation, salary bands, benefits enrollment, and payroll-related data through MCP resources, enabling AI agents to analyze compensation equity, recommend salary adjustments, and provide benefits guidance. Data is accessed via schema-validated MCP handlers that enforce access controls and data sensitivity rules, ensuring sensitive payroll information is only retrieved by authorized agents.","intents":["Analyze salary equity across departments and roles using AI","Get compensation recommendations for new hires or promotions","Query benefits enrollment and coverage details for employees","Identify compensation outliers or anomalies for HR review"],"best_for":["HR teams using AI for compensation analysis and equity audits","Managers seeking AI-powered salary recommendation guidance","Organizations automating benefits administration and employee inquiries"],"limitations":["Sensitive payroll data access requires strict permission controls; unknown how granular these are","No built-in anonymization or aggregation for privacy-preserving analysis","Unknown support for historical compensation data or change tracking","Likely restricted to read-only access due to payroll sensitivity"],"requires":["MCP client with explicit compensation data access permissions","TalentoHQ account with payroll/compensation visibility","Compliance with data protection regulations (GDPR, CCPA, etc.)","Audit logging for all compensation data access"],"input_types":["Employee IDs","Department or role filters","Date ranges for historical analysis"],"output_types":["Compensation records (salary, bonus, equity)","Benefits enrollment data","Salary band and market rate information","Analysis results (equity reports, recommendations)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-talentohq__cap_4","uri":"capability://data.processing.analysis.performance.review.and.feedback.data.retrieval.for.ai.insights","name":"performance review and feedback data retrieval for ai insights","description":"Exposes performance review cycles, feedback submissions, ratings, and goal tracking data through MCP resources, enabling AI agents to analyze employee performance trends, generate insights, and provide recommendations. Agents can retrieve historical performance data, identify high performers, and flag performance concerns while respecting data sensitivity and access controls.","intents":["Retrieve performance review history for an employee","Analyze performance trends across teams or departments","Generate AI-powered performance insights and recommendations","Identify high performers or employees needing support"],"best_for":["Managers using AI assistants for performance analysis and feedback","HR teams automating performance review workflows and insights","Organizations building AI-powered talent development recommendations"],"limitations":["Unknown support for real-time feedback or continuous performance data","No built-in bias detection or fairness analysis for AI recommendations","Likely read-only access; performance data modifications may require separate workflows","Unknown how historical performance data is retained and versioned"],"requires":["MCP client with performance data access permissions","TalentoHQ account with performance management features enabled","Understanding of performance review cycles and rating scales","Compliance with employment law regarding performance data"],"input_types":["Employee IDs","Review cycle dates","Department or team filters"],"output_types":["Performance review records (ratings, feedback, goals)","Performance trend analysis","AI-generated insights and recommendations"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-talentohq__cap_5","uri":"capability://automation.workflow.recruitment.and.applicant.tracking.integration","name":"recruitment and applicant tracking integration","description":"Connects TalentoHQ's recruitment module to AI agents via MCP, enabling agents to query job openings, retrieve applicant information, update application status, and generate candidate recommendations. Agents can parse job descriptions, match candidates against requirements, and automate screening workflows while maintaining data consistency between recruitment and HR systems.","intents":["Query open job positions and their requirements","Retrieve applicant profiles and application history","Update application status (screening, interview, offer, hired)","Generate AI-powered candidate recommendations based on job fit"],"best_for":["Recruitment teams automating candidate screening and matching","HR teams building AI-powered recruitment assistants","Organizations streamlining hiring workflows with AI"],"limitations":["Unknown support for resume parsing or document analysis","No built-in bias detection for AI candidate recommendations","Likely limited to applicant data already in TalentoHQ; external candidate sources may require separate integration","Unknown how application history and status changes are tracked"],"requires":["MCP client with recruitment data access","TalentoHQ account with recruitment/ATS features enabled","Job descriptions and candidate profiles in TalentoHQ","Compliance with employment law regarding candidate data"],"input_types":["Job IDs or titles","Applicant IDs","Application status updates","Candidate filter criteria"],"output_types":["Job opening details and requirements","Applicant profiles and application history","Candidate recommendations and match scores","Status update confirmations"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-talentohq__cap_6","uri":"capability://automation.workflow.leave.and.time.off.management.automation","name":"leave and time-off management automation","description":"Exposes leave policies, time-off requests, and absence tracking through MCP resources, enabling AI agents to process leave requests, check availability, and manage time-off workflows. Agents can validate requests against policies, check team coverage, and automatically approve or flag requests for manager review based on configurable rules.","intents":["Submit and track time-off requests through an AI assistant","Check employee leave balance and accrual","Validate leave requests against company policies","Automate leave approval workflows based on team coverage"],"best_for":["HR teams automating leave request processing","Employees using AI assistants for self-service time-off management","Organizations streamlining absence tracking and coverage planning"],"limitations":["Unknown support for complex leave policies (carryover, accrual, different leave types)","No real-time team coverage analysis; may require separate scheduling system","Likely limited to standard leave types; custom leave categories may not be supported","Unknown how leave requests are escalated for manager approval"],"requires":["MCP client with leave management permissions","TalentoHQ account with leave/time-off features enabled","Configured leave policies and approval workflows","Integration with payroll system for accrual calculations"],"input_types":["Employee IDs","Leave type and dates","Reason for absence","Manager approval decisions"],"output_types":["Leave balance and accrual information","Leave request status and approval decisions","Absence calendar and team coverage data"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-talentohq__cap_7","uri":"capability://planning.reasoning.training.and.development.program.management","name":"training and development program management","description":"Exposes training catalogs, course enrollments, completion tracking, and learning paths through MCP resources, enabling AI agents to recommend training programs, track employee development, and manage learning workflows. Agents can match employees to relevant courses based on skills, roles, and career goals, and provide personalized development recommendations.","intents":["Recommend training courses based on employee role and skills","Enroll employees in training programs","Track training completion and certification status","Generate personalized learning paths for career development"],"best_for":["L&D teams automating training recommendations and enrollment","Managers using AI for employee development planning","Organizations building AI-powered learning platforms"],"limitations":["Unknown support for external training providers or SCORM content","No built-in skill assessment or competency mapping","Likely limited to training data in TalentoHQ; external learning platforms may require separate integration","Unknown how training effectiveness is measured or tracked"],"requires":["MCP client with training/learning data access","TalentoHQ account with L&D features enabled","Training catalog and course data in TalentoHQ","Employee skill and role information for recommendations"],"input_types":["Employee IDs","Role or skill filters","Training course IDs","Enrollment decisions"],"output_types":["Training recommendations and learning paths","Course enrollment confirmations","Training completion and certification status","Development progress reports"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-talentohq__cap_8","uri":"capability://safety.moderation.compliance.and.policy.management.through.ai","name":"compliance and policy management through ai","description":"Exposes company policies, compliance requirements, and policy acknowledgment tracking through MCP resources, enabling AI agents to answer policy questions, track employee acknowledgments, and flag compliance issues. Agents can retrieve policy documents, check acknowledgment status, and provide policy guidance to employees and managers.","intents":["Answer employee questions about company policies","Track policy acknowledgments and compliance status","Flag non-compliant behavior or policy violations","Provide policy guidance to managers and employees"],"best_for":["HR teams automating policy distribution and acknowledgment tracking","Employees using AI assistants for policy questions","Organizations ensuring compliance with company policies"],"limitations":["Unknown support for policy versioning and change tracking","No built-in legal review or compliance validation","Likely read-only access; policy updates may require separate admin workflows","Unknown how policy violations are tracked and escalated"],"requires":["MCP client with policy data access","TalentoHQ account with policy management features","Company policies documented in TalentoHQ","Compliance tracking and acknowledgment workflows"],"input_types":["Policy keywords or categories","Employee IDs for acknowledgment tracking","Policy update notifications"],"output_types":["Policy documents and summaries","Acknowledgment status and tracking","Compliance reports and violation flags"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["MCP-compatible client (Claude Desktop, custom MCP host, or LLM framework with MCP support)","Valid TalentoHQ account with API credentials","Network access to hr.talentohq.com/mcp endpoint","Understanding of MCP resource URI syntax and request/response patterns","MCP client with write permissions enabled","TalentoHQ account with employee management permissions","Knowledge of required employee fields and validation rules","Proper error handling for schema validation failures","MCP client with read access to org structure data","TalentoHQ account with visibility into organizational hierarchy"],"failure_modes":["MCP protocol is request-response only — no real-time event streaming or webhooks for HR changes","Requires MCP client implementation; not directly accessible via REST or GraphQL","Unknown transaction support for multi-entity HR operations (e.g., atomic employee + role + compensation updates)","No built-in data versioning or audit trail visibility through MCP interface","No batch CRUD operations — each employee record change requires a separate MCP request","Unknown support for complex HR workflows (e.g., cascading updates when changing department)","Confirmation workflows may add latency for high-volume operations","No built-in conflict resolution for concurrent updates to the same employee record","Unknown support for historical org structure versions or change tracking","No real-time notifications when org structure changes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"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":"2026-06-17T09:51:04.050Z","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=talentohq","compare_url":"https://unfragile.ai/compare?artifact=talentohq"}},"signature":"oQAjSwL+C00+PpQ47K+fznia8PZOD6e7lzMQhGCg9FMeabVF9139K0smPPC3cLM5TJImTtdhFuGPH5SFW1jRBA==","signedAt":"2026-06-20T20:07:49.952Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/talentohq","artifact":"https://unfragile.ai/talentohq","verify":"https://unfragile.ai/api/v1/verify?slug=talentohq","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"}}