TalentoHQ

Exposes TalentoHQ HR database entities (employees, departments, roles, compensation, performance data) through the Model Context Protocol, enabling LLM agents and AI tools to read and write HR records with standardized MCP resource handlers. 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.

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.

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.

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.

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.

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.

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.

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.

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.