{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_akilat-spec-t-t-leave-manager-mcp","slug":"akilat-spec-t-t-leave-manager-mcp","name":"t-t-leave-manager-mcp","type":"mcp","url":"https://github.com/akilat-spec/t-t-leave-manager-mcp","page_url":"https://unfragile.ai/akilat-spec-t-t-leave-manager-mcp","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:akilat-spec/t-t-leave-manager-mcp"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_akilat-spec-t-t-leave-manager-mcp__cap_0","uri":"capability://tool.use.integration.leave.request.submission.via.mcp","name":"leave-request-submission-via-mcp","description":"Enables AI agents and LLM-powered applications to submit leave requests through a standardized MCP tool interface that abstracts the underlying leave management system. The capability implements request validation, payload formatting, and error handling within the MCP protocol layer, allowing clients to submit structured leave data (dates, type, reason) without direct system access.","intents":["I want my AI agent to submit leave requests on behalf of users without exposing backend APIs","I need to integrate leave management into an LLM workflow while maintaining security boundaries","I want to standardize leave submission across multiple backend systems through a single MCP interface"],"best_for":["teams building AI agents that manage HR workflows","enterprises integrating leave systems with LLM-powered assistants","developers creating multi-tenant HR automation platforms"],"limitations":["MCP protocol adds request/response serialization overhead (~50-100ms per submission)","No built-in request queuing or retry logic — failures require client-side handling","Synchronous submission only — no async job tracking for long-running approvals","Limited to leave types and date formats supported by the underlying backend system"],"requires":["MCP client implementation (Claude Desktop, custom MCP host, or compatible LLM framework)","Access to underlying leave management system (API credentials or direct database connection)","Node.js runtime for the MCP server process"],"input_types":["structured JSON with leave request fields (start_date, end_date, leave_type, reason)","user/employee identifier (email, ID, or username)"],"output_types":["JSON response with request status (submitted, pending, approved, rejected)","request ID for tracking","error messages with validation details"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_akilat-spec-t-t-leave-manager-mcp__cap_1","uri":"capability://tool.use.integration.leave.balance.query.with.context.passing","name":"leave-balance-query-with-context-passing","description":"Provides MCP tools that allow AI agents to query employee leave balances and historical leave data, with results automatically injected into the LLM context window. The implementation uses MCP's context-passing mechanism to make leave data available to the model without requiring separate API calls or manual context management by the client.","intents":["I want my AI assistant to check leave balances before submitting requests","I need the LLM to have real-time visibility into employee leave history for decision-making","I want to prevent leave request submissions that would exceed available balance"],"best_for":["AI agents that need to validate leave requests against current balances","LLM-powered HR chatbots providing leave information to employees","automated leave approval workflows that require balance verification"],"limitations":["Query results are point-in-time snapshots — no real-time sync if balances change during agent execution","Context window injection may consume significant tokens for large historical datasets","No caching layer — each query hits the backend system, increasing latency for repeated queries","Requires the underlying system to expose balance and history APIs"],"requires":["MCP client with context-passing support","Employee identifier (email, ID, or username) to query","Backend leave management system with queryable balance/history endpoints"],"input_types":["employee identifier (string)","optional date range filters (start_date, end_date)","optional leave type filter (string)"],"output_types":["JSON object with current leave balances by type (annual, sick, personal, etc.)","historical leave records with dates, types, and approval status","formatted text summary for LLM consumption"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_akilat-spec-t-t-leave-manager-mcp__cap_2","uri":"capability://tool.use.integration.leave.request.approval.workflow.orchestration","name":"leave-request-approval-workflow-orchestration","description":"Orchestrates multi-step leave approval workflows through MCP tools, enabling AI agents to route requests to appropriate approvers, track approval status, and handle conditional logic based on leave type, duration, or employee role. The implementation abstracts approval routing rules and status tracking, allowing the LLM to manage complex workflows without direct access to backend approval systems.","intents":["I want my AI agent to route leave requests to the correct manager based on employee hierarchy","I need to automate approval workflows with conditional logic (e.g., requests >5 days require director approval)","I want the LLM to track approval status and notify stakeholders when requests are approved/rejected"],"best_for":["enterprises automating HR approval workflows with AI agents","organizations with complex approval hierarchies (multi-level managers, department-specific rules)","teams building AI-assisted leave management systems"],"limitations":["Workflow orchestration is synchronous — no built-in support for async approvals or timeout handling","Approval routing rules must be pre-configured in the backend; dynamic rule changes require system restart","No audit trail or approval history tracking within the MCP layer — depends on backend system","Limited to approval workflows supported by the underlying leave management system"],"requires":["MCP client with tool-calling support","Backend leave management system with approval workflow APIs","Employee hierarchy or manager mapping data in the backend system","Approval routing rules configured in the backend"],"input_types":["leave request ID (string)","employee ID (string)","leave type and duration (for conditional routing)","optional approver override (string)"],"output_types":["workflow status (submitted, pending_approval, approved, rejected)","current approver information (name, email, role)","approval history with timestamps and comments","next steps or required actions"],"categories":["tool-use-integration","planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_akilat-spec-t-t-leave-manager-mcp__cap_3","uri":"capability://tool.use.integration.leave.type.and.policy.information.retrieval","name":"leave-type-and-policy-information-retrieval","description":"Exposes leave types, accrual policies, and eligibility rules through MCP tools, enabling AI agents to understand organizational leave policies and provide accurate information to employees. The implementation queries the backend leave system for policy metadata and formats it for LLM consumption, allowing the agent to answer policy questions and validate requests against rules.","intents":["I want my AI assistant to explain leave policies to employees without manual documentation","I need the LLM to validate leave requests against accrual policies and eligibility rules","I want to provide employees with accurate information about leave types and balances"],"best_for":["HR chatbots answering employee questions about leave policies","AI agents validating leave requests against organizational rules","self-service leave management portals powered by LLMs"],"limitations":["Policy information is static snapshots — changes to policies require backend updates and MCP server restart","No versioning or historical policy tracking — cannot retrieve policies as of a past date","Policy complexity may exceed LLM reasoning capabilities (e.g., complex accrual formulas, conditional eligibility)","Depends on backend system exposing policy metadata in a structured format"],"requires":["MCP client with tool-calling support","Backend leave management system with policy/metadata APIs","Leave policy data structured in the backend system"],"input_types":["optional leave type filter (string)","optional employee role or department filter (string)"],"output_types":["list of leave types with descriptions","accrual policies (e.g., annual accrual rate, carryover rules)","eligibility rules (e.g., minimum tenure, role-based restrictions)","formatted policy summary for LLM consumption"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_akilat-spec-t-t-leave-manager-mcp__cap_4","uri":"capability://tool.use.integration.employee.leave.history.analysis.and.reporting","name":"employee-leave-history-analysis-and-reporting","description":"Provides MCP tools to retrieve and analyze employee leave history, enabling AI agents to generate reports, identify patterns, and support decision-making. The implementation queries historical leave data from the backend and formats it for analysis, allowing the LLM to answer questions about leave usage trends, compliance, and employee behavior.","intents":["I want my AI agent to generate leave usage reports for managers","I need to identify patterns in leave requests (e.g., frequent Friday absences) for compliance review","I want the LLM to provide insights into team leave trends and resource planning"],"best_for":["HR analytics and reporting workflows powered by AI agents","managers reviewing team leave patterns and planning coverage","compliance and audit workflows requiring leave history analysis"],"limitations":["Analysis is limited to data available in the backend system — no external data integration","Large historical datasets may exceed context window limits, requiring pagination or summarization","No built-in statistical analysis or ML-based pattern detection — depends on LLM reasoning","Privacy considerations: exposing detailed leave history requires careful access control"],"requires":["MCP client with tool-calling support","Backend leave management system with historical leave data","Employee identifier or team/department filter for scoping queries"],"input_types":["employee ID or team/department filter (string)","date range for historical analysis (start_date, end_date)","optional leave type filter (string)"],"output_types":["historical leave records with dates, types, and durations","aggregated statistics (total days used, frequency by type)","formatted report or summary for LLM analysis","trend data for visualization"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_akilat-spec-t-t-leave-manager-mcp__cap_5","uri":"capability://tool.use.integration.mcp.protocol.abstraction.for.leave.systems","name":"mcp-protocol-abstraction-for-leave-systems","description":"Implements the MCP (Model Context Protocol) server interface to abstract a leave management system, translating between MCP tool calls and backend leave APIs. The implementation handles protocol serialization, error mapping, and context injection, allowing any MCP-compatible client (Claude, custom agents, etc.) to interact with the leave system without understanding its internal APIs.","intents":["I want to expose my leave management system to Claude and other LLM applications","I need a standardized interface for AI agents to interact with leave data","I want to avoid building custom REST APIs for each new LLM integration"],"best_for":["enterprises integrating existing leave systems with LLM applications","teams building multi-client LLM integrations (Claude, custom agents, etc.)","organizations standardizing on MCP for AI system integrations"],"limitations":["MCP protocol overhead adds latency (~50-100ms per tool call) compared to direct API access","Requires MCP client implementation — not all LLM platforms support MCP natively","Error handling is limited to MCP protocol semantics — backend-specific errors must be mapped","No built-in authentication — relies on MCP client authentication mechanisms"],"requires":["Node.js runtime for the MCP server","Backend leave management system with accessible APIs","MCP client implementation (Claude Desktop, custom MCP host, etc.)"],"input_types":["MCP tool call requests with structured parameters"],"output_types":["MCP tool call responses with structured results","error responses with diagnostic information"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_akilat-spec-t-t-leave-manager-mcp__cap_6","uri":"capability://tool.use.integration.leave.type.and.policy.configuration.via.mcp","name":"leave-type-and-policy-configuration-via-mcp","description":"Provides MCP tools for querying and managing leave type definitions and associated policies (e.g., accrual rates, carryover limits, approval requirements). The server exposes configuration data that agents can use to understand leave policies before processing requests, including leave type names, descriptions, maximum days per year, and approval workflows.","intents":["I want my agent to understand leave policies (e.g., max 20 vacation days per year) before approving requests","I need to retrieve leave type definitions to validate that a request uses a valid leave type","I want to surface policy information to employees in a chatbot so they understand leave rules"],"best_for":["AI agents implementing policy-aware leave approval logic","HR chatbots providing employees with self-service policy information","organizations with complex leave policies needing centralized configuration"],"limitations":["Configuration is read-only via MCP tools; policy updates require backend system changes","No versioning of policies; agents cannot reason about historical policy changes","Policy enforcement is not built into the MCP server — agents must implement business logic","No support for conditional policies (e.g., different limits for different departments)"],"requires":["backend system with leave type and policy definitions","MCP server with access to policy configuration data"],"input_types":["optional leave_type filter to retrieve specific policy"],"output_types":["structured JSON with leave type definitions: {name, description, max_days_per_year, carryover_limit, requires_approval, ...}","error response if leave type not found"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_akilat-spec-t-t-leave-manager-mcp__cap_7","uri":"capability://tool.use.integration.error.handling.and.fallback.responses.in.mcp.tools","name":"error-handling-and-fallback-responses-in-mcp-tools","description":"Implements comprehensive error handling for MCP tool invocations, returning structured error responses with error codes, messages, and recovery suggestions when operations fail. The server handles backend system failures, validation errors, and edge cases (e.g., employee not found, request already processed) with graceful degradation and clear error messages that help agents understand what went wrong and how to recover.","intents":["I want my agent to handle errors gracefully when the leave management backend is unavailable","I need clear error messages to help the agent understand why a request failed and retry appropriately","I want to prevent agents from getting stuck in error loops due to malformed requests"],"best_for":["production AI agents that need robust error handling","systems integrating with unreliable or legacy backend systems","teams building agent workflows with retry logic and fallbacks"],"limitations":["Error handling adds complexity and latency to tool invocations","Retry logic must be implemented in the agent or client; MCP server does not handle retries","No built-in circuit breaker or rate limiting — repeated failures will continue to be attempted","Error messages are limited to text; no structured error codes for programmatic handling"],"requires":["MCP server with error handling middleware","backend system that returns meaningful error responses","agent implementation with retry and fallback logic"],"input_types":["any MCP tool request that may fail"],"output_types":["structured error response: {error_code, message, details, recovery_suggestion}","HTTP status codes (if using HTTP transport) indicating error type"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["MCP client implementation (Claude Desktop, custom MCP host, or compatible LLM framework)","Access to underlying leave management system (API credentials or direct database connection)","Node.js runtime for the MCP server process","MCP client with context-passing support","Employee identifier (email, ID, or username) to query","Backend leave management system with queryable balance/history endpoints","MCP client with tool-calling support","Backend leave management system with approval workflow APIs","Employee hierarchy or manager mapping data in the backend system","Approval routing rules configured in the backend"],"failure_modes":["MCP protocol adds request/response serialization overhead (~50-100ms per submission)","No built-in request queuing or retry logic — failures require client-side handling","Synchronous submission only — no async job tracking for long-running approvals","Limited to leave types and date formats supported by the underlying backend system","Query results are point-in-time snapshots — no real-time sync if balances change during agent execution","Context window injection may consume significant tokens for large historical datasets","No caching layer — each query hits the backend system, increasing latency for repeated queries","Requires the underlying system to expose balance and history APIs","Workflow orchestration is synchronous — no built-in support for async approvals or timeout handling","Approval routing rules must be pre-configured in the backend; dynamic rule changes require system restart","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.48999999999999994,"match_graph":0.25,"freshness":0.5,"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":"2026-05-24T12:16:25.635Z","last_scraped_at":"2026-05-03T15:19:31.415Z","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=akilat-spec-t-t-leave-manager-mcp","compare_url":"https://unfragile.ai/compare?artifact=akilat-spec-t-t-leave-manager-mcp"}},"signature":"+/FJFPPl0wXnEWJSnKlUqsiRHROuGQB150ycq7pE/3EKi1KNsiePMi+rsB9swthDEhMiEFGKBEkfL9rR879pAQ==","signedAt":"2026-06-21T12:41:55.241Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/akilat-spec-t-t-leave-manager-mcp","artifact":"https://unfragile.ai/akilat-spec-t-t-leave-manager-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=akilat-spec-t-t-leave-manager-mcp","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"}}