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Finance & PaymentsAuto-generatedScore: 34

Apacta MCP Server

The Apacta API is a specialized, industry-focused digital backbone designed to serve the operational needs of tradespeople, contractors, and small-to-medium construction or service businesses.

Quick Start Summary

The Apacta MCP server is a Model Context Protocol bridge that connects AI assistants β€” including Claude Desktop, Cursor, Windsurf, and VS Code Copilot β€” to the Apacta API through natural language. It exposes 10 API endpoints as callable tools, such as Get a list of activities, Create an activity, Bulk delete activities, and more. No authentication is required β€” setup takes approximately 30 seconds. The server uses STDIO transport and can be installed by running npx -y @mcp/apacta-com. This integration is sourced from the auto Apacta OpenAPI specification (v0.0.42) and has a quality score of 34/99 (fair documentation coverage).

10Endpointstools mapped
NoneAuthopen access
34/99Qualityfair
~30 secSetupno auth

Server Details

Category
Finance & Payments
Authentication
None
Endpoints
10 operations
Transport
STDIO
Spec Version
v0.0.42
Install Command
npx -y @mcp/apacta-com

Environment Variables

APACTA_API_KEY

Example: your_apacta_api_key

Top Endpoints

GET
/activities

Get a list of activities

POST
/activities

Create an activity

DELETE
/activities/bulkDelete

Bulk delete activities

PUT
/activities/{activity_id}

Edit an activity

DELETE
/activities/{activity_id}

Delete an activity

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πŸ“– Detailed MCP Integration Guide

A technical breakdown of capabilities, agent workflows, and security/configuration best practices.

⚑Capabilities & Use Cases
The Apacta API is a specialized, industry-focused digital backbone designed to serve the operational needs of tradespeople, contractors, and small-to-medium construction or service businesses. Developed by the Danish company Apacta, this API provides a programmatic interface to their core cloud-based platform, which digitizes traditional field workflows. Its primary function is to centralize and streamline critical on-site activities, specifically the registration of working hours (time tracking and clocking), the logging of material consumption and inventory usage per job, and the execution of structured quality assurance (QA) checklists. Typical use cases include enabling foremen to clock crews in and out via mobile devices, automatically deducting used materials from a project's inventory in real-time, and ensuring compliance by having technicians complete digital inspection forms with photo evidence. For enterprises, it transforms paper-based processes into auditable digital trails, improving project cost accuracy, payroll efficiency, and regulatory compliance. For the individual tradesperson, it reduces administrative overhead, allowing them to focus on their craft while ensuring all billable hours and materials are meticulously recorded for invoicing.
πŸ€–AI Agent Value
Exposing the Apacta API as a set of tools within an AI coding assistant via the Model Context Protocol (MCP) unlocks significant productivity and automation potential for developers building solutions on or integrating with the Apacta platform. Instead of manually querying dashboards or writing boilerplate integration code, a developer can instruct their AI agent to directly interact with the live operational data. The AI gains real-time awareness of project statuses, labor allocation, and material stocks. This context allows the assistant to intelligently suggest optimizations, automate routine reporting, and proactively flag potential issues. For instance, it can help a developer quickly prototype a custom analytics dashboard by having the AI fetch and structure time log data across multiple job sites, or it can generate the scaffolding for a notification system that alerts when material usage on a project deviates from its budgeted estimate. The value lies in drastically accelerating the development cycle for custom integrations and internal tools, as the AI handles the data retrieval, formatting, and basic logic, freeing the developer to focus on higher-level architecture and business rules.
πŸ’¬Example Workflows
Through this MCP server integration, a developer can command the AI agent to perform a variety of dynamic, context-rich workflow tasks. For example, a manager could instruct, "AI, query all clocking records for this week for the Copenhagen site and generate a summary of hours worked versus hours scheduled in a table," enabling instant labor variance analysis. To automate project cleanup, one could command, "AI, identify all activities associated with the 'Retrofit Project' that have been inactive for over 90 days and prepare a draft bulk delete request for my review," streamlining database hygiene. For real-time inventory management, a developer could ask, "AI, get the current usage of 'Type X Cement' across all active projects and update our central procurement sheet with the total depletion, then alert me if any site is below safety stock." In quality assurance, a natural language instruction like, "AI, pull the latest five QA checklists for the electrical inspection activity and highlight any items that failed or had notes attached," would allow for rapid compliance auditing and issue tracking, transforming passive data into actionable insights.
πŸ›‘οΈSecurity & Auth
While the current authentication description indicates a method based on URL query parameters, it is critical for developers to treat this with extreme caution. Sending credentials in the URL can lead to exposure in server logs, browser history, and referrer headers. The foremost security best practice is to ensure all API communication occurs over HTTPS, as specified, to encrypt data in transit. For production use, it is strongly recommended to advocate for or implement more robust authentication mechanisms supported by the underlying platform, such as OAuth 2.0 bearer tokens or API keys included in the request headers. Developers should strictly adhere to the principle of least privilege, requesting and configuring only the specific API scopes (e.g., read-only access to clocking records) necessary for the AI agent's function. Configuration should be managed via secure environment variables, never hardcoded, and any AI tool integration should be sandboxed and tested thoroughly to prevent unintended data modification or deletion. Audit logs should be reviewed regularly to monitor API usage patterns for anomalous activity.

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