Klavis ReportGen MCP Server Automated Report Generation

Overview

This MCP (Model Context Protocol) server provides an automated, AI-powered workflow for generating professional reports and documents. It is designed as a modular service that can be self-hosted or used via Klavis’ managed infrastructure. The server exposes report-generation capabilities that combine templating, data ingestion, and large language model (LLM) powered analysis so developers can produce consistent, exportable reports at scale.

For teams building analytics pipelines, compliance workflows, or agent-driven automation, the ReportGen MCP server reduces manual document assembly by converting structured data and templates into polished outputs (PDF, Word, HTML) and by adding AI-driven insights or executive summaries. It is intended for integration with other services (agents, webhooks, chat platforms, orchestration layers) and supports enterprise deployment patterns.

Features

• Generate full reports from templates and structured data using LLM-assisted content synthesis.
• Manage and reuse templates for branding, legal, or formatting consistency.
• Import data from multiple sources and map it into templates.
• Export reports to common formats: PDF, DOCX (Word), and HTML.
• Produce automated insights, summaries, and analysis sections using AI models.
• Run as a managed service (recommended for production) or self-host via Docker/container.
• Designed to integrate with agent frameworks, function-calling workflows, and webhooks.

Installation / Configuration

Quick install using Klavis managed service (recommended):

# Python client
pip install klavis

# or JavaScript client
npm install klavis

Python example: create a Report Generation MCP server instance via Klavis SDK:

from klavis import Klavis

klavis = Klavis(api_key="your-free-key")
server = klavis.mcp_server.create_server_instance("REPORT_GENERATION", "user123")
# Once created, use the SDK's server object to request report generation.

Self-host with Docker:

# Pull the latest Docker image
docker pull ghcr.io/klavis-ai/report-generation-mcp-server:latest

# Run the container (example; set your Klavis API key as env var)
docker run -p 5000:5000 -e KLAVIS_API_KEY=$KLAVIS_API_KEY \
  ghcr.io/klavis-ai/report-generation-mcp-server:latest

Configuration notes:

• KLAVIS_API_KEY is required for managed LLM/AI access and feature toggles.
• Default service port is 5000 in the container image (can be remapped).
• Use the Klavis SDK for authentication and orchestration; the MCP server uses the Klavis API for model access and telemetry.

Repository:

• Source and issues: https://github.com/Klavis-AI/klavis/tree/main/mcp_servers/report_generation
• Docs & API keys: https://www.klavis.ai/docs and https://www.klavis.ai/home/api-keys

Available Tools

• Report Creation
  • Generate reports from a template + data payload. Templates control layout and placeholders.
• Template Management
  • Create, update, version, and reuse templates (branding, sections, and layout).
• Data Integration
  • Map JSON/CSV/time-series inputs to template placeholders. Connectors or webhooks can feed data into the service.
• Format Exporters
  • Convert generated content to PDF, DOCX, and HTML for distribution or archival.
• Automated Insights
  • Use LLM-based analysis to produce executive summaries, key findings, or recommendations.

Supported output formats (examples):

Use Cases

• Monthly business reports: automate the assembly of KPIs, charts, and narrative summaries from analytics pipelines. Trigger generation at month-end and auto-distribute PDFs to stakeholders.
• Executive summaries: convert raw analysis tables into concise executive-ready summaries using AI-generated insights and recommended actions.
• Audit / compliance documentation: assemble standardized compliance documents from structured event logs and templates; include human-reviewed sections and AI-suggested rationales.
• Customer-facing reports: generate custom, branded reports per-customer by combining templates with customer-specific data (billing, usage, SLAs).
• Agent-driven workflows: integrate with conversational agents or orchestration layers (including Discord bots or monitoring systems) to create on-demand reports via function calling or MCP messages.

Integration patterns

• SDK-first: use the Klavis SDK to create server instances, send data payloads, and request report generation programmatically.
• REST / Webhook: run the MCP server as a service that accepts payloads from automation platforms, CI pipelines, or chatbots; then retrieve or push the rendered artifact.
• Event-driven: connect data pipelines to trigger report generation when a dataset is updated or a scheduled job completes.

Contributing & License

• Contributions are welcome — see the repository’s CONTRIBUTING.md for guidelines.
• This project is Apache 2.0 licensed (see LICENSE in the repository).

For questions, API keys, and detailed SDK examples, visit the Klavis docs (https://www.klavis.ai/docs) or open an issue on the GitHub repo.