Report Generation MCP Server

What does “Report Generation” MCP Server do?

The Report Generation MCP Server is designed to connect AI assistants with robust report generation capabilities, integrating external data sources and structured workflows to streamline the creation and management of reports. By exposing key functionalities via the Model Context Protocol (MCP), this server allows developers and AI agents to automate tasks such as gathering data, assembling documents, and formatting outputs based on customizable templates. Its integration into the development workflow enhances productivity by enabling seamless interactions between AI tools and reporting utilities, making it easier to perform database queries, manage files, or invoke external APIs as part of report assembly.

List of Prompts

No specific prompt templates were found in the available files or documentation.

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List of Resources

No explicit resources are described in the available repository files or documentation.

List of Tools

No tools were explicitly listed in server.py or related files from the available repository content.

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Use Cases of this MCP Server

• Report Automation: Automate the end-to-end process of collecting data and generating structured reports, reducing manual effort and errors.
• Document Assembly: Assemble complex documents from multiple data sources, ensuring consistency and standardization across reports.
• Development Workflow Integration: Integrate with development tools to enable on-demand report generation as part of CI/CD or project tracking.
• Custom Report Templates: Leverage customizable templates for generating different types of reports tailored to various business needs.
• Data-Driven Insights: Enable AI assistants to generate reports based on real-time data queries, providing actionable insights to teams.

How to set it up

Windsurf

1. Ensure Node.js is installed as a prerequisite.
2. Open your Windsurf configuration file (e.g., windsurf.config.json).
3. Add the Report Generation MCP Server using the following JSON snippet:

   {
     "mcpServers": {
       "report-gen-mcp": {
         "command": "npx",
         "args": ["@klavis-ai/report_generation-mcp-server@latest"]
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify that the server is running and accessible from the MCP client panel.

Securing API Keys (Windsurf Example)

{
  "mcpServers": {
    "report-gen-mcp": {
      "command": "npx",
      "args": ["@klavis-ai/report_generation-mcp-server@latest"],
      "env": {
        "API_KEY": "${REPORT_GEN_API_KEY}"
      },
      "inputs": {
        "api_key": "${REPORT_GEN_API_KEY}"
      }
    }
  }
}

Claude

1. Install Node.js if not already present.
2. Locate the Claude MCP configuration file.
3. Insert the following:

   {
     "mcpServers": {
       "report-gen-mcp": {
         "command": "npx",
         "args": ["@klavis-ai/report_generation-mcp-server@latest"]
       }
     }
   }
   

4. Save changes and restart Claude.
5. Confirm the MCP server appears in the Claude integrations list.

Cursor

1. Verify Node.js is installed.
2. Open the Cursor workspace settings.
3. Add the server entry:

   {
     "mcpServers": {
       "report-gen-mcp": {
         "command": "npx",
         "args": ["@klavis-ai/report_generation-mcp-server@latest"]
       }
     }
   }
   

4. Save and reload the Cursor environment.
5. Test by triggering a report generation task.

Cline

1. Make sure Node.js is set up.
2. Access the Cline MCP configuration file.
3. Configure as below:

   {
     "mcpServers": {
       "report-gen-mcp": {
         "command": "npx",
         "args": ["@klavis-ai/report_generation-mcp-server@latest"]
       }
     }
   }
   

4. Save and restart Cline.
5. Check server diagnostics for successful registration.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "report-gen-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change "report-gen-mcp" to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

This MCP server appears to provide a useful abstraction for report generation, but the lack of visible prompt templates, resources, and tools in the public repository limits its immediate out-of-the-box utility for developers. Documentation on specific features or endpoints would improve usability. As it stands, the setup instructions are clear, but feature discovery is limited.

MCP Score

Overall, the current public implementation rates a 3 out of 10 for developer readiness, due to missing detailed documentation, prompt templates, and tool/resource definitions, despite clear setup instructions.