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What is TrackMCP?

If you've set up an MCP server but are unsure how to maximize its potential, TrackMCP can offer essential insights to enhance your experience. - Recognize the users accessing your MCP server - Identify which AI clients are establishing connections - Compare the number of newly registered users with returning ones - Gain insight into the tasks users are attempting to complete - Trace the sequence of tools they engage with - Assess whether users are successfully finishing their tasks - Detect potential bottlenecks in workflows and highlight opportunities for improvement With just a single line added to your MCP server using either TypeScript or Python, TrackMCP will seamlessly take over, delivering your data on the dashboard in real-time. Moreover, we provide a free MCP repository where you can display your MCP server, making it easier for others to discover and utilize it effectively. By sharing your project, you not only increase your visibility but also contribute to building a vibrant and collaborative community focused on MCP development. This collective effort can lead to innovative solutions and improvements across the board.

What is Ragie?

Ragie streamlines the tasks of data ingestion, chunking, and multimodal indexing for both structured and unstructured datasets. By creating direct links to your data sources, it ensures a continually refreshed data pipeline. Its sophisticated features, which include LLM re-ranking, summary indexing, entity extraction, and dynamic filtering, support the deployment of innovative generative AI solutions. Furthermore, it enables smooth integration with popular data sources like Google Drive, Notion, and Confluence, among others. The automatic synchronization capability guarantees that your data is always up to date, providing your application with reliable and accurate information. With Ragie’s connectors, incorporating your data into your AI application is remarkably simple, allowing for easy access from its original source with just a few clicks. The first step in a Retrieval-Augmented Generation (RAG) pipeline is to ingest the relevant data, which you can easily accomplish by uploading files directly through Ragie’s intuitive APIs. This method not only boosts efficiency but also empowers users to utilize their data more effectively, ultimately leading to better decision-making and insights. Moreover, the user-friendly interface ensures that even those with minimal technical expertise can navigate the system with ease.

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Integrations Supported

Confluence
Google Drive
Microsoft OneDrive
Microsoft PowerPoint
Model Context Protocol (MCP)
Nango
Notion
Salesforce
Workers by Delos

Integrations Supported

Confluence
Google Drive
Microsoft OneDrive
Microsoft PowerPoint
Model Context Protocol (MCP)
Nango
Notion
Salesforce
Workers by Delos

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Pricing Information

$500 per month
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

TrackMCP

Date Founded

2025

Company Location

India

Company Website

trackmcp.com

Company Facts

Organization Name

Ragie

Date Founded

2024

Company Website

www.ragie.ai/

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