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Ragie

Safety: 75/100

0.0(0)

This MCP server provides access to Ragie's knowledge base via a 'retrieve' tool, enabling AI models to search and retrieve relevant information.

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Overview

This MCP server provides access to Ragie's knowledge base via a 'retrieve' tool, enabling AI models to search and retrieve relevant information.

This MCP server is relatively safe for read-only access to a knowledge base. However, the security of the API key and the sensitivity of the data in the knowledge base should be carefully considered. Misuse of the 'retrieve' tool could lead to information leakage or excessive API usage.

Performance depends on the Ragie API's response time and the size of the knowledge base. Consider optimizing queries and caching results to improve performance.

Cost is primarily determined by Ragie API usage. Monitor API call volume to manage expenses.

HybridJavaScript86 starsMITReleased 5/20/2025

Connections & Capabilities

Connects To

GitHub

Capabilities

Read

Client Compatibility

Claude Desktop

full

Cursor

full

Windsurf

untested

VS Code (Copilot)

untested

Quickstart

Install

npx -y

Claude Desktop Config

{
  "mcpServers": {
    "ragie": {
      "command": "npx",
      "args": [
        "-y",
        "@ragieai/mcp-server",
        "--partition",
        "optional_partition_id"
      ],
      "env": {
        "RAGIE_API_KEY": "your_api_key"
      }
    }
  }
}

Environment Variables

RAGIE_API_KEY

Tools (1)

safe

retrieve

Searches the Ragie knowledge base for relevant information based on a query.

Read-only access to knowledge base; no write or delete operations.

Security

Auth Method

API Key

Data Locality

hybrid

Known Risks

!Exposure of the Ragie API key if not properly managed.
!Potential for information leakage from the knowledge base.
!Denial-of-service if the 'retrieve' tool is used to make excessive requests.
!Vulnerabilities in the Ragie API could be exploited through this server.

Safety Score

moderate risk

Positive

+Read-only access to knowledge base via the 'retrieve' tool.
+No direct write or delete operations exposed.
+Requires an API key for authentication.
+Can be configured to query specific Ragie partitions, limiting scope.

Risks

-API key is stored as an environment variable, which can be exposed if not properly managed.
-Potential for information leakage if the knowledge base contains sensitive data.
-The 'retrieve' tool could be used to gather large amounts of data if not rate-limited appropriately.
-Dependency on the Ragie API introduces a potential point of failure.

Use Cases

Knowledge Retrieval

-Answering user questions based on company documentation.
-Providing context to AI models for improved reasoning.
-Summarizing information from multiple sources.

Customer Support

-Finding solutions to common customer problems.
-Providing agents with relevant information during customer interactions.
-Automating responses to frequently asked questions.

Content Creation

-Generating ideas for blog posts and articles.
-Researching topics for content marketing.
-Creating summaries of existing content.

Who Is This For?

Best For

AI models needing access to a knowledge base.
Applications requiring information retrieval capabilities.
Customer support agents seeking quick answers.
Content creators researching topics.

Not Ideal For

Applications requiring write access to the knowledge base.
Scenarios where real-time information is critical.
Use cases involving sensitive data without proper security measures.

Autonomy & Execution

Default ModeRead-only

Sandboxed

The server only provides read access to the knowledge base, limiting the scope of autonomous actions.

StatelessYes

Retry LogicNot documented

ConcurrencyNot documented

Production Tip

Monitor API usage and implement rate limiting to prevent abuse and ensure availability.

FAQ

What is the purpose of this MCP server?

It provides a standardized way for AI models to access and query a Ragie knowledge base.

How do I install and configure the server?

Install the package using npm and set the RAGIE_API_KEY environment variable. Optionally, configure the partition ID.

What is the 'retrieve' tool?

It's the primary tool for searching the knowledge base and retrieving relevant information.

How do I specify which knowledge base partition to query?

Use the `--partition` command line option or configure it in the MCP configuration file.

What kind of authentication is used?

API key authentication is used to access the Ragie API.

Can I use this server with Claude desktop?

Yes, by creating a `claude_desktop_config.json` file with the appropriate configuration.

Is it safe to expose this server to the internet?

It's generally safe as it provides read-only access, but protect the API key and monitor usage.

Key Features

MCP Server Protocol Support - Enables seamless integration with any MCP-compliant AI client or IDE.

Retrieve Tool - Provides a standardized function for AI agents to query and fetch information from Ragie knowledge bases.

Knowledge Base Connectivity - Bridges the gap between proprietary Ragie datasets and external AI models.

Context-Aware Retrieval - Allows AI assistants to pull relevant data chunks dynamically based on the current user conversation.

Use Cases & Problems Solved

Use Cases

•Use when you need to connect an AI assistant to a centralized Ragie knowledge base for context-aware document retrieval.
•Perfect for developers building AI agents that require real-time access to company-specific documentation or private knowledge repositories.
•Ideal if you need to integrate Ragie's search capabilities directly into an MCP-compliant client like Claude Desktop.
•Great for automating the retrieval of specific snippets from large datasets without manually copying and pasting information into a prompt.
•Use to streamline the development of RAG-based applications by offloading the retrieval logic to an existing, optimized MCP server.

Problems Solved

✓Eliminates the complexity of writing custom API integration code for connecting AI models to Ragie data stores.
✓Removes the friction of manually searching through disparate document silos when prompting an AI model.
✓Reduces the development time required to make private knowledge bases accessible to LLMs via standard protocols.
✓Solves the issue of context window limitations by programmatically fetching only the most relevant document chunks for a specific query.

Who It's For

AI application developers working with the Model Context ProtocolFull-stack engineers building RAG-enabled internal toolsTechnical product managers integrating knowledge management systems into AI workflowsDevOps engineers managing AI infrastructure and data pipeline connectivity

Metrics

Discovered2/14/2026

Reviews0

Saved By0 users

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