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MCP server · RAG

Ragora

Search across all your knowledge bases to retrieve relevant information instantly from connected documentation. Discover specific collections and perform targeted queries to get precise answers for any topic. Compare information across multiple sources and monitor usage to manage your data assets efficiently.

Unclaimed last commit 7 months ago rag
34Low

Scored 3 months ago · breakdown

About Ragora

Ragora is an MCP server published by velarynai in the RAG category: search across all your knowledge bases to retrieve relevant information instantly from connected documentation. Discover specific collections and perform targeted queries to get precise answers for any topic. Compare information across multiple sources and monitor usage to manage your data assets efficiently.

Six months or more without a commit doesn't mean the server is broken, but check the open issues before depending on it in production.

Install

From source
git clone https://github.com/velarynai/ragora-mcp

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

Tools (3)

discover_collectionsList all knowledge bases you have access to, with descriptions, stats, and usage examples

Parameters not published.

searchSearch across **all** your knowledge bases at once

Parameters not published.

search_collectionSearch a **specific** collection by name, with optional tag/metadata filters

Parameters not published.

Ask AI

✦

Ask AI about Ragora

Powered by Claude · Grounded in docs

I know everything about Ragora. Ask me about installation, configuration, usage, or troubleshooting.

Security checks

  • ✓README presentThe repository ships documentation.

5 checks have not been run yet.

README

Ragora MCP Server

Search your knowledge bases from any AI assistant using the Model Context Protocol.

Ragora's MCP server is a hosted cloud service — no local installation required. Just configure your AI tool with the server URL and your API key.

Server URL: https://mcp.ragora.app/mcp

Quick Start

1. Get an API Key

Sign up at ragora.app and create an API key at Settings > API Keys.

2. Connect Your AI Tool

Claude Desktop

Option 1: CLI (recommended)

claude mcp add --transport http ragora https://mcp.ragora.app/mcp \
  --header "Authorization: Bearer sk_live_your_key_here"

Option 2: Config file

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (Mac) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "ragora": {
      "type": "http",
      "url": "https://mcp.ragora.app/mcp",
      "headers": {
        "Authorization": "Bearer sk_live_your_key_here"
      }
    }
  }
}

Claude Code

claude mcp add --transport http ragora https://mcp.ragora.app/mcp \
  --header "Authorization: Bearer sk_live_your_key_here"

Cursor

Add to .cursor/mcp.json in your project root:

{
  "mcpServers": {
    "ragora": {
      "type": "http",
      "url": "https://mcp.ragora.app/mcp",
      "headers": {
        "Authorization": "Bearer sk_live_your_key_here"
      }
    }
  }
}

VS Code / GitHub Copilot

Add to .vscode/mcp.json in your project root:

{
  "mcpServers": {
    "ragora": {
      "type": "http",
      "url": "https://mcp.ragora.app/mcp",
      "headers": {
        "Authorization": "Bearer sk_live_your_key_here"
      }
    }
  }
}

Windsurf

Add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "ragora": {
      "serverUrl": "https://mcp.ragora.app/mcp",
      "headers": {
        "Authorization": "Bearer sk_live_your_key_here"
      }
    }
  }
}

Any MCP Client

Ragora uses Streamable HTTP transport. Point any MCP-compatible client to:

  • URL: https://mcp.ragora.app/mcp
  • Method: POST
  • Header: Authorization: Bearer sk_live_your_key_here

Available Tools

Tool Description
discover_collections List all knowledge bases you have access to, with descriptions, stats, and usage examples
search Search across all your knowledge bases at once
search_collection Search a specific collection by name, with optional tag/metadata filters

Tool Parameters

search(query, top_k?)

  • query (required): Natural language search query
  • top_k (optional): Number of results to return (default: 5)

search_collection(collection_name, query, top_k?, custom_tags?, filters?)

  • collection_name (required): Collection name or slug (use discover_collections to find names)
  • query (required): Natural language search query
  • top_k (optional): Number of results (default: 5)
  • custom_tags (optional): Tags to scope retrieval
  • filters (optional): Metadata filters

Dynamic Collection Tools

For each collection you have access to, dedicated tools are generated using the collection's slug:

  • search_{slug} — semantic search with optional version, tags, and filters
  • get_topic_{slug} — retrieve information about a specific topic
  • list_versions_{slug} — list available documentation versions

For example, a collection called "Employee Handbook" gets:

  • search_employee_handbook
  • get_topic_employee_handbook
  • list_versions_employee_handbook

Configure tool names and descriptions in the Ragora dashboard under Collection > MCP Settings.

Usage Examples

Search all collections:

"Search my docs for webhook event handling"

Search a specific collection:

"Look up the refund policy in the employee handbook"

Discover what's available:

"What workspaces do I have, and what's in them?"

How It Works

Your AI Tool                        Ragora Cloud
┌──────────────────┐               ┌──────────────────┐
│ Claude / Cursor  │   HTTP POST   │  MCP Server      │
│ / VS Code / etc  │──────────────>│  (Streamable HTTP)│
└──────────────────┘    /mcp       └────────┬─────────┘
                                            │
                                   ┌────────▼─────────┐
                                   │  Gateway API      │
                                   │  (Auth + Search)  │
                                   └────────┬─────────┘
                                       ┌────┴────┐
                                       ▼         ▼
                                   Postgres    Qdrant
                                   (metadata)  (vectors)
  • No local install — connect directly via HTTP
  • Multi-tenant — your API key determines which collections you can access
  • Dynamic tools — tools are generated based on your accessible collections
  • Hybrid search — dense (E5-Large) + sparse (BM25) with reciprocal rank fusion

Security

  • API keys authenticate all requests via Authorization: Bearer header
  • Each API key is scoped to the user's owned and purchased collections
  • Cross-tenant data access is impossible — all queries are filtered by accessible collection IDs
  • API keys should never be committed to version control

Troubleshooting

Issue Solution
"Tool not found" Restart your AI app after config changes. Verify your API key starts with sk_live_
"Unauthorized" Check the Authorization: Bearer header format. Verify the key hasn't expired
No results Ensure documents are uploaded and processing is complete. Try broader queries
Connection timeout Check internet access. Verify server status at https://mcp.ragora.app/health

Links

License

MIT

README copy captured when this server was indexed; it may lag the repository. The original is authoritative.

Questions

About Ragora

How do I install Ragora?

No package has been published for this server, so install it from source: clone the repository and follow its README.

Is Ragora safe to use with an AI agent?

Its trust score is 34 out of 100 (low). It passes 1 of the 1 static security checks run so far. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Ragora still maintained?

The last commit was 7 months ago. That's long enough that you should check whether the maintainer is responding to issues before depending on it.