Calypso Multimodal RAG
@calypso-so
About Calypso Multimodal RAG
Launch a hosted Gemini File Search true multimodal RAG layer that answers from your PDFs, docs, screenshots, charts, diagrams, help center, FAQs, and images with citations people can verify.
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"calypso-mcp": {
"command": "npx",
"args": [
"-y",
"calypso-mcp"
],
"env": {
"CALYPSO_API_KEY": "YOUR_CALYPSO_API_KEY",
"CALYPSO_API_BASE_URL": "https://api.calypso.so/v1"
}
}
}
}Tools
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Overview
What is Calypso Multimodal RAG?
Calypso Multimodal RAG is an MCP server that brings multimodal Retrieval-Augmented Generation (RAG) to AI tools and workflows, providing a grounded answer layer for websites, AI agents, support copilots, internal knowledge assistants, and product experiences. It retrieves from actual source materials—PDFs, docs, screenshots, diagrams, charts, help content, and structured knowledge—to ensure answers remain source-backed, explainable, and production-ready.
How to use Calypso Multimodal RAG?
After configuring the Calypso MCP server in your MCP client (e.g., Claude Desktop), you can invoke three tools: calypso-rag-agent to query the RAG agent for grounded answers, calypso-upload-agent-file to upload a file into the agent store, and calypso-upload-knowledge-file to upload a file into the durable knowledge store.
Key features of Calypso Multimodal RAG
- Grounded answers with retrieval-backed responses, not generic model output.
- Multimodal RAG support across PDFs, docs, screenshots, diagrams, and images.
- Built for AI agents and real product surfaces such as website chat and support.
- Two separate upload stores: agent store for retrieval-backed RAG and knowledge store for durable indexing.
- RAG-first file semantics via
rag_policyon agent file attachments.
Use cases of Calypso Multimodal RAG
- AI support assistants that answer from company documentation and knowledge bases.
- Sales enablement copilots with grounded product information.
- Internal knowledge agents retrieving from internal files and diagrams.
- Website answer widgets offering searchable, citation-backed responses.
FAQ from Calypso Multimodal RAG
What is a grounded answer layer?
A grounded answer layer retrieves from real source material—such as PDFs, docs, or screenshots—rather than relying on generic model memory, so responses are source-backed, explainable, and production-ready.
What types of source material does Calypso support?
Calypso supports PDFs, documentation, help center content, screenshots, diagrams, charts, images, internal files, and structured knowledge.
How does Calypso handle file uploads?
The server provides two upload tools: calypso-upload-agent-file puts content into the agent store for retrieval-backed RAG chat, and calypso-upload-knowledge-file ingests content into a durable knowledge store for shared corpus indexing.
What makes Calypso different from other MCP RAG servers?
Calypso is designed as a multimodal grounded answer layer with reuse across multiple surfaces, retrieval over real source material, and strong fit for operational and customer-facing AI systems.
Frequently asked questions
What is a grounded answer layer?
A grounded answer layer retrieves from real source material—such as PDFs, docs, or screenshots—rather than relying on generic model memory, so responses are source-backed, explainable, and production-ready.
What types of source material does Calypso support?
Calypso supports PDFs, documentation, help center content, screenshots, diagrams, charts, images, internal files, and structured knowledge.
How does Calypso handle file uploads?
The server provides two upload tools: `calypso-upload-agent-file` puts content into the agent store for retrieval-backed RAG chat, and `calypso-upload-knowledge-file` ingests content into a durable knowledge store for shared corpus indexing.
What makes Calypso different from other MCP RAG servers?
Calypso is designed as a multimodal grounded answer layer with reuse across multiple surfaces, retrieval over real source material, and strong fit for operational and customer-facing AI systems.
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