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Calypso Multimodal RAG MCP Server

Official
by calypso-so
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TypeScript
Remote

calypso-rag-agent

Ask natural-language questions and receive grounded, cited answers from Calypso's knowledge base, with support for file IDs and multi-turn follow-ups.

Instructions

[CALYPSO RAG AGENT] Sends each prompt directly to the Calypso RAG agent using the full conversation context.

Use this when you want Calypso knowledge retrieval and grounded answers from the RAG backend. Typical requests:

  • "Summarize the key points from our onboarding documentation"

  • "What does the knowledge base say about campaign approval rules?"

  • "Compare the documented indexing flow with the retrieval flow"

  • "Answer using the uploaded file ids: ["file_123"]"

Responses API behavior:

  • First turns start a named Calypso conversation via /v1/responses.

  • Follow-up turns chain with previous_response_id so the backend owns conversation state.

  • When fileIds are provided, the MCP uses rag_policy retrieval semantics instead of inline attachment stuffing.

MCP session behavior:

  • This tool maintains a stable conversation id in the background for multi-turn retrieval context.

  • Use /new to start a fresh conversation and clear the current context window.

Quick commands (examples):

  • "Summarize the latest indexed knowledge about WhatsApp templates"

  • "Find the source of truth for campaign approval behavior"

  • "Start a new topic" (or use /new)

Available RAG models: calypso-rag-agent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoOptional RAG model variant. Defaults to `calypso-rag-agent`. Available models: calypso-rag-agent.
promptYesYour request. Include context, constraints, and desired output.
fileIdsNoOptional uploaded agent-store `file_id` values to attach with `rag_policy` retrieval semantics.

Tool Definition Quality

A4.5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully explains behavior: maintains conversation state, chains follow-up turns via previous_response_id, uses rag_policy for fileIds, and supports /new to reset context. This goes well beyond what the schema reveals.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is fairly long but organized into sections (Responses API, MCP session, quick commands). Every section adds value, though some repetition (model name listed twice) and length could be trimmed slightly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and no output schema/annotations, the description covers conversation state, file handling, and usage examples. Missing return format details, but the overall context is sufficiently complete for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds meaning by explaining fileIds trigger rag_policy retrieval semantics, and clarifies model default. This enriches the parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool's verb and resource: 'Sends each prompt directly to the Calypso RAG agent using the full conversation context.' It clearly distinguishes this from sibling tools (which list/upload knowledge files) by focusing on retrieval and grounded answers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit usage context: 'Use this when you want Calypso knowledge retrieval and grounded answers from the RAG backend.' It provides typical request examples and quick commands. However, it does not explicitly mention when NOT to use it or name alternative tools for other actions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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