qorami-mcp
▎ Check an email before an AI agent sends it: send / ask a human / block — with machine reason codes and prompt-injection detection.
README
Qorami SDK
Official clients, tool schemas and an MCP server for Qorami — a control point between your AI agents and actually sending email. Before each send, the agent asks Qorami, which replies send, request_human_confirmation, or do_not_send.
Get an API key in the dashboard. Full API reference: https://qorami.fr/docs.
| Path | What |
|---|---|
js/ |
Zero-dependency JavaScript / TypeScript client (fetch, Node 18+ or browser). |
python/ |
Zero-dependency Python client (stdlib only) + a LangChain tool. |
tools/ |
Drop-in OpenAI function-calling & Anthropic tool-use schemas for qorami_check_email. |
mcp/ |
Stdio MCP server (verify_email + check_action_status) for Claude Desktop, Cursor, any MCP client. |
examples/ |
Runnable Node & Python quickstarts. |
JavaScript / TypeScript
import { QoramiClient } from './js/qorami.mjs'
const qorami = new QoramiClient({ apiKey: process.env.QORAMI_API_KEY })
await qorami.guard(
{ recipient: 'client@example.com', subject: 'Our offer', body, policyProfile: 'sales' },
{
send: () => mailer.send(), // allowed
requestHumanConfirmation: (r) => queue(r.action.id), // a human was notified
doNotSend: (r) => log('blocked', r.decision), // do not send
},
)
Or step by step with qorami.verify(...) and, after a review, poll
qorami.status(actionId) until nextAction.type === 'send'.
Python
from qorami import QoramiClient
qorami = QoramiClient(api_key=os.environ["QORAMI_API_KEY"])
result = qorami.verify(recipient="client@example.com", subject="Our offer",
body=email_body, policy_profile="sales")
if result.next_action_type == "send":
send_email()
elif result.next_action_type == "request_human_confirmation":
queue_for_review(result.action_id) # a human was notified by email
# else: do_not_send
LangChain
python/langchain_tool.py exposes a
qorami_check_email tool an agent can call before sending:
from langchain_tool import build_qorami_tool
tool = build_qorami_tool() # reads QORAMI_API_KEY
MCP server
Register Qorami as a native tool in Claude Desktop / Cursor / any MCP client —
see mcp/. It exposes verify_email and check_action_status over stdio.
The contract
Every client returns the same decision the agent must obey via nextAction.type:
send, request_human_confirmation (a human approves first — poll the action),
or do_not_send. See https://qorami.fr/docs.
License
MIT — see LICENSE.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.