Sybill MCP connector for AI agents

CALL ANY TOOL

5 tools covering get, list, search.

sybilmcp_ask_sybill

Ask sybill ai about your sales calls, deals, accounts, or co

Ask sybill ai about your sales calls, deals, accounts, or contacts.

Parameters

Name

Type

Required

Description

message

string

Required

Your question about sales calls, deals, accounts, or contacts.

sybilmcp_get_account

Get full details of a single crm account including contacts,

sybilmcp_get_conversation

Get full details of a single conversation including summary,

sybilmcp_get_deal

Get full details of a single crm deal including summary, con

sybilmcp_list_accounts

List crm accounts with optional filters for name, website, o

sybilmcp_list_conversations

List sales conversations with optional filters for date rang

sybilmcp_list_deals

List crm deals with optional filters for name, stage, amount

Build your Agent

Same auth pattern across every framework.

from langchain_mcp_adapters.client import MultiServerMCPClient
from scalekit import ScalekitClient

client = ScalekitClient(env_url=ENV_URL, client_id=CLIENT_ID, client_secret=SECRET)
token = client.agent.get_token(user_id="user_id", connector="sybilmcp")

mcp = MultiServerMCPClient({
    "sybilmcp": {
        "url": "https://mcp.scalekit.com/sybilmcp",
        "headers": {"Authorization": "Bearer " + token}
    }
})
tools = await mcp.get_tools()

import OpenAI from "openai";
import { ScalekitClient } from "@scalekit-sdk/node";

const client = new ScalekitClient({ envUrl, clientId, clientSecret });
const token = await client.agent.getToken({ userId: "user_id", connector: "sybilmcp" });

const openai = new OpenAI();
// Connect to MCP at https://mcp.scalekit.com/sybilmcp
// Pass: Authorization: Bearer + token

import Anthropic from "@anthropic-ai/sdk";
import { ScalekitClient } from "@scalekit-sdk/node";

const client = new ScalekitClient({ envUrl, clientId, clientSecret });
const token = await client.agent.getToken({ userId: "user_id", connector: "sybilmcp" });

const anthropic = new Anthropic();
// Connect to MCP at https://mcp.scalekit.com/sybilmcp
// Pass: Authorization: Bearer + token

from google.adk.agents import LlmAgent
from scalekit import ScalekitClient

client = ScalekitClient(env_url=ENV_URL, client_id=CLIENT_ID, client_secret=SECRET)
token = client.agent.get_token(user_id="user_id", connector="sybilmcp")
# Connect to MCP at https://mcp.scalekit.com/sybilmcp
# Pass: Authorization: Bearer + token

Try these prompts

Paste any prompt into your agent to get started.

Get started

Copy the prompt

Copied

Ask Sybill AI about your sales calls, deals, accounts, or contacts?

Copy the prompt

Copied

List sales conversations with optional filters for date range, meeting type, and attendees?

Advanced

Copy the prompt

Copied

Get full details of a single conversation including summary, transcript, and recording URLs?

SEE HOW AUTH WORKS

User authorises once. Every agent call after uses their token with scope enforcement.

1

Authorize

Your user connects

Sybill MCP

once. We tie it to their identity and the meetings they approved — no shared bot account, no org-wide access

Who:

user ‘A’

when:

Once per user

access:

Limited to user

2

Store

Their

Sybill MCP

token lives in a vault scoped to them. User A's meetings are never reachable by an agent acting for user B, even on the same connection

vault:

encrypted

scope:

per-user

tokens:

auto-refreshed

3

Resolve

When your agent calls a

Sybill MCP

tool, we fetch the right token server-side. It never touches your agent, never appears in the LLM context, never shows up in your logs

speed:

~40ms

check:

before every call

seen by:

nobody

4

Audit

Every

Sybill MCP

tool call is logged — who triggered it, which meeting was fetched, what came back. 90 days of history, tied to the user who authorized it

history:

90 days

export:

SIEM-ready

logged:

every call

Test other agents

See the same per-user auth pattern across other connectors.

No items found.

Why Scalekit

Secure your agent's access. Connectors ship in minutes

Other connector libraries treat auth as a demo afterthought.

01.

Shared tokens break per-user analytics

A shared token looks fine in a demo. In production every call looks like a service account. Scalekit resolves the real user credential.

// shared token
 audit → bot_service_account

 // scalekit
 audit → user_abc ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

One connector today. Ten tomorrow.

“Our agents act across Salesforce, Gong, Google Drive, and more, on behalf of every customer. Scalekit behind the scenes meant we can keep adding tools without ever rebuilding how credentials or tool calling work.”

Venu Madhav Kattagoni

Head of Engineering / Von

FAQs

Frequently Asked Questions

Does the agent access Sybill as the user or as a shared key?
As the user. Each workspace member authorizes once and Scalekit resolves their credential at request time. Audit logs attribute every action to that user, not a shared service account.

Where is the Sybill OAuth token stored?
In Scalekit's managed AES-256 token vault, namespaced per tenant. Refresh is automatic. Revocation is a single dashboard action. Tokens never appear in prompts, logs, or LLM context.

Can I limit what the agent is allowed to do in Sybill?
Yes. Pass a tool name filter to listScopedTools so the AI agent only sees the subset you authorize. Pre-API-call scope checks block out-of-policy actions before the request reaches Sybill.

What happens when a user revokes Sybill access?
The connection is invalidated on the next tool call. Subsequent requests for that user fail closed with a clear error. Other users in the tenant remain unaffected. The event is logged for audit.

Which call summaries can the agent surface from Sybill?
Only deals, accounts, and conversations visible to the authorizing user. A rep sees their own calls; a manager sees the team's. Sybill's native visibility rules apply to every ask_sybill query.

Sybill MCP

Sybill MCP tools for AI agents