Adzviser MCP

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API KEY

ADVERTISING ANALYTICS

Marketing

Every ad account, cross-channel campaign, and performance metric your team tracks lives in Adzviser. Adzviser MCP gives your agent authenticated access to advertising analytics scoped to the user who authorized it.

  • Acts as the user: Access and write actions stay tied to the Adzviser MCP account that authorized the agent.
  • Credentials stay vaulted: AES-256, resolved at request time, never in LLM context.
  • Scoped before every call: User permissions enforced. 90-day audit trail.
Adzviser MCP
agent · Acme Q3
Run
Compare our Google vs Meta spend and ROAS for this month vs last month.
S
adzviser_performance_compare
88ms
Ad analytics agent
This month: Google $18.4K spend, ROAS 3.2x; Meta $12.1K, ROAS 2.8x. vs last month: Google up 12% spend, ROAS flat; Meta down 8% spend, ROAS improved 0.4x. Google driving better return.
Sources: Google Ads, Meta Ads, this month vs last month
adzvvisermcpmcp
2 platforms
18:29
Message Claude...

Tools your ad analytics agent reaches for on Adzviser MCP, scoped per user.

CALL ANY TOOL
List ad accounts, pull cross-channel metrics, compare periods, and generate consolidated reports.
adzvisermcp_list_workspace
List workspace
Retrieve a list of workspaces that have been created by the user and their data sources, such as Google Ads, Facebook Ads accounts connected with each.
Parameters
Name
Type
Required
Description
No parameters required
adzvisermcp_list_metrics_and_breakdowns_fb_post
List metrics and breakdowns fb post
adzvisermcp_list_metrics_fb_page
List metrics fb page
adzvisermcp_list_metrics_and_breakdowns_ig_post
List metrics and breakdowns ig post
adzvisermcp_list_countries_fb_ad_library
List countries fb ad library
adzvisermcp_retrieve_reporting_data
Retrieve reporting data
adzvisermcp_list_metrics_and_breakdowns_ga4
List metrics and breakdowns ga4
adzvisermcp_list_metrics_and_breakdowns_ebay
List metrics and breakdowns ebay
adzvisermcp_list_metrics_and_breakdowns_mntn
List metrics and breakdowns mntn
adzvisermcp_list_metrics_and_breakdowns_zoho
List metrics and breakdowns zoho
adzvisermcp_list_metrics_and_breakdowns_cm360
List metrics and breakdowns cm360
adzvisermcp_list_metrics_and_breakdowns_dv360
List metrics and breakdowns dv360
adzvisermcp_list_metrics_and_breakdowns_sa360
List metrics and breakdowns sa360
adzvisermcp_list_metrics_and_breakdowns_adroll
List metrics and breakdowns adroll
adzvisermcp_list_metrics_and_breakdowns_matomo
List metrics and breakdowns matomo
adzvisermcp_list_metrics_and_breakdowns_hubspot
List metrics and breakdowns hubspot
adzvisermcp_list_metrics_and_breakdowns_klaviyo
List metrics and breakdowns klaviyo
adzvisermcp_list_metrics_and_breakdowns_marketo
List metrics and breakdowns marketo
adzvisermcp_list_metrics_and_breakdowns_shopify
List metrics and breakdowns shopify
adzvisermcp_list_metrics_and_breakdowns_youtube
List metrics and breakdowns youtube
adzvisermcp_list_metrics_and_breakdowns_callrail
List metrics and breakdowns callrail
adzvisermcp_list_metrics_and_breakdowns_omnisend
List metrics and breakdowns omnisend
adzvisermcp_list_metrics_and_breakdowns_mailchimp
List metrics and breakdowns mailchimp
adzvisermcp_list_metrics_and_breakdowns_pipedrive
List metrics and breakdowns pipedrive
adzvisermcp_list_countries_google_ads_transparency
List countries google ads transparency
adzvisermcp_list_metrics_and_breakdowns_salesforce
List metrics and breakdowns salesforce
adzvisermcp_list_metrics_and_breakdowns_bigcommerce
List metrics and breakdowns bigcommerce
adzvisermcp_list_metrics_and_breakdowns_activecampaign
List metrics and breakdowns activecampaign
adzvisermcp_list_metrics_and_breakdowns_x_ads
List metrics and breakdowns x ads
adzvisermcp_list_metrics_and_breakdowns_fb_ads
List metrics and breakdowns fb ads
Build your Agent
Drop the toolkit in, point it at the user, and your ad analytics agent can use Adzviser MCP from the first run.
Python · LlamaIndex
import { ScalekitClient } from "@scalekit-sdk/node";
import { DynamicStructuredTool } from "@langchain/core/tools";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { z } from "zod";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);

const { tools } = await sk.tools.listScopedTools("user_123", {
filter: { connectionNames: ["adzvvisermcp"], toolNames: ["adzviser_accounts_list", "adzviser_campaigns_list", "adzviser_metrics_get"] },
pageSize: 100,
});

const lcTools = tools.map((t) => new DynamicStructuredTool({
name: t.tool.definition.name,
description: t.tool.definition.description,
schema: z.object({}).passthrough(),
func: async (args) => {
const { data } = await sk.tools.executeTool({
toolName: t.tool.definition.name,
identifier: "user_123",
params: args,
});
return JSON.stringify(data);
},
}));

const agent = createReactAgent({ llm, tools: lcTools });
import { ScalekitClient } from "@scalekit-sdk/node";
import OpenAI from "openai";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const openai = new OpenAI();

const { tools } = await sk.tools.listScopedTools("user_123", {
filter: { connectionNames: ["adzvvisermcp"], toolNames: ["adzviser_accounts_list", "adzviser_campaigns_list", "adzviser_metrics_get"] },
pageSize: 100,
});

const llmTools = tools.map((t) => ({
type: "function",
function: {
name: t.tool.definition.name,
description: t.tool.definition.description,
parameters: t.tool.definition.input_schema,
},
}));

const resp = await openai.responses.create({
model: "gpt-4o", input: prompt, tools: llmTools,
});
import { ScalekitClient } from "@scalekit-sdk/node";
import Anthropic from "@anthropic-ai/sdk";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const anthropic = new Anthropic();

const { tools } = await sk.tools.listScopedTools("user_123", {
filter: { connectionNames: ["adzvvisermcp"], toolNames: ["adzviser_accounts_list", "adzviser_campaigns_list", "adzviser_metrics_get"] },
pageSize: 100,
});

const llmTools = tools.map((t) => ({
name: t.tool.definition.name,
description: t.tool.definition.description,
input_schema: t.tool.definition.input_schema,
}));

const msg = await anthropic.messages.create({
model: "claude-sonnet-4-6", max_tokens: 1024,
tools: llmTools,
messages: [{ role: "user", content: prompt }],
});
import { Agent } from "@google/adk/agents";
import {
MCPToolset, StreamableHTTPConnectionParams,
} from "@google/adk/tools/mcp";

const toolset = new MCPToolset({
connectionParams: new StreamableHTTPConnectionParams({
url: "https://mcp.scalekit.com/adzvvisermcp",
headers: { Authorization: `Bearer ${userScopedToken}` },
}),
});

const agent = new Agent({
name: "agent", model: "gemini-2.0-flash",
tools: await toolset.getTools(),
});
Try these prompts
Paste any prompt into your agent to start using Adzviser MCP.
Accounts & campaigns
Copy the prompt
Copied
List all connected ad accounts.
Copy the prompt
Copied
Show active campaigns across all platforms.
Copy the prompt
Copied
Which campaigns are paused right now?
Copy the prompt
Copied
Get metrics for [campaign name] last 30 days.
Performance & compare
Copy the prompt
Copied
Compare Google vs Meta spend this month.
Copy the prompt
Copied
Which campaigns have ROAS above 3x?
Copy the prompt
Copied
Compare [campaign] this month vs last month.
Copy the prompt
Copied
Cross-channel spend breakdown today.
Reporting
Copy the prompt
Copied
Consolidated report for all platforms this week.
Copy the prompt
Copied
Top 5 campaigns by conversion volume.
Copy the prompt
Copied
Which ad sets have CPA above target?
Copy the prompt
Copied
Platform performance trend last 90 days.
SEE HOW AUTH WORKS
Users authorize Adzviser MCP once. Their credentials stay vaulted, every call is checked, and every action is logged.
1
Authorize
Your user connects
Adzviser 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
Adzviser 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
Adzviser 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
Adzviser 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
Same per-user auth pattern across other ad analytics agents and MCP connectors. Working code, live demos, fork what fits.
GTM and RevOps Teams
Revenue forecast commentary
Pulls open pipeline from Salesforce and HubSpot, calculates coverage against quota, flags at-risk stages, posts commentary to Slack, and logs every snapshot to Google Sheets.
GTM and RevOps Teams
Deal intelligence agent
Pulls recent Gong calls, scores deal risk with an LLM, cross-references the record in Attio, and DMs each owner their at-risk deals in Slack. Every read is scoped to that rep's own access.
Engineering Teams
Engineering standup agent
Pulls commits from GitHub and GitLab, tracks issue movement in Jira, and posts a per-engineer standup brief to Slack. Each engineer's activity is read on their own delegated OAuth.
GTM and RevOps Teams
Competitive intelligence briefing agent
Scans Gong calls for competitor mentions, matches each one to its Notion battlecard, and DMs every affected rep a single Slack digest per cycle. Every call runs as the PMM who owns the briefing, never a shared bot.
Test other agents
Same per-user auth pattern across other ad analytics agents and MCP connectors. Working code, live demos, fork what fits.
GTM
Revenue forecast agent
Score pipeline coverage against quota across Salesforce and HubSpot, post forecast commentary to Slack, log snapshots to Sheets.
SALES
Deal intelligence agent
Score Gong call risk with an LLM, cross-reference the Attio record, and DM each owner their at-risk deals in Slack.
ENGINEERING
Engineering standup agent
Pull commits from GitHub and GitLab, track Jira issue movement, and post a per-engineer standup brief to Slack.
GTM
Competitive intelligence briefing agent
Scan Gong calls for competitor mentions, match each one to its Notion battlecard, and DM every affected rep a single Slack digest.
Why Scalekit
Secure your agent's access. Connectors ship in minutes
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 so attribution, audit, and scope stay accurate.
// shared token
 audit → bot_service_account
 user_filter → broken

 // scalekit
 audit → user_abc
 scope → enforced ✓
02.
Authentication is not authorization
03.
Multi-tenancy is architectural
04.
Adzviser MCP today. Others 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 Adzviser MCP 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 Adzviser MCP api key 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 Adzviser MCP?
Yes. Pass a tool name filter to listScopedTools so the ad analytics agent only sees the subset you authorize. Pre-API-call scope checks block out-of-policy actions before the request reaches Adzviser MCP.
What happens when a user revokes Adzviser MCP 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.
Can the agent access ad accounts from other platforms the user hasn't connected?
Only platforms the authorizing user has connected to their Adzviser workspace. Disconnected or unauthorized ad accounts return no data. Access mirrors the user's native Adzviser connections.
Start in your coding agent
Up and running in one command
Install the Scalekit skill in your editor of choice. Connector, auth, tools, prompt, all wired up
Claude Code REPL
/plugin marketplace add scalekit-inc/claude-code-authstack
/plugin install agentkit@scalekit-auth-stack
Cursor Code REPL
# ~/.cursor/mcp.json
{
""mcpServers"": {
""adzvvisermcp"": {
""url"": ""https://mcp.scalekit.com/adzvvisermcp"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}
Codex Code REPL
# ~/.codex/config.toml
[mcp_servers.adzvvisermcp]
url = ""https://mcp.scalekit.com/adzvvisermcp""
auth_env = ""SCALEKIT_TOKEN""
Copilot Code REPL
# .vscode/mcp.json
{
""servers"": {
""adzvvisermcp"": {
""url"": ""https://mcp.scalekit.com/adzvvisermcp"",
""type"": ""http""
}
}
}