Laiki MCP: how it works and the guide to ready-to-use prompts

Laiki MCP is the feature that connects an AI agent, such as Claude or ChatGPT, directly to the marketing and sales data centralized on the platform. Instead of exporting a spreadsheet, copying numbers and pasting them into the chat, Laiki users ask the question in natural language and receive the analysis calculated on the real data of their own operation, in minutes.

How Laiki MCP works day to day

In practice, the connection covers four fronts. The first is discovering the account and what already exists in it: organization, segments already created, connected paid media accounts, existing dashboards. Before any analysis, the agent confirms what is already available, so it does not recreate from scratch something the operation already has ready. This avoids the most common rework for those who start using AI without this connection: describing again, in every conversation, how the company's funnel is structured.

The second is querying leads and events: how many leads a campaign generated in a period, the full history of a specific lead (pages visited, emails opened, forms submitted), the behavior of an entire segment. This is the part that replaces the spreadsheet exported manually every month, and it is where most of an analyst's time usually goes.

The third is the live paid media overview, straight from Meta Ads, Google Ads and LinkedIn Ads, without waiting for a manual report to be built. Asking how much was invested, what the CPL is by channel or how many InMails a campaign generated now gets an answer right away, with data that matches what is in the ad account itself.

The fourth is what is already structured in dashboards and segments inside the platform: the agent reads what the team has already configured, instead of rebuilding the rule from scratch with every question. If a segment of hot leads already exists, for example, the agent reuses that rule instead of trying to guess the criterion.

Together, these four fronts make the agent answer real business questions: which channel brought in the most customers, which lead segment is hottest this week, how many InMails a LinkedIn campaign generated, what is the average speed between one stage and another of the sales funnel. Always in the vocabulary of the operation itself, because the MCP already sees the segmentation, the funnel stage and the campaign naming that the team uses day to day, not a generic funnel model that does not match the company's reality.

What changes compared with a generic AI or just having a dashboard

A dashboard shows the numbers that someone has already decided to calculate and display beforehand. An AI without this connection answers only with what is in the prompt. Laiki MCP sits between the two: it lets you ask something new, that nobody designed in advance, and still receive an answer with the real data of the account, not a generic estimate. Anyone who has tried asking an ordinary AI to calculate the speed between funnel stages knows the difference: the answer explains the formula, but has no number from the operation itself behind it.

This also changes the routine of those who build reports. A question that used to require opening three different tools, exporting each one, cross-referencing them manually in a spreadsheet and only then building the chart is now asked directly to the agent, which already has access to the organized database. The collection work disappears; what is left is deciding what to do with the answer.

From the loose question to the right prompt

Having the MCP connected solves the technical part of the connection, but still leaves one question open: what to ask. Most teams spend more time deciding which question to ask the data than actually waiting for the answer. This is the problem that the Laiki Prompt Guide solves: 70 ready-to-copy-and-paste prompts, organized by area (Marketing, Sales, Behavior) and by data source (GA4, Meta Ads, Google Ads, LinkedIn Ads, Segments, Leads).

Instead of figuring out on your own how to ask about multichannel attribution, sales cohorts or real CPL, the guide already brings the question ready, with an explanation of what each prompt is for and a suggestion of how to deepen the analysis from it. A Sales-type example asks for an overview of deals closed by period, comparing the speed between stages of the sales funnel. A Behavior-type example asks for the analysis of a specific lead, crossing pages visited with email opens, to understand whether they are warming up or cooling down. A Marketing-type example asks for the attribution of a lead from the first click to the close, comparing first-click with last-click on the same customer. The answer can come out as plain text in the conversation, or become a PDF, an HTML page or a presentation, depending on what the team asks for. The guide is available for free download.

Data security behind the answer

Connecting an external AI agent to the company's data raises a legitimate question: who else has access to that data. At Laiki, data isolation happens per organization at the database layer, with AES-256-GCM encryption, passwordless authentication and audit logs. The AI agent only sees what the account has authorized, and none of this changes how the operation already protects this data today.

A real example

The case of Willian Martins, Marketing Manager at Dimas Construções, illustrates what happens when data leaves the spreadsheet and enters an organized database: centralization of data that used to be scattered, real-time analysis of indicators and faster, more reliable decisions. It is the same principle that underpins the MCP: the answer is only good when the data behind it is already organized before the question is asked. An AI agent does not create that organization; it depends on it to answer accurately.

Frequently asked questions about Laiki MCP

Do I need to know how to code to use Laiki MCP?

No. The technical connection already comes ready on the platform; users just install the connector in the AI agent of their choice and start asking in natural language, without writing a single line of code.

Does Laiki MCP work with Claude and ChatGPT?

Yes, both. Because it is an open protocol, different AI agents connect to the same organized Laiki database, without depending on an exclusive integration for each one.

Does the Prompt Guide replace the need to learn how to use the MCP?

No, it speeds up the first stage, which is knowing what to ask. The quality of the answer still depends on the account's data being organized, which Laiki solves even before the question exists.

Do I have to pay per token to use Laiki MCP?

Using the MCP depends on the AI agent chosen by the customer (Claude, ChatGPT), which has its own usage cost. Those who prefer not to deal with token consumption can generate dashboards and reports directly on the platform, without going through the external agent.

Which tools does Laiki MCP already see?

Meta Ads, Google Ads, LinkedIn Ads and GA4 on the media side, plus CRM integrations such as RD Station, HubSpot, Pipedrive, CV CRM and Exact Sales. Any other public API can come in through the platform's own workflows.

What stays after connecting

Laiki MCP turns a business question into an answer with real data behind it, without exporting a spreadsheet and without waiting for a report to be built by hand. The Prompt Guide shortens the path to the first right question, for those who do not yet know where to start. Discover the Laiki platform and see how this works on the data your operation already collects.