Available Tools

Fibbler exposes a set of read-only tools through MCP. You do not need to call these tools directly. Your AI tool will choose the right one based on what you ask. This page is a reference so you know what data is available.

Workspace switching

If you are a member of multiple Fibbler workspaces (typical on the Agency plan), you can switch between them from inside the AI tool. This works for both OAuth connections (Claude, ChatGPT) and API keys that are scoped to All accounts. The two switching tools below only show up when you have access to more than one workspace that can use MCP. The switch is persisted, so subsequent conversations stay on the chosen workspace until you switch again.

list_accounts

Lists every Fibbler workspace you have access to and marks which one is currently active. Useful to confirm what the AI is looking at before asking a data question. Try: "Which Fibbler workspaces can you see?"

switch_account

Switches the active workspace. You can refer to the workspace by name or ID. After switching, every other tool call returns data for the new workspace. Try: "Switch to the Acme workspace, then show me last month's pipeline."

Workspace overview

get_account_overview

Shows which integrations are connected (LinkedIn, Google Ads, Meta Ads, CRMs), what data is available, and which tools you can use. This is the best starting point for any conversation.

Campaign performance

get_campaign_performance

LinkedIn campaign analytics with deal influence. Shows impressions, clicks, engagements, spend, CTR, engagement rate, companies reached, influenced deals, influenced pipeline, and influenced revenue per campaign. Pipeline counts influenced deals that are not closed-won (open and closed-lost); revenue is closed-won. It also returns weighted pipeline and weighted revenue per campaign, the deduplicated versions that split each deal across the campaigns that reached it (so, unlike the influenced figures, they are safe to sum for a non-overlapping total). Like the app, the split is by impressions unless you ask for engagement, and some ad formats, like text and spotlight ads, aren't part of it. You can filter by time period and sort by any metric, including influenced or weighted pipeline and revenue. It returns the top 20 campaigns unless you ask for more, while the totals always cover every campaign in the period. It accepts the same periods as every other tool: rolling windows, calendar periods, 180 days, a full year, all time, or a custom month range (see time periods).

It is also the tool to use for the account-level headline numbers people put in a monthly or quarterly report: total ad spend, companies reached and companies engaged. Both company counts are paid only and count each company once however many campaigns reached it, so a company that only saw an organic company page post is not in them. That makes them lower than a figure that includes organic, and comparable to a paid-only company list.

compare_campaign_periods

Campaign performance trends with period-over-period comparison. Shows totals for the current period alongside percentage changes versus the previous period, plus top movers. Supports rolling periods (7d, 30d, etc.) and calendar periods (this quarter, this year, this month).

get_trend_data

Month-over-month trending for impressions, engagements, companies reached, ad spend, and pipeline. Shows absolute values and percentage change for each month. You can ask for 1 to 24 completed months (6 if you don't say), and the current month so far is added at the end, marked as partial. The pipeline trend can be narrowed to the buying company's industry, size or country, in which case the ad metrics are left out because they cannot be split that way.

Deal attribution

get_influenced_deals

Lists individual deals that were influenced by LinkedIn, Google Ads or Meta Ads. Each deal includes amount, stage, touchpoint counts (paid impressions, clicks, engagements, organic), and days to create/close. You can filter by time period, deal status (all, won, open, lost), source (LinkedIn, Google Ads or Meta Ads), and by the buying company's industry, size or country (see filtering by industry, size and country). Sort by date, amount, or name.

get_deal_attribution_summary

Aggregated pipeline and revenue metrics. Shows total influenced deals, pipeline, revenue, won deals, ad spend, average days to close, channel breakdown (LinkedIn, Google Ads and Meta Ads), cost per opportunity, and top campaigns by pipeline and revenue. Optionally compare against a previous period. You can also narrow it to the buying company's industry, size or country, in which case the spend figures are left out (see filtering by industry, size and country).

get_deal_campaign_breakdown

Maps campaigns to deals. You can group by campaign (to see which campaigns influenced the most deals) or by deal (to see which campaigns contributed to a specific deal). Supports text search for deal names, filtering by source, and filtering by the buying company's industry, size or country.

get_linkedin_deal_insights

What the deals LinkedIn influenced look like next to the deals LinkedIn never touched, the same numbers as the Deal Insightspage. For both groups you get the number of deals, pipeline, median deal size, won deals, revenue, median sales cycle and win rate. Influenced follows your attribution settings, and not touched means no LinkedIn activity at all before the deal was created. Defaults to the last 12 months and can be filtered by company size. It describes the deals, it doesn't say LinkedIn caused them. Requires LinkedIn Ads and a CRM connection.

Companies and journeys

get_company_engagement

Shows which companies are seeing your LinkedIn Ads. Each company includes paid impressions, clicks, engagements, and organic activity. You can filter by time period, search by company name, and sort by impressions, clicks, or engagements.

get_customer_journey

Timeline of ad exposure and deal progression for a specific company. Shows month-by-month paid and organic impressions and engagements, plus deal events (created, closed, amounts, stages). Search by company name. Covers up to 24 months of history.

get_audience_titles

The LinkedIn job titles your ads actually reached, with impressions, clicks, engagements, and CTR, in total or per campaign. Each title also shows whether it is currently excluded or included in your targeting (the same data behind Audience Exclusions). Read-only: it reports the breakdown, it does not change your exclusions. Try: "Which job titles are engaging most with our LinkedIn ads?"

get_audience_insights

The industries, company sizes and countries of the companies your LinkedIn ads reached, with companies and impressions per group. The same data as the Audience Insights page, with one difference: the tool counts paid and organic reach together unless you ask for paid only, while the page starts on paid only. You can filter by campaign, industry, size and country before the breakdown is calculated, and ask for the individual companies behind any one group. Try: "Which industries are we reaching in Sweden?"

Evidence and correlation

get_correlation_evidence

The correlational answer to "did the ads actually matter?" Two comparisons: Reach (did companies you reached more enter the pipeline more often?) and Engage(did companies that engaged enter more than those who only saw the ads?). Each returns a verdict: Clear correlation, Correlation, No correlation, Negative correlation, or Too little data. The verdict is power-aware, so a thin sample reads as "too little data" rather than a false signal. This is correlation, never proof the ads caused the outcome. Requires a LinkedIn connection and a CRM.

The same call also returns pipelineGrowth, the third thing in that drawer: how long pipeline takes to arrive, on two clocks (from first reach, and from first engagement). That part is timing, not correlation, so it carries none of the causal caveats above. Note the windows differ on purpose: the two comparisons always read the last 12 months, while pipelineGrowth reads your whole history, because a timing curve cut to a window truncates every cohort reached before it opened.

Warm accounts

get_warm_accounts

Companies heating up on your ads that are not deals in your CRM yet. They are engaging with LinkedIn ads or visiting via Google Ads or Meta Ads. A prospecting shortlist, not a performance report. Choose the channel and the scope (account-wide or per campaign). Each company is flagged as brand new or rising (engagement at least doubled versus the prior period). Try: "Which accounts are heating up on LinkedIn but aren't deals yet?"

Cross-channel

get_cross_channel_performance

Compares LinkedIn Ads, Google Ads and Meta Ads on the same rule, the same numbers as the Cross-Channel Insights page. A deal counts toward a channel if someone at the company clicked one of its ads before the deal was created. For each channel you get spend, deals that clicked, cost per deal, cost per won deal, pipeline, revenue, median deal size, median sales cycle and win rate, with Google Ads split into branded and non-branded search. LinkedIn deals that met your attribution settings with your ads but without a click are reported next to the LinkedIn row, and organic never counts. A No paid ad touch group gives you something to compare against, and the totals count each deal once. Can be filtered by company size, and can optionally return every campaign that spent, month by month. Requires LinkedIn Ads, a CRM connection, and the Google Ads or Meta Ads add-on.

Google Ads

get_google_ads_insights

Companies visiting your website from Google Ads, with deal attribution. Shows company name, visit count, influenced pipeline, and revenue. You can filter by time period, search by company name, filter to companies with or without deals, sort by visits, pipeline, or revenue, and filter by industry.

get_google_ads_campaign_analytics

Google Ads campaign and keyword performance in one view. Shows campaign-level spend, companies identified, pipeline and revenue, plus a keyword-level breakdown with the same metrics. Deal attribution is canonical: pipeline counts influenced deals created in the period that are not closed-won (open and closed-lost), and revenue is closed-won, so the numbers reconcile with the rest of Fibbler. Filter by time period.

get_google_ads_audience_insights

Audience breakdown by industry and company size from Google Ads visitors. Shows top industries, company size distribution, total companies identified, and total visits. Filter by time period.

Meta Ads

get_meta_ads_insights

Companies visiting your website from Meta Ads (Facebook and Instagram), with deal attribution. Shows company name, visit count, influenced pipeline, and revenue. You can filter by time period, search by company name, filter to companies with or without deals, sort by visits, pipeline, or revenue, and filter by industry. Try: "Which companies came to our site from Meta Ads and have a deal?"

get_meta_ads_campaign_analytics

Meta Ads campaign performance: spend, impressions, clicks and engagements, plus influenced pipeline and revenue per campaign, counted the same way as everywhere else in Fibbler. Meta has no keywords and we don't get conversions from it, so neither is in here. It also tells you how many visits came without a campaign ID on the link, since those can't be tied to a campaign. Filter by time period.

get_meta_ads_audience_insights

Audience breakdown by industry and company size for the companies that visited your website from Meta Ads. It describes who arrived, not who the ads were aimed at, and it has no job titles. Filter by time period.

Filtering by industry, size and country

Questions like "how much pipeline came from fintech companies under 200 employees in Sweden" work over MCP. The deal tools can be narrowed to companies with a given industry, company size or country: get_influenced_deals, get_deal_attribution_summary, get_deal_campaign_breakdown and get_trend_data, plus get_company_engagement and get_audience_insights on the audience side. Just describe the segment in your question. This is the same filter as the one on the Audience Insights and Company Insights pages, so the numbers agree.

Time periods

Most tools support flexible time period filtering. You can ask for data using rolling periods (last 7 days, last 30 days, etc.), calendar periods (this quarter, this month, this year), or custom month ranges. Just describe the time range you want in natural language and it will be translated to the right filter.

• Quarters and years are calendar ones. This quarter means January to March, April to June, and so on, and this year means January to December. Fibbler has no fiscal year setting, so if your financial year is offset, ask for the months explicitly instead of saying "Q3".
• Anything longer than 90 days is served as whole calendar months. Fibbler stores LinkedIn company data as calendar months plus the 7, 30, 60 and 90 day rolling windows, so there is no day-level series behind a 180 day or full-year question, and arbitrary day ranges are not available anywhere in Fibbler.
• The answer states the period it actually used. LinkedIn campaign answers come back with the window the numbers cover, including a note when the request had to be widened to whole months. If what you asked for and what you got differ, that line is the authority.
• A rolling total is not the sum of the months it spans. The two come from different LinkedIn endpoints, so do not derive one from the other.

Multiple CRMs

If you have more than one CRM connected, name the one you want in your question (e.g., "show me influenced deals from HubSpot"). If you don't, Fibbler picks one of them for you.