ChatGPT Plugin

ABMeter

A/B tests from your assistant

Brand: ABMeter

Category: Developer Tools
Primary Subcategory: Product Analytics & Experimentation

Integration details

Description

ABMeter is an A/B testing and feature flag service for individuals and small teams without a data scientist. Your assistant designs an experiment with you, creates its parameters, variants, metrics and audiences, and reads the results with a two-sample z-test. Starting or stopping an experiment and turning a feature flag on or off happen only on the ABMeter website, where you are signed in. Once an experiment runs, each user stays in the group they were first assigned to: the assistant cannot change its allocation or audiences, and an edit that would move its users, such as a feature flag that overrides a tested parameter, is refused and handed to you on the website. The assistant can still edit the rest of your configuration from the conversation, including the hypothesis, description and metrics of a running experiment, parameter defaults and variants that no running experiment uses, and the audience or variant of a flag that is already on. A new account is empty. Ask the assistant to load the sample data to try it on a realistic dataset in a separate demo space.

Integration type: Plugin
Verification status: Not applicable
Platform: ChatGPT
Category: Developer Tools
Primary Subcategory: Product Analytics & Experimentation
Secondary Subcategories: None listed
Brand: ABMeter
Access: Account required
First tracked: 2026-11-04
Tool count: 33
Market: US

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Primary Subcategory

Competing in ChatGPT Product Analytics & Experimentation

View Category

Listed in alphabetical order

Primary Subcategory

ABMeterComing soonGet notified

Adobe CJA0/100

Amplitude41/100

Amplitude EU41/100

Caprail0/100

Tools

33 tools agents can invoke

Create a new event type for tracking user actions and deriving metrics. Every event automatically includes user_id (for experiment attribution) and occurred_at (timestamp) - only define custom_fields for additional data.
create_event_type
Create a new A/B experiment.
create_experiment
Create a new feature flag with audience targeting
create_feature_flag
Create a custom business metric with a formula over event measures
create_metric
Create a new parameter in a space for use in experiments
create_parameter
Create a new predicate-based global audience for targeting
create_predicate_audience
Create a new space for organizing feature flags and experiments
create_space
Create a new user list global audience for targeting specific users
create_user_list_audience
Create a new variant with parameter values in a space
create_variant
Evaluate experiment effects using a metric formula. Returns statistical analysis including p-values, confidence intervals, and effect sizes for each audience compared to control. Use search_metrics tool to find available metrics before calling this tool. Method: two-sample z-test on per-user metric values, fixed horizon (no sequential testing). Full explanation: https://abmeter.ai/lab/statistics If the groups are too small to detect the lift the user hopes for, the sample-size calculator says how many users and weeks the test needs, and what to do instead: https://abmeter.ai/lab/sample-size
evaluate_experiment_effects
Evaluate exposure statistics for an experiment, showing unique user counts per audience
evaluate_exposure_stats
Link this MCP client to your browser for automatic navigation
link_to_browser
Open one of the customer's own ABMeter pages (experiment, feature flag, metric, parameter, variant, space, audience, event type) in the linked browser. Not a general web-browsing tool. Call `link_to_browser` once before the first navigation; without it this tool fails.
navigate_browser
Send the ABMeter team a feature request on the user's behalf: what they wanted to do and what was missing. Nothing in the account changes; people at ABMeter read it.
report_gap
Search for event types that can be used to create metrics
search_event_types
Search for events to understand what data is being collected
search_events
Search for experiments by name, hypothesis, status, or space
search_experiments
Search for feature flags by slug, description, enabled state, or space
search_feature_flags
Search for metrics (customer-defined business KPIs with formulas). Each metric includes the list of event types it references in its formula. For measures (atomic aggregations like count, sum, avg), use search_event_types instead.
search_metrics
Search for parameters by name or type
search_parameters
Search for spaces (experimentation layers) by name or description
search_spaces
Search for variants by name, description, or space
search_variants
Load a realistic sample dataset (experiments, feature flags, metrics, ~10k events) into the demo-space. Resets only the demo-space and demo entities — data in other spaces is untouched. Re-running regenerates the dataset from scratch.
setup_demo
Read-only historical lookup of feature flag exposures: unique user counts over time, per time bucket
track_feature_flag_exposures
Update an existing event type to modify custom fields or description
update_event_type
Update an existing experiment configuration. Supports updating allocation, hypothesis, description, metrics, and audiences. Allocation and audiences (add/remove/rename/rebalance) can only change while the experiment is in 'init' state; once started, both are locked until the experiment is archived or reset.
update_experiment
Update an existing feature flag configuration (description, audience, variant)
update_feature_flag
Update an existing custom business metric (description and/or formula)
update_metric
Update an existing parameter configuration (type, default_value, description)
update_parameter
Update an existing predicate-based global audience
update_predicate_audience
Update an existing space
update_space
Update an existing user list global audience
update_user_list_audience
Update an existing variant
update_variant

FAQs

ABMeter ChatGPT Plugin FAQ

How the directory, categories and Discoverability Score work.

Read the methodology

How do I improve ABMeter's ChatGPT Plugin discoverability?

The levers are the listing surface agents actually read: names, descriptions, keywords, tool metadata, and registry health. Which lever matters depends on where discovery breaks, which is what continuous measurement shows.

What are ABMeter alternatives on ChatGPT?

As of 2026-11-04, ABMeter competes with PostHog, Mixpanel, Amplitude, Amplitude EU, Statsig, Datadog Experiments, Adobe CJA, Caprail and 31 more in ChatGPT Product Analytics & Experimentation, ranked by public Discoverability Score.

Where is this profile measured?

This profile uses the geography attached to the latest public registry snapshot: US. Locale tags are intentionally omitted.