Privacy-first AI-native web analytics MCP server for querying traffic, events, funnels, and alerts via conversational agents.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent โ or use 1-click editor setup below.
We ran the install command below but it didn't respond within our test window โ this can mean a slow first-time install rather than a real problem.
npx -y @clamp-sh/mcpNo response to initialize.
This is an experimental automated check and can have false negatives โ missing environment variables, a slow cold install, etc. It doesnโt necessarily mean somethingโs wrong. Last checked 1mo ago.
๐ก Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Inspect callable tools, capabilities, and parameters exposed to AI agents by Clamp Sh MCP.
traffic.overviewPageviews, visitors, sessions, bounce rate, avg duration. Period comparison included.
traffic.timeseriesAny core metric over time with automatic granularity (visitors, sessions, bounce rate, avg duration, or event counts).
traffic.compareCompare any metric across two arbitrary periods. Returns absolute and percent delta.
traffic.liveVisitors active in the last N minutes plus their top pages, referrers, and countries.
breakdownGroup visitors and pageviews by any dimension: `pathname`, `referrer_host`, `country`, `city`, `region`, `device_type`, `browser`, `browser_version`, `os`, `os_version`, `entry_page`, `exit_page`, `channel`, or any UTM field.
pages.engagementPer-page metrics with `view`: `summary`, `engagement` (adds engagement seconds and bounce rate), `sections` (per-section view counts for one pathname; needs the section-views SDK extension).
Analytics MCP server for Clamp Analytics. Gives Claude, Cursor, VS Code Copilot, Windsurf, Cline, and any other Model Context Protocol client live access to your traffic, pages, events, revenue, errors, funnels, cohorts, and alerts โ and the tools to create funnels and metric alerts from the conversation.
Using Cursor, VS Code, Claude Code, or Claude Desktop? Install the hosted Remote MCP from your Clamp dashboard in one click โ auth happens automatically, no API key to paste, no config files to edit. Open a project, go to the Installation tab, and pick your editor. This npm package is the stdio path for CI, headless agents, custom clients, and self-hosted Clamp instances.
Once connected, your agent can answer questions like:
Each question maps to one or two MCP tool calls. The agent picks the right tool, runs the query, and returns the answer in chat โ no dashboard tab, no copy-paste.
A dashboard answers the questions you anticipated when you built it. An MCP server lets the agent answer the ones you didn't. Same data, different access pattern: the agent can compose tools, follow up on its own answers, and run the diagnostic queries you'd manually click through.
Tools are typed (outputSchema per MCP protocol 2025-06-18), so client-side validation works before any call lands. Per-tool descriptions name the use case and a representative example, so agents pick the right tool on the first try.
For one-click install in Cursor / VS Code / Claude Code / Claude Desktop, use the Clamp dashboard. For stdio (CI, headless agents, custom clients, self-hosted instances), pick your client below.
Add to .vscode/mcp.json in your project:
Both clients use the same shape โ drop this into claude_desktop_config.json or .cursor/mcp.json:
Get your API key from the Clamp dashboard under Settings โ API Keys. Keys are scoped to a project and start with sk_proj.
| Tool | What it does |
|---|---|
traffic.overview | Pageviews, visitors, sessions, bounce rate, avg duration. Period comparison included. |
traffic.timeseries | Any core metric over time with automatic granularity (visitors, sessions, bounce rate, avg duration, or event counts). |
traffic.compare | Compare any metric across two arbitrary periods. Returns absolute and percent delta. |
traffic.live | Visitors active in the last N minutes plus their top pages, referrers, and countries. |
breakdown | Group visitors and pageviews by any dimension: pathname, referrer_host, country, city, region, device_type, browser, browser_version, os, os_version, entry_page, exit_page, channel, or any UTM field. |
pages.engagement | Per-page metrics with view: summary, engagement (adds engagement seconds and bounce rate), sections (per-section view counts for one pathname; needs the section-views SDK extension). |
events.list | Custom event counts with property filtering and grouping. |
events.observed_schema | The actual fired-event signature with per-property type observations. Diff against a local event-schema.yaml to surface schema drift. |
events.property_values | Top distinct string-typed values a property has taken on a specific event. Use to discover the value space before defining a cohort filter or running events.list with property=. |
revenue.sum | Sum Money-typed event properties. Split by currency, group by any dimension, switch to first-touch via attribution_model. Drill-in filters: plan, product, first_touch_dim + first_touch_value. |
revenue.summary | The whole Revenue tab in one call: revenue per currency, customers, orders, AOV, LTV, MRR (running balance) + ARR, per-plan rollup (where mrr_contribution is the cumulative MRR balance per plan, not the in-period delta), per-product rollup, prior-period comparison, and mode flags (is_subscription, has_one_time) so callers can render conditionally. |
revenue.retention | Cohort revenue retention on MRR-on-MRR math. Per window: mature_size, retained, retention_rate, mrr_at_d, baseline_mrr_mature, nrr (net revenue retention; >1.0 = net expansion). Anchored at the customer's first subscription_started. |
revenue.timeseries | Plot one revenue metric per bucket. metric accepts revenue (Money sum), mrr (running balance, picks up from prior period), customers (distinct paying), or transactions (event count). |
sessions.paths | Aggregate session paths: top entry โ exit pairs with pages per session and duration. |
users.journey | Chronological session-and-event reconstruction for one anonymous ID. |
cohorts.create / cohorts.list / cohorts.retention / cohorts.compare / cohorts.delete | Define cohorts by event + period + filter; query retention curves; compare 2โ10 cohorts side-by-side on the same retention windows; delete a saved cohort by name (definitions are immutable, so delete + re-create is the rename/restructure flow). |
errors.list / errors.groups / errors.timeline / errors.context | Recent errors, fingerprint-grouped errors with affected-user counts, error rate over time, and breadcrumbs leading to a single error. |
projects.list | List all projects this credential can access. |
docs.search | Keyword-search the Clamp docs index. |
| Tool | What it does |
|---|---|
funnels.create | Define and immediately evaluate a multi-step conversion funnel. Steps accept property predicates: cta_click[location=hero][plan=pro]. Names must be unique within a project โ a duplicate-name call returns 409. |
funnels.list | List funnels or fetch one with cohort filters (country, channel, device, UTM). |
funnels.delete | Delete a saved funnel by name (definitions are immutable, so delete + re-create is the rename/restructure flow). |
alerts.create | Set up metric alerts (e.g. "visitors drops_by 30% over 7d"). |
alerts.list / alerts.delete | List and remove alerts. |
period โ "today", "yesterday", "7d", "30d", "90d", or a custom range as "YYYY-MM-DD:YYYY-MM-DD". Defaults to "30d".
limit โ Max rows returned, 1-50. Defaults to 10.
Filters โ Most tools accept pathname, referrer_host, channel, country, region, city, device_type, browser, browser_version, os, os_version, and the full UTM set (utm_source, utm_medium, utm_campaign, utm_content, utm_term). Funnels accept the same cohort filters.
dimension (breakdown only) โ see the breakdown row above for valid values.
view (pages.engagement only) โ "summary" (default), "engagement", or "sections". "sections" requires pathname.
Revenue filters (revenue.sum, revenue.timeseries) โ plan and product filter to one subscription tier or one-time product. first_touch_dim (one of channel, referrer_host, utm_campaign, utm_source, utm_medium, country) + first_touch_value filter to customers whose earliest session matched that dim. attribution_model="first_touch" on revenue.sum groups by first-touch dim instead of conversion-time dim.
Revenue metric (revenue.timeseries only) โ "revenue" (default), "mrr" (running balance), "customers", or "transactions".
Pre-built analytics workflows the AI can follow. Each prompt tells the model which tools to call, in what order, and how to present the results.
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