lodd.dev

Lodd's MCP server exposes 42 tools covering traffic, events, funnels, conversion attribution, and engagement metrics, so its agent can answer both single-metric lookups and compound questions that require several of those tools at once. The practical limit is your event volume: the free tier covers up to 2,500 events/mo, and it works with Claude Desktop, Claude Code, Cursor, Codex, or any MCP client.

The metric areas the 42 tools cover

Lodd groups its tools into five areas. A question is answerable if it maps onto one or more of them:

Because all 42 tools sit in the same conversation, the agent can chain them. That is the difference from a dashboard: a dashboard shows one view at a time, while the agent can pull traffic, funnel, and source data into a single answer.

Simple questions

These map to one tool or one metric:

• "How much traffic did we get this week?"
• "Which pages got the most visits?"
• "What are our top traffic sources?"
• "How many signups came from Reddit?"
• "Show me the conversion funnel from /pricing to /signup."

Compound questions — where it actually earns its place

The site's own framing is that questions like "Why did signups drop?" need traffic data, funnel analysis, and source attribution simultaneously. A dashboard can't assemble that; an agent with all the tools in one session can.

Documented examples from Lodd's page:

• Traffic drop diagnosis. The agent spotted a 34% traffic drop, traced it to a CSS regression hiding the pricing CTA, and shipped the fix — all inside the editor session.
• Funnel breakdown. Asked for a conversion funnel, it built one from live data, found an 86% drop-off between pricing and signup, identified a 12-field form as the cause, and explained what to change.
• Source decay. A weekly check caught a 45% drop in Reddit traffic before anyone noticed.
• Multi-tool root cause. For "Why did signups drop?", the agent used get_analytics, get_funnel, and get_traffic_sources together and returned: signups down 34%, Reddit traffic down 45% because an r/SaaS post aged off the front page (that post had been driving 67% of signup traffic), and the /pricing → /signup funnel falling from 28% to 11% because the signup page has 12 fields and Reddit traffic bounces in 8 seconds.

Note the pattern in that last answer: it combines a metric change, a cause, and a recommended action. That is the shape of question Lodd is built for.

Question types that fit well

• "Why did X change?" — needs a metric plus a breakdown by source or step.
• "Is channel Y worth it?" — needs attribution plus engagement, e.g. "Is Reddit actually worth it?"
• "Did the deploy break anything?" — needs a before/after comparison across traffic and funnels.
• "How do we compare to last month?" — needs period-over-period comparison.
• "What should I write about next?" — needs source and engagement data to rank topics.

Recurring and scheduled questions

Beyond ad-hoc prompts, you can set up a recurring check — for example a weekly traffic review. The agent runs it automatically, compares this week to last, flags anything unusual, and delivers a summary. This is how the 45% Reddit drop was caught.

What to check before relying on it

• Event volume. Free up to 2,500 events/mo. If your traffic is well above that, confirm the paid tier's terms before assuming coverage — the page shows a "Simple pricing" section but no figures in the material available here.
• Client. Confirm your MCP client is supported: Claude Desktop, Claude Code, Cursor, Codex, or any MCP client. The same tools and data apply across them.
• Setup. Lodd describes setup as "one prompt," with a copy-install prompt on the page. Querying is likewise prompt-based — there is no dashboard to navigate.

If your questions are mostly "what is this single number," a dashboard is cheaper. If they are "why did this happen and what do I change," the multi-tool chaining is the reason to use Lodd.

An MCP server is a service that exposes tools and data to an AI agent through the Model Context Protocol, so the agent can call those tools directly inside a conversation. Lodd is a web analytics MCP server: it gives an agent 42 tools covering traffic, events, funnels, conversion attribution, and engagement metrics, letting the agent query your analytics data in the same session where you're already working. It's the right fit if you use an MCP client like Claude Desktop, Claude Code, Cursor, or Codex and want analytics answers without switching to a dashboard.

What MCP actually is

The Model Context Protocol is a standard way for AI agents to connect to external tools and data. Instead of you copying numbers out of a dashboard and pasting them into a chat, the agent calls the tool itself and reads the result.

For analytics, that changes the unit of work. A dashboard answers one question per screen. An agent with analytics tools can call several at once and combine the results.

Why Lodd puts analytics behind MCP

Lodd's own framing is "analytics that live where you work." The mechanism is straightforward: the analytics tools are exposed to the agent, so the agent has your traffic data in the same session as your code or your conversation.

That matters for compound questions. Lodd's example is "Why did signups drop?" — a question that needs traffic data, funnel analysis, and source attribution at the same time. In Lodd's illustration, the agent used get_analytics, get_funnel, and get_traffic_sources together and returned a combined answer: Reddit traffic fell 45% after a post aged out, and the /pricing → /signup step dropped from 28% to 11%, with a 12-field signup form as the likely cause.

A single dashboard can't produce that answer, because no one chart holds all three pieces. The agent can, because it has all 42 tools in the same conversation.

What you can do with it

The documented use cases fall into three patterns:

The tools cover traffic, events, funnels, conversion attribution, and engagement metrics. Lodd also states it works with Claude Desktop, Claude Code, Cursor, Codex, and any other MCP client — same tools, same data.

How setup and querying work

Lodd describes the flow as "one prompt to set up, any prompt to query," and offers a copy-install prompt on the site. The general shape:

1. Connect — add Lodd as an MCP server in your client (Claude Desktop, Claude Code, Cursor, Codex, or another MCP client).
2. Ask — pose a question in natural language, like "why did signups drop?" or "is Reddit actually worth it?"
3. Expect tool calls — the agent selects and runs the relevant analytics tools, then answers from live data rather than from a fixed report.

The expected result is an answer in the conversation, not a link to a dashboard you then have to interpret.

Practical conditions to weigh

• Client requirement. This only works if you already use an MCP-capable client. Without one, there's nothing for the server to plug into.
• Volume limit. Lodd's site states free up to 2,500 events/mo. The page references "pricing" and "upgrade" but the supplied material doesn't include plan details or prices, so check the site for current tiers before committing.
• Privacy. Lodd lists "Private by design" as a heading; the supplied excerpt doesn't detail the mechanism, so read the site's privacy section if that's a deciding factor.
• Trade-off vs. dashboards. You gain compound, conversational answers and lose the fixed, always-on visual overview. Teams that need a shared dashboard for non-agent users may still want one alongside Lodd.

The short version: MCP is the connection layer, and Lodd uses it so your agent can answer multi-part analytics questions in one pass instead of you assembling the answer from separate reports.

Lodd is a web analytics MCP server that lets an AI agent query traffic, sources, funnels, and events directly inside a conversation, while Google Analytics is a dashboard-first platform you navigate yourself. Choose Lodd if you want analytics answers delivered by an agent in the same session as your work; stay with Google Analytics if you need a mature, standalone reporting suite and don't mind manual dashboard exploration. The comparison below uses the same dimensions for both, based on Lodd's own site claims and general knowledge of Google Analytics.

Core difference: who does the querying

The practical consequence: with Lodd, "why did signups drop?" is answered by combining traffic, funnel, and source data in one agent turn. With Google Analytics, that same question usually means opening several reports and joining the findings yourself.

What an agent workflow looks like in practice

Lodd's site gives a concrete example. An agent used get_analytics, get_funnel, and get_traffic_sources together and reported:

• Signups down 34% for the week
• Reddit traffic fell 45% after an r/SaaS post aged off the front page — that post had been driving 67% of signup traffic
• The /pricing → /signup funnel dropped from 28% to 11%, traced to a 12-field signup form that casual traffic abandons in 8 seconds

It then proposed two fixes: post a follow-up to a relevant subreddit, and cut the form to 3 fields. That is the shape of the difference — the agent didn't just surface a metric, it chained sources, funnel, and diagnosis in one conversation.

Other examples on the site include an agent spotting a 34% traffic drop caused by a CSS regression hiding a pricing CTA and shipping the fix without leaving the editor, and a scheduled weekly check that caught a 45% drop in Reddit traffic before anyone noticed.

Where each option fits

Lodd is the better fit when:

• Your analytics questions are exploratory and multi-source ("why did signups drop?", "is Reddit actually worth it?", "did the deploy break anything?")
• You already work inside an MCP client and want data in the same session as your code
• You want charts and tables generated on demand rather than navigating fixed dashboards
• You want scheduled reports delivered as agent summaries

Google Analytics remains the better fit when:

• You need a standalone reporting platform independent of any AI client
• Your team relies on shared dashboards and standard report templates
• You want the ecosystem maturity and integrations of an established platform

Setup and cost signals

Lodd describes setup as "one prompt to set up" and querying as "any prompt to query." It works with Claude Desktop, Claude Code, Cursor, Codex, or any MCP client. The site states it is free up to 2,500 events per month, and its pricing section references upgrades — but the provided page evidence does not list specific paid tiers or prices, so check the site directly before committing. Lodd also emphasizes "private by design," though the evidence doesn't detail the underlying data handling, so treat that as a claim to verify against your own requirements.

Bottom line

The decision is less about feature parity and more about where you want the analysis to happen. If your workflow lives in an AI agent and you want analytics to answer compound questions in one turn, Lodd is built for exactly that. If you need a conventional, dashboard-centric analytics platform that stands on its own, Google Analytics is still the default — and the two can coexist while you evaluate.

Lodd is free for up to 2,500 events per month, and the site describes pricing as "simple" with an upgrade path beyond that. Privacy is presented as a design principle rather than a feature: the page's own heading is "Private by design," and the framing throughout is "your data, your questions." If you want to test an MCP-native analytics tool with an AI agent before committing budget, the free tier is the intended entry point. What the site does not publish is the actual price of the paid tier, the exact event-count thresholds above the free plan, or the mechanics of how data is stored and isolated — so treat those as open questions to confirm before you rely on Lodd for anything sensitive.

What the free tier covers

The only concrete number on the page is the ceiling: free up to 2,500 events/mo. An "event" here is the unit Lodd counts — the same unit its 42 MCP tools query across traffic, events, funnels, conversion attribution, and engagement metrics.

That budget is small. A low-traffic site, a staging environment, a side project, or a single landing page can plausibly stay under 2,500 events in a month. A production site with steady visitor flow will not. The practical read: the free tier is sized for evaluation and small surfaces, not for running analytics on a real audience at scale.

How pricing is framed

The page uses the phrase "Simple pricing" as a section heading and references an upgrade, but publishes no numbers. That's a deliberate pattern in early-stage developer tools: the free tier is the pitch, and pricing is discussed in context. It also means you cannot budget from the page alone.

Two things follow from this. First, the free tier is the honest starting point — you can install, connect an agent, and see whether the query model fits how you work before any commercial conversation. Second, "simple pricing" is a claim, not a specification. If cost predictability matters to you, get the actual tier structure in writing before you route production traffic into Lodd.

Privacy: what "private by design" actually claims

The page's third heading is "Private by design," and the recurring line is "Your data, your questions." The intent is clear — analytics data stays tied to your account and is queried by your agent rather than pooled into someone else's dashboard product. That's a meaningful contrast with the default model of mainstream web analytics, where your traffic data lives in a vendor's interface and you visit it on their terms.

But "private by design" is a positioning statement, not a data-processing agreement. The page does not state where data is stored, how long it's retained, whether it's encrypted at rest, whether it's used for anything beyond serving your queries, or what happens to it if you stop paying. Those are the questions that determine whether Lodd is appropriate for a site handling personal data, health information, or anything under a compliance regime.

If your use case is a marketing site or a developer tool, the privacy framing is probably sufficient context to proceed. If your use case involves regulated data, treat the heading as a prompt to ask for specifics rather than as an answer.

Why the free tier is the right way to evaluate this

Lodd's value proposition is not "another dashboard." It's that your agent holds all 42 tools in the same conversation as your code, so a single question can pull traffic, funnel, and source data together. The page's own example walks through this: a 34% signup drop traced to Reddit traffic falling 45% after a post aged off a front page, plus a funnel break where /pricing → /signup fell from 28% to 11% because a 12-field form was killing casual traffic.

That kind of multi-tool reasoning is what you're actually buying. It's also hard to judge from a feature list — you need to ask your own questions against your own data and see whether the answers are useful. The 2,500-event free tier is enough to do exactly that on a small property, and it's the only way to find out whether the "one prompt to set up, any prompt to query" claim holds up for your workflow.

Choosing: when the free tier is enough, and when to ask about upgrading

Stay on the free tier if you're evaluating Lodd, running it on a side project or staging site, or your monthly event volume comfortably fits under 2,500. There's no reason to pay for capacity you don't use.

Ask about the paid tier if you're putting Lodd in front of real traffic, you need a cost figure to justify the tool internally, or you need retention and storage guarantees the page doesn't provide. The upgrade path exists; the terms are the missing piece.

Look elsewhere if you need published, self-serve pricing before you can adopt a tool, or if your data falls under a regime that requires documented processing terms. Neither is a flaw in Lodd — it's just a mismatch between what the page discloses and what your situation requires.

The one thing you can act on today without waiting for answers: connect an agent to a low-traffic property, stay inside the free allowance, and test whether the query model actually replaces the dashboard you're trying to escape.

Lodd is a web analytics MCP server built for AI agents. Instead of sending you to a dashboard, it exposes 42 MCP tools covering traffic, events, funnels, conversion attribution, and engagement metrics, so an agent like Claude Code, Claude Desktop, Cursor, or Codex can query your analytics inside the same conversation where you're already working. It's free up to 2,500 events per month, and setup is described as one prompt.

The core idea: analytics where the agent already is

Traditional analytics tools assume a human opens a browser, navigates a dashboard, and reads charts. Lodd assumes the opposite: your agent asks the questions and pulls the numbers directly.

The site frames this as replacing Google Analytics with "analytics that live where you work." The agent can check traffic, spot issues, and tell you what's working — and when you need something visual, it builds charts and tables on the fly rather than making you find a pre-built report.

What the 42 tools actually cover

The tool set spans five areas named on the site:

Because all of these live in one MCP server, a single question can combine them. The site's own example is "Why did signups drop?" — a question that needs traffic data, funnel analysis, and source attribution at once. A dashboard can't answer it in one pass; an agent with all 42 tools in the same conversation can.

What that looks like in practice

The site gives three concrete scenarios:

• Claude Code: the agent spotted a 34% traffic drop, traced it to a CSS regression hiding the pricing CTA, and shipped the fix — all without leaving the editor.
• Claude Desktop: asked for a conversion funnel, the agent built one from live data, found an 86% drop-off between pricing and signup, identified a 12-field form as the cause, and explained what to change.
• Claude Cowork: a scheduled weekly traffic check compared this week to last, flagged anything unusual, and delivered a summary before the workday started — catching a 45% drop in Reddit traffic before anyone noticed.

The same tools and data work across Cursor, Codex, and any other MCP client.

Who this is for

Lodd fits if you already work inside an MCP-capable agent and want analytics answers without switching context. The example dialogue on the site is telling: the agent didn't just report a 34% signup drop — it traced it to a Reddit post aging off the front page (67% of signup traffic), noticed the funnel broke from 28% to 11% between /pricing and /signup, and recommended two fixes.

That's the value proposition: not prettier charts, but an agent that can reason across traffic, funnels, and sources in one conversation.

It's less useful if you want a standalone dashboard for a team that doesn't use AI agents, or if you need analytics your whole org can browse without an MCP client.

Setup and pricing

• Setup: described as "one prompt to set up" — you copy an install prompt into your MCP client.
• Querying: "any prompt to query" — you ask in natural language.
• Free tier: up to 2,500 events per month.
• Privacy: the site lists "Private by design" as a heading, though the excerpt doesn't detail the specifics.

The site mentions pricing and upgrade as concepts but the provided material doesn't include actual paid tiers or prices, so check the site directly for current plans.

The short version

Lodd turns web analytics into something your AI agent can act on directly: 42 MCP tools for traffic, events, funnels, attribution, and engagement, queryable in natural language from Claude Desktop, Claude Code, Cursor, Codex, or any MCP client. Free up to 2,500 events/month. If your workflow already lives in an agent, it removes the dashboard step entirely.