Analytics MCP Servers: What to Look For Before You Connect One

Model Context Protocol, or MCP, is a standard way for an AI assistant to call tools in another system. An analytics MCP server exposes your metrics as tools, so you can ask an assistant "what changed in signups last week?" and have it fetch the data rather than guess.

The category is new and uneven. Some servers are thin wrappers around a reporting API. Others are built for agents from the ground up. This post gives you criteria to judge them, and describes how OnyxMetric's server handles each, so you can compare with our approach without taking our word for it.

First, what an MCP server is not

It isn't a dashboard replacement and it isn't magic. An assistant calling analytics tools does three things: picks a tool, fills in parameters, and summarizes what came back. The quality of the answer depends on three things you can inspect:

1. How well the tools match the questions people ask.
2. How safe the connection is.
3. How precise the returned data is.

Every criterion below maps to one of those.

1. Where it runs and how it authenticates

Some servers run on your machine as a local process with credentials in an environment variable. Others are hosted remotely and you connect with a URL.

Local servers are easy to start and awkward to share. Each person installs it, stores a credential, and updates it. Hosted servers are easier for teams but raise the question of how access is granted.

Look for:

• OAuth support. Your client opens a browser, you log in, you approve. No long-lived key sits in a config file. This matters most on shared repositories where a committed config should hold a URL and no secret.
• API keys as an alternative. Useful for headless runs and CI, where no browser exists.
• Acting as the signed-in user. The agent should have the permissions of the person who connected, no more.

OnyxMetric's hosted endpoint is https://app.onyxmetric.com/api/mcp. It supports OAuth for clients that implement the MCP authorization flow, and personal API keys for the rest.

2. Scopes and least privilege

Analytics tools mix harmless reads with consequential writes. Viewing top pages is one thing. Deleting a site is another. A server should let you separate them.

Ask:

• Can I create a credential limited to read-only?
• Can I limit it to certain resources, such as a single site?
• Are destructive tools clearly labeled so clients can ask for confirmation?

OnyxMetric supports scoped API keys, and OAuth tokens carry scope strings in a resource and action form. Destructive tools are annotated as such.

3. Tool design: few and meaningful beats many and vague

An assistant chooses among tools by reading their names and descriptions. Fifty overlapping tools confuse it. Good servers organize around questions:

• Overview and time series: how is traffic trending?
• Breakdown by a dimension: which pages, sources, countries?
• Live stats: who is on the site now?
• Behavior: funnels, retention, journeys, goals
• Quality: errors and Web Vitals
• Raw access: sessions, events, users

OnyxMetric's server covers all of those, with tools such as get_overview, get_breakdown, get_live_stats, get_retention, analyze_funnel, get_errors and get_web_vitals, plus session and event access.

Also check the escape hatch. No fixed tool set covers every question. A read-only query tool, with a documented schema and limits, lets an assistant answer the odd ones. OnyxMetric offers get_query_schema and run_query, which run read-only SQL against your event data with row and time caps.

4. Whether it can write

Reading is the safe default. Writing is where agents become useful and also where mistakes cost something. Creating a goal or saving a funnel after you ship a feature is valuable. Deleting data is a different class of action.

Prefer servers where writes are explicit, authorized separately from reads, and reversible where possible. Watch for any tool that mutates state without clear naming.

5. Output quality

Run the same question through a few servers and examine what comes back.

• Structured output. Tools that return typed fields are easier for a model to use correctly than a blob of text.
• Pagination and limits. Without them, a broad query dumps thousands of rows into the context and the answer degrades.
• Time handling. Does it let you specify ranges, comparison periods and time zones? Ambiguity here produces confidently wrong answers.
• Consistency with the dashboard. The assistant's numbers should match what you see in the app. If they don't, you will stop trusting both.

6. Cost and context footprint

Every tool definition is loaded into the assistant's context. A server with dozens of long descriptions costs tokens on every request. Look at how many tools it registers and how wordy they are. Smaller surfaces leave room for your actual work.

7. What it does to your privacy posture

An AI assistant calling your analytics means some data reaches a model provider. Decide in advance what's acceptable. Aggregates like pageviews by country carry little risk. Individual user records and session details carry more. Use scoped keys to keep the agent away from data it doesn't need, and read your assistant provider's data-handling terms.

A test script for any server

Pick a trial site and ask these in order. They escalate from easy to hard:

1. "List my sites." Tests connection and permissions.
2. "How did traffic change last week compared with the week before?" Tests time handling.
3. "Which pages bring the most visitors from search?" Tests breakdowns and filters.
4. "Where does the signup funnel lose the most people?" Tests multi-step tools.
5. "Are there new JavaScript errors since Tuesday's release?" Tests errors and date reasoning.
6. "Create a goal for the pricing page." Tests writes and whether the client asks for confirmation.
7. A question your tools can't answer directly. Tests the fallback.

Compare each answer against the dashboard. Note where the assistant had to guess.

Our bias

We build one of these, so weigh this accordingly. What we think matters most, in order: safe authentication, tools that match real questions, honest limits, and numbers that agree with the app. Everything else, including how many tools are on the list, is secondary.

If you want to try ours, the setup pages in the docs cover Claude Code, Claude Desktop, Codex, Cursor, VS Code and opencode. Connect one test site, run the script above, and judge it by what it gets right.