Every agency gets this request sooner or later. A client sends forty competitor domains, or a link-building lead sends a list of prospects, and wants traffic, keyword and backlink numbers for all of them by the end of the week. Checking each domain in the UI is slow. Writing a script is faster, but only if someone on the team is comfortable maintaining Python.
SE Ranking recently published a step-by-step guide to bulk domain analysis with its API. It describes two routes. One is a scripted pipeline in Python and Google Colab. The other is SE Ranking's MCP server, which lets an AI assistant call the same API from a chat window. We spend a lot of our time connecting Claude to marketing data through MCP, so this post covers how the no-code route works, where it falls short, and when we would still use the script.
What data you are actually pulling
Both routes use the same two SE Ranking APIs, so the data is the same.
The Domain Analysis API returns domain-level statistics from organic and paid search for a specified location. The data includes total domain traffic, the number of keywords a domain ranks for, newly acquired and dropped keywords, keywords that improved, declined or held position, and how many keywords sit in the top position bands.
The Backlinks API covers the link profile. It returns SE Ranking's Domain Trust score, total backlinks, referring domains and the dofollow/nofollow split.
Together these support most of the bulk jobs agencies run: sizing up a competitive niche, screening guest post or link prospects, and checking a prospect's backlink profile before a pitch. SE Ranking's guide says the two APIs together offer nearly all the numeric metrics available in the platform versions of these tools. Its documentation lists the full set of parameters.
One detail matters for every prompt or script you write. Domain Analysis metrics are tied to a location. If you leave the market unspecified, you may compare numbers from the wrong database without noticing.
Setting up the MCP route
SE Ranking describes its MCP server as hosted and remotely managed. There is nothing to install locally, and you do not need Docker or a terminal. Setup takes a few minutes:
- Claude: add a custom connector in Claude's connector settings, or in Claude Desktop under Customize > Connectors. Name it SE Ranking, enter the server URL given in SE Ranking's documentation, and authorize through OAuth.
- ChatGPT: open Apps, search for SE Ranking and select Connect.
After that, the assistant can call SE Ranking's Data API on your behalf. SE Ranking's guide says these are the same endpoints its Python scripts use: the Domain Analysis domain overview endpoints and the Backlinks API backlink summary endpoint. Only the way you invoke them changes.
What a working prompt looks like
SE Ranking's own example prompt is: "Pull a domain overview and backlink summary for these 50 domains and put it in a table." That works, but in our experience you get cleaner, more repeatable output when the prompt is specific. We would add:
- The location. Name the market, for example the UK, so the traffic and keyword numbers match the client's market.
- The columns. List exactly which metrics you want and in what order, such as traffic, ranking keywords, Domain Trust and referring domains. This stops the assistant from choosing its own selection on each run.
- Error handling. Ask it to flag any domain that returned no data or an error instead of quietly dropping the row.
- Batching. SE Ranking's guide says the backlink summary endpoint supports batching up to 100 targets in a single request. For longer lists, splitting them into chunks makes it easier to see which part failed.
It also helps to ask the assistant to state which tool calls it made. That gives you a light audit trail inside the conversation.
Watch the credit meter
This is the part most no-code walkthroughs skip. SE Ranking's guide describes Data API access, and MCP access with it, as credit-based. It is included with paid plans, and a 14-day trial adds 100,000 free API credits. The guide is explicit that MCP draws on the same credit balance as the script path, not a separate one.
The guide gives one reference figure: backlink summaries alone run roughly 100 credits per domain, so in SE Ranking's words a 50-domain pull is roughly 5,000 credits for that piece alone. It also says the cost per record for domain overviews isn't confirmed, and it recommends checking the Unit costs table in your API Dashboard before running this at scale.
The guide raises one more caution. It states that the number of underlying API calls a single prompt turns into isn't documented, so you should not assume one prompt equals one call. For an agency, that means a casual follow-up asking the assistant to add backlink data for a second list spends real credits. We would run a small test batch first and compare the credit balance before and after.
The model side has its own cost as well. Bulk work involves a lot of tool calls and a lot of tokens. We wrote about how cheaper models change the math for high-volume marketing agents, and the same reasoning applies here. If you run this kind of pull often, the choice of model is part of the budget.
The Python and Colab route
SE Ranking's guide provides two Colab scripts. Both read a list of domains from a Google Sheet, call the API and write the results back to the Sheet.
- Domain Analysis + Backlinks API: returns more data because it covers both modules. The guide notes that it is slower and spends Backlinks API credits.
- Domain Analysis API only: the guide describes it as faster, but it does not return Domain Trust, backlinks or referring domains.
You paste the script into Colab, add your API key from the API section of SE Ranking's settings, and run it. The output lands in a Google Sheet, where you use formulas to extract the metrics you need.
The main difference is the output. As SE Ranking puts it, MCP output is conversational. You can ask for a table, but that is not the same as a persistent, formula-editable Sheet. The script produces an artifact that you can rerun, version, share with a client and build a dashboard on. SE Ranking itself recommends the Python and Colab path for recurring, repeatable reporting pipelines.
The cost of the script route is maintenance. Someone has to own the code, keep the API key secure, and fix it when an endpoint or library changes.
When each makes sense for an agency
Here is how we would divide the work.
Use the MCP server when:
- The question is one-off or exploratory, such as a pre-pitch competitor scan or a first look at a new niche.
- The person asking is an account manager or strategist who does not write Python.
- You expect to ask follow-ups, such as filtering the list, re-sorting it or comparing two subsets, before you know what the answer looks like.
- Speed matters more than a reusable artifact.
Use the Python/Colab route when:
- The same pull runs every month for the same client.
- The output feeds a report, a Looker Studio dashboard or another system.
- You need a reproducible record of exactly what was requested and returned.
- You want explicit control over every call and the credits it spends.
SE Ranking also describes its MCP connector as an active area of development and suggests treating its current capabilities as the present state rather than a finished feature set. That is another reason not to build a client's recurring reporting on chat output yet.
The hybrid we would recommend
In practice, you don't have to choose one route. The workflow we would suggest to most agencies has three steps:
- Explore in chat. Use the MCP server to work out which metrics, filters and thresholds actually answer the client's question.
- Codify what repeats. Once a pull has run the same way two or three times, move it into a script with a fixed domain list, fixed columns and a fixed location.
- Keep the judgment human. Bulk metrics tell you who has traffic and links. They do not tell you why a competitor wins. Domain Trust or keyword counts are often table stakes rather than the reason a buyer chooses one vendor over another. We covered that distinction in qualifiers vs. determiners, and it applies to competitive SEO research as much as to positioning.
The MCP route removes the coding barrier, but it does not make the work free and it does not remove the need for checking. Write specific prompts, watch the credits, and verify any figure before it reaches a client.
Want help wiring this into your stack?
We build MCP connections between Claude and the data agencies and in-house teams rely on, from ad platforms to BigQuery and GTM, and we know where they need guardrails. If you are deciding between chat-based analysis and a scripted pipeline, or want both working together, talk to Bitegrico about your MCP and analytics implementation.