How do I turn a question we keep losing into a plan?

Ask your assistant for the full picture of one question you keep losing: who is named instead, and which sources decide the answer. Then ask the Attensira MCP server for a win plan, which is free and orders the next move per engine with a credits estimate. Hand the plan to the crew only once you agree with it.

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Claude Codeattensira · attensira.com

Asked: We never win "MCP server for AI search visibility". What would it take?

called win_plan prompt="MCP server for AI search visibility"

Named in 0 of 24 answers. Two moves, one per engine, about 12 credits. No change proven yet: this is work to do, not a result.

ChatGPT: write a page of our own: about 8 credits; none of our pages is among the sources that decide it (n = 12)
Perplexity: get into the roundup it reads: about 4 credits; a Search Engine Land guide to MCP servers (n = 12)
Named in answers today: 0 of 24; SE Ranking named in 15 of 24

Our own losing prompt, and the plan the crew would follow.

attensira.com workspace, read over the Attensira MCP · 4 Oct 2026

Who does the work

Analyst · Read the board
Researcher · Stored the answer

Which call answers it?

Paste it into any client connected to the Attensira MCP server, or ask the question in your own words and your assistant picks the same call.

The call

{ "tool": "get_prompt", "arguments": { "prompt": "MCP server for AI search visibility", "days": 90 } }

What can’t this tell you?

Nothing in this chain predicts that a change will work. There is no attribution path from a page edit to a later mention: you change something, wait for enough calendar days to rebuild an n, and re-measure. A plan executed today shows its earliest possible signal tomorrow and a trustworthy one much later.
The rates here are mention and citation rates with their sample size, not a rank against competitors and not a score. Competitor rates use the same denominator as yours, so they do not sum to anything and cannot be read as a share of a market.
A single collected answer is an anecdote. Each prompt is read at most once per model per country per workspace-local day, sampled once on Starter and three times on Growth and Business, and models vary their answers run to run at their own discretion.

What is the chain behind it?

When one call is not enough, these are the calls that follow, in order, and why each one is there.

Call 01
Call get_prompt with the exact wording or the prompt id
get_prompt accepts either a prompt id or the prompt's exact text, and returns the per-platform rates, the delta, the competitors seen in this prompt's answers and the sources those answers cited — all in one call. Use days=90 so the rates rest on more than a handful of readings. Inexact text will not match, so fall back to search if it misses.
Call 02
Call get_page on any URL of yours that was cited
get_page returns that URL's citation rate, its delta against the previous window, its per-engine channels and the prompts whose answers cited it. A page that already earns citations is a better template for the next one than a guess about what models like.
Call 03
Use search when you need the wording behind a rate
search runs full text across prompts, answers, pages and sources, and every snippet it returns is capped at 400 characters. That makes it a pointer to a record you then fetch in full, never a way to read a complete model answer.
Call 04
Call win_plan on the prompt
win_plan is free and already ordered. It returns what decides the answer today on each platform, one next move per platform with the sentence of evidence behind it, and a credits estimate. Its footer says no change is proven until a re-read comes back moved.
Call 05
Hand what you found to ask, then poll get_session
ask spends credits and needs write access (any signed-in connection, or a read-and-write key), and it earns them here — the agent reasons across the slices you just pulled, crawls and audits pages, and drafts a plan. A long question comes back as status "running" with no answer; wait poll_after_ms and poll get_session with the returned id.

Who wins the prompt we lose?

Before a plan, the board: SE Ranking is named or linked in 20 of 24 answers to “MCP server for AI search visibility”; we are in none. The hero's 15 of 24 counts answers that name the brand; this board also counts those that only link its domain. The plan's two moves aim at the sources behind these rows.

Named or linked inn=24 answers

“MCP server for AI search visibility”, ChatGPT and Perplexity, 3 of 31 days sampled · 4 Sep 2026 to 4 Oct 2026
Name	Named or linked in	Count
seranking.com	83.3%	20
llmpulse.ai	62.5%	15
aiclicks.io	50.0%	12
ahrefs.com	33.3%	8
searchable.com	25.0%	6
attensira.comyou	0.0%	0

Analyst · Read the boardattensira.com prompt board, read over the Attensira MCP · 4 Oct 2026 · n=24 answers

What did it show on our own workspace?

The plan split by engine, and the split is the lesson. On ChatGPT, nothing of ours is among the sources that decide the answer, so the move is a page of our own. On Perplexity, the answer is built from a roundup, so the move is getting into it. One prompt, two different kinds of work. The plan's footer said no change was proven yet, which is the only honest thing a plan can say before the work ships.

How do I read what comes back?

Competitors named in get_prompt, you absent, sources pointing at third-party lists: A placement problem rather than a page problem. The work is getting onto those lists, not rewriting your own page.
Your rate low but not zero: The model names you sometimes. Consistency is the gap, not existence, and that is a different fix from being unknown.
A cited source you do not own with a high n: The models have a go-to reference for this prompt and it is not you.
Mention rate 0 with n above 20: A measured absence rather than a missing measurement. This is the version worth working on.

What does the answer we want to be in say?

ChatGPT on 2 Oct, to the losing prompt: GEOly, SE Ranking and Rank Prompt, each with a line on what it covers, sourced mostly from GitHub. The plan's own page has to earn a line like theirs.

Answer · ChatGPTasked 2 Oct 2026

Question: MCP server for AI search visibility

ChatGPT answered: If you mean an MCP server that lets AI agents query and analyze AI-search visibility (GEO/AEO), there are several current options: GEOly MCP — brand mentions, citations, share of model, competitors, prompts, and AI-search demand… SE Ranking AI Visibility MCP — brand presence, citations, prompts, share of voice, and AI-search data across major engines. Rank Prompt MCP — API + MCP + webhooks for AI visibility monitoring and automation. …

Sources cited

1developers.cloudflare.com
2, 3, 4, 5, 6github.com
7trendos.com
8rankprompt.com
9seranking.com
10geobubbles.com

Not namedAttensira is missing from this ChatGPT answer.

Researcher · Stored the answerattensira.com stored ChatGPT answer, read over the Attensira MCP · 4 Oct 2026 · 10 links

Why read the shape of the sources before the number?

When the cited sources are roundups and listicles you are not in, no edit to your own page changes the answer, because your page was never what the model read. That is placement work, and a different project from a rewrite. Whichever of your URLs did get cited is worth a get_page call: a page that already earns citations shows what shape works for your domain more reliably than a theory about what models prefer.

Why do the credits come last?

The reads before it are free and immediate, and they are what make the crew's answer specific instead of generic. ask crawls and audits pages and drafts the work, so it spends credits and needs write access. Spend them once the plan reads right to you, not before.

The workspace-level version of this question is where you lost ground this week. If the deciding sources are domains you have never heard of, read which sources the models cite.

Questions about turning a lost prompt into a plan

Do I have to look up the prompt id first?

No. get_prompt accepts the prompt's exact text as well as its id, which saves a search round-trip when you already know the wording. It has to be exact — inexact text will not match, and search is the fallback.

Why does this recipe spend credits when the others do not?

Because it ends in ask, which runs an agent chat on an agent model. get_prompt, get_page, search and win_plan are reads and free. The tools that spend credits are the ones that start agent work, such as ask, run_automation and add_prompts; the MCP tool reference lists every one.

ask returned status "running" and no answer. Did it fail?

No, that is the documented behaviour for a long question. Wait the poll_after_ms it returns, then poll get_session with the session id until status is complete and the answer is populated.

Can I see which slices the agent actually read?

Yes. Call get_session with include set to ["events"] for the step-by-step trace of which tools it called and with what arguments. That is the check to run when an answer looks wrong.

My rate is null rather than 0. What is the difference?

A null rate means Attensira did not measure that thing in that window — the platform was not tracked, or no run succeeded. A 0 with n above 0 is a measured absence. Treating null as zero invents a failure that did not happen.

Where do these facts come from?

Attensira's MCP recipes state that sampling runs at most once per prompt per model per day, so a plan executed today shows its earliest possible signal tomorrow.
docs.attensira.com · retrieved 2026-10-04
“Sampling is at most once per prompt per model per day, so a plan executed today shows its earliest possible signal tomorrow and a trustworthy one much later.”
Attensira's MCP tool reference states that the read tools are free and immediate, while ask costs credits and takes time.
docs.attensira.com · retrieved 2026-10-04
“those are free and immediate, this costs credits and takes time”

Where to go next

Turn the question you keep losing into a plan you can act on.