We build an MCP gateway. That means we have to answer "where is the demand for MCP tooling" with numbers instead of instinct — and when we finally measured it, the shape of the answer was not what the topic's noise suggests. This is that measurement, with the method attached so anyone can repeat it and disagree with our reading.
The MCP ecosystem produces a lot of content and very little demand data. Every week brings another "awesome MCP servers" list, another protocol explainer, another launch post. What none of them carry is the number that decides what to write next: how many people search for a given phrase in a month, and how hard the results page for it is to enter.
We had a second reason to care. Our own pages were indexed, our sitemap was clean, and our Search Console still showed almost no non-branded impressions. When that happens, the useful question is not "is our prose good" but "does anyone search for this at all, and with what words". A demand measurement answers both at once.
The pool comes from paid keyword data, not estimate tables scraped from a free tool: 73 phrases were screened: seed terms around the protocol itself, the client/server vocabulary, the transports, the comparison phrasings ("mcp vs api"), and the adjacent infrastructure terms our product lives among (agent memory, agentic RAG, streamable HTTP). Volumes and difficulty came from measured requests against the keyword APIs we already pay for, with the location pinned to the United States (location code 2840) so the numbers do not mix markets. 48 of the 73 cleared 50 searches/month. Those 48 were then clustered by search-result overlap: fetch the top 10 results for each phrase, and group phrases whose result sets substantially coincide. That is the only honest way to decide whether two phrases belong on one page or two. Snapshot date: 21 September 2026. Every number below is from that single day, which matters — see the limits section at the end.
The 73 phrases sum to 42,270 searches per month. One phrasing carries more than a quarter of it:
| Phrase | Monthly searches | Share of pool | |---|---|---| | model context protocol | 12,100 | 28.6% | | mcp protocol | 4,400 | 10.4% | | a2a protocol | 3,600 | 8.5% | | mcp tools | 2,400 | 5.7% | | agentic rag | 1,900 | 4.5% | | mcp vs api | 1,900 | 4.5% | | everything else (67 phrases) | 15,970 | 37.8% |
Two readings follow, and they point in opposite directions. The optimistic one: there is a real audience — 42,000 searches a month is not a hobbyist niche. The pessimistic one: the top three phrases are 47.6% of it, and those three are the name of the thing. People searching "model context protocol" and "mcp protocol" are not looking for a vendor; they are looking for the definition, which is exactly the query an encyclopaedia and a documentation site answer for free.
Difficulty here is a 0–100 estimate of how hard the first page is to enter. Sort the pool by volume and a pattern appears — the big phrases are defended, and the usable ones sit in the middle:
| Phrase | Monthly searches | Difficulty | |---|---|---| | anthropic mcp | 1,600 | 69 | | mcp protocol | 4,400 | 64 | | model context protocol | 12,100 | 58 | | mcp tools | 2,400 | 56 | | mcp spec | 1,300 | 32 | | mcp server list | 880 | 22 | | mcp vs api | 1,900 | 12 | | streamable http | 880 | 12 |
The two phrases with the lowest difficulty in the whole table are also two of the most specific: "mcp vs api" compares two architectures, and "streamable http" names one transport that MCP standardised. Both are the kind of query a reader has after learning what MCP is, and both are answerable by anyone who has actually shipped the thing rather than read the specification.
That asymmetry is the practical finding: the demand that a new entrant can realistically win is not the demand that looks biggest. Chasing the 12,100 phrase means competing with the protocol's own documentation; answering the 12-difficulty phrase means writing something only an implementer can write.
This was the surprise. We expected the 48 phrases to collapse into a handful of topical clusters, the way "cheap flights" and "low cost flights" belong together. Instead, clustering the live result pages turned 48 phrases into 43 clusters — with only 4 merges and 11 adjacent pairs across the whole set.
Translation: for MCP, almost every phrase owns its own result page. "mcp spec" and "mcp specification" are not the same query if the results pages disagree; "mcp tools" and "mcp servers" sit next to each other without merging. The topic looks like one huge subject from the outside and behaves like a mosaic of small ones from the inside.
This has a direct consequence for anyone planning content here, and it is the opposite of the usual advice to build one comprehensive pillar page. A single page cannot be the canonical answer to 43 different result pages. What works is a small centre per phrase family — the protocol overview, the transport comparison, the client-configuration walkthrough — each one narrow enough to actually match its result page, and each one linking to its neighbours so that a reader who lands on any of them can reach the rest. Our own overview of the protocol's actors, lifecycle and transports is written to that shape, rather than to a keyword count.
The last piece of the measurement is not about search demand at all; it is about what our own site saw while we were publishing into this pool. In the four weeks ending 22 September 2026, our Search Console recorded 54 impressions and 7 clicks — and every one of them was a branded query. Not one of the non-branded phrases in the table above produced an impression during that window.
That is worth stating plainly, because it is the number that disciplines the rest: a pool of 42,270 monthly searches, fifty-odd published articles, and a non-branded impression count of zero. Demand being real does not mean demand is reachable; the pages existed before the audience did, and Google has to decide to serve them before any of the arithmetic in this article matters.
The same period's edge logs add a useful counterweight: of the page reads recorded at our CDN in one week, 282 carried AI-crawler user agents and 144 carried search-crawler user agents. Treat that as a signal rather than a measurement — a user agent is a claim, not an identity, and machine files like robots.txt inflate both numbers unless they are filtered out. The direction is still informative: agents are reading this material earlier and more often than search engines are.
Three things, all of them reversible if the next snapshot disagrees: We stopped measuring success in pages and started measuring it in phrase families covered, which is the unit the clustering says actually exists. We publish a cluster as a batch — one centre page on the head phrasing, members on the long tails — instead of one page at a time. A member page that links to siblings which do not exist yet is a broken link, and a centre page with no members is a page nobody reaches from anywhere. We kept the measurement in the repository next to the content, so the next round can diff against it instead of re-deriving it from memory.