A U.S.-scoped snapshot from Ileventech counted 42,270 monthly searches across 73 MCP-related phrases on September 21, 2026. That is the sum for the phrases the publisher screened—not a count of every MCP search, and not a worldwide total. Most of the volume sat in a handful of broad terms, while the publisher found 43 distinct search-result clusters among 48 phrases that cleared its volume threshold.
What the 42,270 figure measures
Ileventech’s September 21, 2026 snapshot covered 73 phrases related to MCP. The publisher says it used paid keyword APIs for monthly volume and difficulty, with the location set to the United States (location code 2840). Forty-eight phrases exceeded 50 estimated monthly searches. The total of 42,270 is the sum across all 73 screened phrases, not a census of all queries people might use to find MCP information.
The phrase set included protocol names and definitions, client/server terminology, transports, comparisons such as “mcp vs api,” and adjacent infrastructure topics such as agent memory and agentic RAG. Ileventech does not name the keyword-data provider, publish the complete phrase list, or explain how its difficulty scores are calculated. Treat the figures as one publisher’s dated, U.S.-scoped keyword snapshot rather than a universal or independently verified demand count.
Which MCP-related phrases account for the volume?
The leading terms were broad names and concepts. Ileventech reports these monthly volumes and shares of the 42,270-search pool:
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| Phrase | Reported 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% |
The top three phrases together make up 47.6% of the reported pool. The other 67 screened phrases account for 15,970 searches per month, or 37.8%. The distribution therefore is not a single large “MCP” audience expressed in interchangeable wording: a large portion is concentrated in a few broad labels, with the rest spread across many related terms.
What the result-page clusters suggest
Search volume alone does not show whether one article can satisfy several related queries. To assess that, Ileventech fetched the top ten results for each of the 48 phrases above its 50-search threshold and grouped phrases when their result sets substantially overlapped. It reports 43 clusters, with four merges and 11 adjacent pairs.
In that snapshot, most qualifying phrases had their own result-page grouping. That supports planning content around distinct query families rather than assuming a single broad explainer will match every search. For example, a definition query, a comparison such as “mcp vs api,” and a practical query such as “mcp server list” may call for different answers when their search results differ. Clustering is evidence about those observed result pages, not a guarantee that separate pages will rank or that rankings will stay fixed.
Search volume and difficulty are different signals
Ileventech describes difficulty as a 0–100 estimate of how hard it is to enter the first page. Its examples show that the largest terms were not necessarily the least difficult, but the underlying provider and formula are undisclosed:
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| Phrase | Reported monthly searches | Reported 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 |
These scores are the publisher’s reported estimates, not a standardized measure comparable across providers. Without the provider’s identity and scoring method, a score such as 12 cannot be interpreted as a probability of ranking or directly compared with a score from another tool. The figures are most useful as relative clues within this particular snapshot.
What the publisher’s own visibility data shows
Ileventech also reports 54 Search Console impressions and seven clicks during the four weeks ending September 22, 2026; all were from branded queries, with no impressions from the measured non-branded phrases. That is a first-party report about the publisher’s site and that period, not a measure of the wider search market or a result for every site publishing MCP material.
For one week, the publisher’s CDN logs recorded 282 page reads carrying AI-crawler user agents and 144 carrying search-crawler user agents. These are user-agent counts, not verified crawler identities: a user agent can be spoofed, and requests for machine files such as robots.txt can inflate counts if they are not filtered. They should not be read as confirmed visits by particular bots.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to use the snapshot without overreading it
- Use it to understand phrasing: the reported demand spans definitions, tools, comparisons, specifications, servers, transports, and neighboring agent topics.
- Separate volume from reachability: a large estimated search total does not show how competitive a query is or whether a particular page can win traffic.
- Plan around intent and observed result overlap: the 43 reported clusters suggest that related phrases may need focused answers, but the result pages can change.
- Compare future measurements on equal terms: keep the U.S. geography and date window consistent, use the same phrase list, establish whether volume is measured or modeled, identify the difficulty metric, and repeat the same top-ten overlap method.
A direct comparison is limited because Ileventech has not published all 73 phrases or identified its data provider. Any later total built from a different phrase list, geography, date, or volume source may be answering a different question.
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