Your concerns about MCP's potential decline are well-founded and reflect real challenges the protocol faces. Let me analyze each point based on current industry trends and adoption patterns.
This concern has significant merit. The relationship between RAG and MCP is more complex than simple replacement, and enterprise preferences are already showing interesting patterns.
Many organizations are indeed prioritizing RAG for internal knowledge management[1]. RAG excels at handling unstructured, text-heavy content like documentation, policies, and institutional knowledge[2]. For enterprises with extensive document repositories, RAG provides:
- Comprehensive context from historical documents and knowledge bases[1]
- Sub-second response times for complex queries spanning multiple information sources[1]
- Advanced document intelligence that extracts structured insights from unstructured content[1]
However, the most effective enterprise implementations are adopting hybrid architectures that combine both approaches[1]. RAG handles strategic, document-heavy queries while MCP provides real-time access to operational data[1]. This suggests that rather than MCP being replaced, it may be relegated to specific use cases rather than becoming the universal solution many predicted.
This concern is particularly valid and represents a significant threat to MCP's value proposition.
Today's API specifications often fall short for AI integration[3]. Developers report that existing OpenAPI specs are "insufficient as a basis for tool calling" due to:
- Largely insufficient documentation on endpoints themselves[3]
- Missing context on what actions are triggered by different HTTP methods[3]
- Underdocumented authentication mechanisms[3]
- Broken or incorrect base URLs[3]
If LLMs become proficient at interpreting and working with API specifications directly, this could eliminate MCP's primary advantage. The protocol's main selling point is providing "a badge that says 'we had AI usage in mind when building this'"[3]. If LLMs can effectively work with standard APIs without this specialized layer, MCP's value diminishes significantly.
This is perhaps the most immediate and practical concern. Developer sentiment around MCP complexity is already emerging as a significant barrier.
Multiple sources highlight the substantial development overhead:
- MCP implementations can require "1000s of lines of code" for proper tooling[4]
- Developers face "double the effort" when managing multiple plugin systems[4]
- The protocol creates "hard dependencies between AI apps and tools"[5]
The complexity extends beyond initial development:
- No built-in security mechanisms in current MCP specifications[4]
- Hidden cost implications as every registered function adds token load[4]
- Debugging and tooling immaturity compared to established HTTP APIs[6]
Industry feedback suggests growing frustration: "many MCP servers already provide far more than twenty functions right from the start"[4], leading to exponentially increasing costs and complexity that users often don't understand.
Despite these concerns, MCP has shown remarkable early adoption:
- Over 1,000 MCP servers created by February 2025[7]
- Major platform adoption including Microsoft's Windows 11 integration[7]
- Enterprise backing from Anthropic, OpenAI, and Microsoft[7]
However, this early momentum doesn't guarantee long-term success if the fundamental concerns you've identified materialize.
Your concerns have substantial merit, but the outcome may be more nuanced than complete MCP abandonment:
- RAG prioritization is already happening for knowledge-heavy use cases, but hybrid approaches are emerging
- LLM training on API specs could indeed reduce MCP's value proposition significantly
- Developer complexity aversion is a real and growing concern that could limit adoption
If two or more of these factors align, MCP usage could indeed plummet in specific segments. However, MCP may survive in specialized niches where its benefits outweigh the complexity costs, particularly for real-time operational data access where RAG is less suitable[1].
The protocol's future likely depends on addressing the complexity concerns while finding sustainable differentiation from both RAG and direct API integration approaches.
[1] https://www.webuild-ai.com/insights/will-mcp-make-rag-obsolete [2] https://www.linkedin.com/pulse/rag-vs-mcp-guide-native-ai-apps-vishvambhar-dayal-tht7c [3] https://news.ycombinator.com/item?id=44314289 [4] https://www.linkedin.com/pulse/danger-mcp-what-every-developer-needs-know-gregor-biswanger-nm9se [5] https://dev.to/viktorardelean/mcp-the-rest-revolution-of-ai-why-this-protocol-changes-everything-4p75 [6] https://duske.me/posts/mcp/ [7] https://rickxie.cn/blog/MCP/ [8] https://dev.to/tak089/what-is-model-context-protocol-mcp-and-why-is-it-trending-in-2025-14lc [9] https://www.ainewshub.org/post/top-10-model-context-protocols-mcp-transforming-ai-in-2025 [10] https://www.sarmedrizvi.com/blog/10-MCP-Servers-you-can-use-in-2025 [11] https://apidog.com/blog/top-10-mcp-servers/ [12] https://becomingahacker.org/integrating-agentic-rag-with-mcp-servers-technical-implementation-guide-1aba8fd4e442 [13] https://www.getambassador.io/blog/mcp-server-explained [14] https://axify.io/blog/code-complexity-explained [15] https://www.arsturn.com/blog/examining-the-future-of-mcp-servers-in-the-ai-landscape-trends-predictions [16] https://www.linkedin.com/pulse/rag-vs-mcp-beyond-hype-choosing-right-ai-power-up-you-justin-parnell-ksuqc [17] https://a16z.com/a-deep-dive-into-mcp-and-the-future-of-ai-tooling/ [18] https://destinovaailabs.com/blog/top-10-model-context-protocol-mcp-servers-for-2025-powering-ai-driven-development/ [19] https://block.github.io/goose/blog/2025/04/22/mcp-is-rewriting-the-rules-of-api-integration/ [20] https://den.dev/blog/mcp-prm-auth/ [21] https://www.linkedin.com/posts/yarivlevski_mcp-probably-the-most-important-trend-of-activity-7315423257455685632-Il1d [22] https://www.gettingstarted.ai/mcp-vs-rag-vs-api/ [23] https://nordicapis.com/how-model-context-protocol-mcp-impacts-apis/ [24] https://www.getzep.com/ai-agents/developer-guide-to-mcp [25] https://www.pomerium.com/blog/june-2025-mcp-content-round-up [26] https://dev.to/pranta/why-being-a-software-developer-sucks-in-2025-46ia [27] https://generect.com/blog/what-is-mcp/ [28] https://www.k2view.com/what-is-model-context-protocol/ [29] https://blog.logto.io/mcp-auth-spec-review-2025-03-26 [30] https://www.linkedin.com/pulse/llms-also-need-helpmcp-provides-standard-way-request-walid-negm-whpoc [31] https://dev.to/fallon_jimmy/top-10-mcp-servers-for-2025-yes-githubs-included-15jg [32] https://theintellify.com/mcp-vs-rag-ideal-ai-model/ [33] https://www.getambassador.io/blog/model-context-protocol-mcp-connecting-llms-to-apis [34] https://block.github.io/goose/blog/2025/04/22/mcp-is-rewriting-the-rules-of-api-integration [35] https://devcontentops.io/post/2025/06/mcp-vs-rag-for-ai-applications [36] https://www.dandoescode.com/blog/mcp-easily-extend-llms-with-existing-apis [37] https://news.ycombinator.com/item?id=43302297 [38] https://ragwalla.com/blog/mcp-enterprise-adoption-report-2025-challenges-best-practices-roi-analysis
follow up questions:
Why these worries about MCP and RAG might be overblown or unnecessary
How MCP's complexity could actually enhance AI reliability rather than hinder it
Why RAG's focus on retrieval doesn't make MCP redundant for complex tasks
How industry adoption of MCP indicates confidence, not fear, in its capabilities
In what ways combining MCP and RAG can create a more robust AI system