{"slug": "mcp-is-more-useful-as-context-distribution-than-as-rpc", "title": "MCP Is More Useful as Context Distribution Than as RPC", "summary": "A developer argues that the Model Context Protocol (MCP) is more valuable for distributing context, rules, and operating contracts to AI clients than for remote procedure calls (RPC). By using MCP to define the working environment before work starts, teams can enforce shared governance and reduce reliance on individual prompt craftsmanship.", "body_md": "Most discussions around MCP focus on tool calling.\n\nThat is natural.\n\nWhen people first see MCP, the obvious use case is simple:\n\nLet the AI call external tools.\n\nA model can read a GitHub issue.\n\nA model can query a database.\n\nA model can update a file.\n\nA model can call an API.\n\nIn that sense, MCP looks like an RPC layer for AI agents.\n\nThat is useful.\n\nBut I think it may not be the most important use of MCP.\n\nThe more interesting use is this:\n\nMCP can distribute context, rules, skills, and operating contracts to AI clients.\n\nIn other words, MCP is not only a way for AI to call tools during work.\n\nIt can also be a way to define the working environment before the work starts.\n\nRAG is usually used to answer this question:\n\nWhat information might be relevant to this request?\n\nThe system searches documents, retrieves chunks, and gives them to the model.\n\nThis works well for many cases.\n\nBut it has structural limits.\n\nRAG retrieves likely relevant information.\n\nIt does not necessarily define how the work should be done.\n\nFor team-level AI work, this is a problem.\n\nA team does not only need information.\n\nA team also needs shared rules.\n\nFor example:\n\nRAG can retrieve documents that describe these rules.\n\nBut retrieval is not the same as governance.\n\nA retrieved chunk is just context.\n\nIt is not necessarily an operating contract.\n\nMany teams try to solve this with prompts.\n\nThey write instructions like:\n\nFollow our coding rules.\n\nUse this design document.\n\nAsk questions when unclear.\n\nDo not make risky changes.\n\nThis helps, but it does not scale well.\n\nEach developer may have a different local prompt.\n\nEach AI client may load a different file.\n\nEach repository may contain a slightly different version of the rules.\n\nSome people may forget to update their instructions.\n\nSome people may not load the correct context at all.\n\nAs a result, the quality of AI output depends too much on the individual user.\n\nOne developer gets good output because they know how to explain the domain.\n\nAnother developer gets poor output because they do not know which context matters.\n\nThat is not a team-level system.\n\nThat is individual prompt craftsmanship.\n\nWhat if MCP is used not only for tool calls, but also for startup context?\n\nAt session start, the AI client calls a startup function.\n\nThat function returns:\n\nNow the model does not merely have access to tools.\n\nIt starts inside a governed context.\n\nThis changes the role of MCP.\n\nRPC-style MCP:\n\nThe model calls tools during work.\n\nContext-distribution MCP:\n\nThe model receives the rules of work before work starts.\n\nThat difference is important.\n\nIf an MCP server exists, that does not mean the model will use it.\n\nThe model has to know that MCP is not optional background infrastructure.\n\nIt has to know that MCP is the authoritative source for the session.\n\nThis requires a client-side bootstrapping rule.\n\nFor example:\n\nAt session start, if the project MCP server is configured, call\n\n`get_startup_context`\n\nfirst and treat the returned access policy as authoritative.\n\nThis is a small rule, but it changes the behavior of the system.\n\nWithout it, the AI may try to answer from memory, local files, or partial context.\n\nWith it, the AI first asks the MCP server how the work should be governed.\n\nThat is the difference between “MCP is available” and “MCP controls the working context.”\n\nThis also changes how domain knowledge can be distributed.\n\nIn many organizations, domain knowledge is held by experienced people.\n\nThey know:\n\nIf this knowledge is only written as documentation, every user must read and understand it.\n\nIf this knowledge is embedded into prompts, every user must keep their prompt updated.\n\nBut if this knowledge is packaged as MCP-accessible skills, the distribution model changes.\n\nThe skill author maintains the domain skill centrally.\n\nThe users consume it through MCP.\n\nThe AI client receives the same skill definition at runtime.\n\nThis separates two roles:\n\nThat separation matters.\n\nIt means a senior engineer can define a domain-specific skill once.\n\nThen multiple developers can use that same skill through their AI clients.\n\nThe goal is not only better answers.\n\nThe goal is more consistent output across the team.\n\nAnother advantage is portability.\n\nIf every user needs a local checkout of the governance repository, the system becomes fragile.\n\nLocal copies become stale.\n\nDifferent users may have different versions.\n\nSetup becomes heavier.\n\nOnboarding becomes slower.\n\nUpdates are harder to propagate.\n\nWith MCP-based context distribution, the local client does not need the full governance repository.\n\nThe local client only needs a bootstrapping instruction and MCP access.\n\nThe authoritative definitions stay on the MCP server.\n\nThe AI resolves documents, skills, workflows, and contracts through named MCP tools.\n\nThis makes the governance layer portable.\n\nThe user does not carry the entire context system locally.\n\nThe user connects to it.\n\nThe difference can be summarized like this.\n\nRAG answers:\n\nWhat information might be relevant?\n\nMCP context distribution answers:\n\nWhat context and rules must govern this work?\n\nRAG retrieves knowledge fragments.\n\nMCP can expose authoritative context.\n\nRAG is useful for answering questions.\n\nMCP can be useful for controlling work.\n\nRAG is often probabilistic retrieval.\n\nMCP can provide named resolvers, catalogs, policies, and contracts.\n\nRAG can tell the model something.\n\nMCP can tell the model how it is allowed to proceed.\n\nThis is why I think MCP as context distribution may be more important than MCP as RPC.\n\nOne example is unknown handling.\n\nIn normal AI usage, uncertainty often disappears into fluent text.\n\nThe model may say something plausible.\n\nThe user may not notice that an important assumption was unresolved.\n\nFor serious work, unknowns should not be hidden.\n\nAn unknown should be a first-class runtime signal.\n\nFor example:\n\nIf unknown handling is only a prompt instruction, it is weak.\n\nBut if unknown handling is part of the MCP-distributed operating contract, every skill and workflow can share the same rule.\n\nThe model can be required to record unknowns.\n\nClosure can be blocked when unresolved unknowns remain.\n\nRisky changes can require escalation.\n\nThis is not ordinary retrieval.\n\nThis is governance.\n\nI now see MCP as something broader than a tool-calling protocol.\n\nIt can become an operating layer for AI-assisted work.\n\nNot an operating system in the traditional sense, but a layer that defines:\n\nThat is much more than RPC.\n\nRPC lets the model do things.\n\nContext distribution tells the model how to work.\n\nFor individual experiments, this may not matter much.\n\nFor teams, it matters a lot.\n\nBecause teams do not only need powerful AI.\n\nThey need repeatable AI-assisted work.\n\nThe key idea is simple:\n\nDo not only give AI tools.\n\nGive AI a governed working context.\n\nMCP makes this possible because it can expose not only actions, but also resources, prompts, catalogs, resolvers, and contracts.\n\nThis suggests a different direction for MCP-based systems.\n\nInstead of asking:\n\nWhat tools can the AI call?\n\nWe should also ask:\n\nWhat context must the AI load before it starts?\n\nWhat rules must govern the work?\n\nWhat skills should be distributed to every user?\n\nWhat should prevent unsafe or incomplete closure?\n\nThis may be where MCP becomes most valuable.\n\nNot as an RPC layer for agents.\n\nBut as a portable context distribution layer for team-level AI work.", "url": "https://wpnews.pro/news/mcp-is-more-useful-as-context-distribution-than-as-rpc", "canonical_source": "https://dev.to/synthaicode_commander/mcp-is-more-useful-as-context-distribution-than-as-rpc-ai4", "published_at": "2026-06-26 03:21:03+00:00", "updated_at": "2026-06-26 03:33:32.635945+00:00", "lang": "en", "topics": ["large-language-models", "ai-agents", "developer-tools", "ai-infrastructure"], "entities": ["MCP", "RAG", "AI", "Model Context Protocol"], "alternates": {"html": "https://wpnews.pro/news/mcp-is-more-useful-as-context-distribution-than-as-rpc", "markdown": "https://wpnews.pro/news/mcp-is-more-useful-as-context-distribution-than-as-rpc.md", "text": "https://wpnews.pro/news/mcp-is-more-useful-as-context-distribution-than-as-rpc.txt", "jsonld": "https://wpnews.pro/news/mcp-is-more-useful-as-context-distribution-than-as-rpc.jsonld"}}